IVGCVSW-1946: Remove armnn/src from the include paths

Change-Id: I663a0a0fccb43ee960ec070121a59df9db0bb04e

30 files changed, 0 insertions, 15129 deletions

diff --git a/src/backends/test/ActivationFixture.hpp b/src/backends/test/ActivationFixture.hpp
deleted file mode 100644
index 5028b252e1..0000000000
--- a/src/backends/test/ActivationFixture.hpp
+++ /dev/null
@@ -1,61 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#pragma once
-
-#include "TensorCopyUtils.hpp"
-#include "WorkloadTestUtils.hpp"
-
-#include <armnn/test/TensorHelpers.hpp>
-
-#include <boost/numeric/conversion/cast.hpp>
-#include <boost/multi_array.hpp>
-
-struct ActivationFixture
-{
-    ActivationFixture()
-    {
-        auto boostArrayExtents = boost::extents
-            [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(batchSize)]
-            [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(channels)]
-            [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(height)]
-            [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(width)];
-        output.resize(boostArrayExtents);
-        outputExpected.resize(boostArrayExtents);
-        input.resize(boostArrayExtents);
-
-        unsigned int inputShape[]  = { batchSize, channels, height, width };
-        unsigned int outputShape[] = { batchSize, channels, height, width };
-
-        inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
-        outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32);
-
-        input = MakeRandomTensor<float, 4>(inputTensorInfo, 21453);
-    }
-
-    unsigned int width     = 17;
-    unsigned int height    = 29;
-    unsigned int channels  = 2;
-    unsigned int batchSize = 5;
-
-    boost::multi_array<float, 4> output;
-    boost::multi_array<float, 4> outputExpected;
-    boost::multi_array<float, 4> input;
-
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    // Parameters used by some of the activation functions.
-    float a = 0.234f;
-    float b = -12.345f;
-};
-
-
-struct PositiveActivationFixture : public ActivationFixture
-{
-    PositiveActivationFixture()
-    {
-        input = MakeRandomTensor<float, 4>(inputTensorInfo, 2342423, 0.0f, 1.0f);
-    }
-};
\ No newline at end of file
diff --git a/src/backends/test/ActivationTestImpl.hpp b/src/backends/test/ActivationTestImpl.hpp
deleted file mode 100644
index 63716453cd..0000000000
--- a/src/backends/test/ActivationTestImpl.hpp
+++ /dev/null
@@ -1,559 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#pragma once
-
-#include <armnn/ArmNN.hpp>
-#include <armnn/Tensor.hpp>
-#include <armnn/TypesUtils.hpp>
-
-#include <test/TensorHelpers.hpp>
-#include "QuantizeHelper.hpp"
-
-#include <backends/CpuTensorHandle.hpp>
-#include <backends/WorkloadFactory.hpp>
-#include "ActivationFixture.hpp"
-
-#include <algorithm>
-
-template<typename T>
-LayerTestResult<T, 4> BoundedReLuTestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                        float upperBound, float lowerBound,
-                                        float inputScale, int32_t inputOffset, float outputScale, int32_t outputOffset,
-                                        const std::vector<T>& inputData, const std::vector<T>& outputExpectedData,
-                                        unsigned int inputWidth, unsigned int inputHeight,
-                                        unsigned int inputChannels, unsigned int inputBatchSize)
-{
-    unsigned int outputWidth = inputWidth;
-    unsigned int outputHeight = inputHeight;
-    unsigned int outputChannels = inputChannels;
-    unsigned int outputBatchSize = inputBatchSize;
-
-    armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
-        armnn::GetDataType<T>());
-
-    armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
-        armnn::GetDataType<T>());
-
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(inputScale);
-        inputTensorInfo.SetQuantizationOffset(inputOffset);
-
-        outputTensorInfo.SetQuantizationScale(outputScale);
-        outputTensorInfo.SetQuantizationOffset(outputOffset);
-    }
-
-    LayerTestResult<T, 4> result(inputTensorInfo);
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo, inputData);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    // Setup bounded ReLu.
-    armnn::ActivationQueueDescriptor descriptor;
-    armnn::WorkloadInfo workloadInfo;
-    AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get());
-
-    descriptor.m_Parameters.m_Function = armnn::ActivationFunction::BoundedReLu;
-    descriptor.m_Parameters.m_A = upperBound;
-    descriptor.m_Parameters.m_B = lowerBound;
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(descriptor, workloadInfo);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-
-    result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputExpectedData);
-
-    return result;
-}
-
-LayerTestResult<float, 4> BoundedReLuUpperAndLowerBoundTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    unsigned int inputWidth = 4u;
-    unsigned int inputHeight = 5u;
-    unsigned int inputChannels = 1u;
-    unsigned int inputBatchSize = 1;
-
-    std::vector<float> input = std::vector<float>{
-      -2.0f,       0.1f,     0.5f,     1.25f,
-     0.786f,    0.9875f,    -1.5f,    0.384f,
-    1.0001f,       3.5f,     7.5f,    0.896f,
-     2.126f,       2.0f,     0.3f,     0.15f,
-     0.999f,       1.2f,    0.89f,      6.1f,
-    };
-
-    // Calculated manually.
-    std::vector<float> output = std::vector<float>{
-      -1.0f,       0.1f,     0.5f,      1.0f,
-     0.786f,    0.9875f,    -1.0f,    0.384f,
-       1.0f,       1.0f,     1.0f,    0.896f,
-       1.0f,       1.0f,     0.3f,     0.15f,
-     0.999f,       1.0f,    0.89f,      1.0f,
-    };
-
-    return BoundedReLuTestCommon(workloadFactory, 1.0f, -1.0f, 1.0f, 0, 1.0f, 0, input, output,
-                                 inputWidth, inputHeight, inputChannels, inputBatchSize);
-}
-
-LayerTestResult<float, 4> BoundedReLuUpperBoundOnlyTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    unsigned int inputWidth = 4u;
-    unsigned int inputHeight = 5u;
-    unsigned int inputChannels = 1u;
-    unsigned int inputBatchSize = 1;
-
-    std::vector<float> input = std::vector<float>{
-      -1.0f,       0.1f,     0.5f,      6.25f,
-     0.786f,    5.9875f,    -0.5f,     0.384f,
-    6.0001f,       3.5f,     7.5f,     0.896f,
-     2.126f,      12.0f,     0.3f,      0.15f,
-     0.999f,       1.2f,    0.89f,       6.1f,
-    };
-
-    // Calculated manually.
-    std::vector<float> output = std::vector<float>{
-       0.0f,       0.1f,     0.5f,       6.0f,
-     0.786f,    5.9875f,     0.0f,     0.384f,
-       6.0f,       3.5f,     6.0f,     0.896f,
-     2.126f,       6.0f,     0.3f,      0.15f,
-     0.999f,       1.2f,    0.89f,       6.0f,
-    };
-
-    return BoundedReLuTestCommon(workloadFactory, 6.0f, 0.0f, 1.0f, 0, 1.0f, 0, input, output,
-                                 inputWidth, inputHeight, inputChannels, inputBatchSize);
-}
-
-LayerTestResult<uint8_t, 4> BoundedReLuUint8UpperBoundOnlyTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    unsigned int inputWidth     = 3u;
-    unsigned int inputHeight    = 2u;
-    unsigned int inputChannels  = 1u;
-    unsigned int inputBatchSize = 1;
-
-    std::vector<uint8_t> input = std::vector<uint8_t>{
-         51, 124, 28,
-        251,   8, 92
-    };
-
-    // Calculated manually.
-    std::vector<uint8_t> output = std::vector<uint8_t>{
-          0, 122,  0,
-        255,   0, 58
-    };
-
-    float inputScale     = 12.0f / 255.0f;
-    int32_t inputOffset  = 63;
-    float outputScale    = 6.0f / 255.0f;
-    int32_t outputOffset = 0;
-
-    return BoundedReLuTestCommon(workloadFactory, 6.0f, 0.0f,
-                                 inputScale, inputOffset, outputScale, outputOffset,
-                                 input, output,
-                                 inputWidth, inputHeight, inputChannels, inputBatchSize);
-}
-
-LayerTestResult<uint8_t, 4> BoundedReLuUint8UpperAndLowerBoundTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    unsigned int inputWidth     = 3u;
-    unsigned int inputHeight    = 2u;
-    unsigned int inputChannels  = 1u;
-    unsigned int inputBatchSize = 1;
-
-    std::vector<uint8_t> input = std::vector<uint8_t>{
-         51, 230, 28,
-        251,   8, 92
-    };
-
-    // Calculated manually.
-    std::vector<uint8_t> output = std::vector<uint8_t>{
-         51, 192, 32,
-        192,  32, 92
-    };
-
-    int32_t inputOffset = 112;
-    float inputScale    = 0.0125f;
-
-    return BoundedReLuTestCommon(workloadFactory, 1.0f, -1.0f,
-                                 inputScale, inputOffset, inputScale, inputOffset, // Input/output scale & offset same.
-                                 input, output,
-                                 inputWidth, inputHeight, inputChannels, inputBatchSize);
-}
-
-namespace
-{
-
-struct BoundedReLuRandomInputTestTraits
-{
-    constexpr static unsigned int inputHeight = 31u;
-    constexpr static unsigned int inputWidth = 19u;
-    constexpr static unsigned int inputChannels = 4u;
-    constexpr static unsigned int inputBatchSize = 2;
-
-    constexpr static unsigned int outputHeight = inputHeight;
-    constexpr static unsigned int outputWidth = inputWidth;
-    constexpr static unsigned int outputChannels = inputChannels;
-    constexpr static unsigned int outputBatchSize = inputBatchSize;
-
-    static armnn::TensorInfo GetInputTensorInfo()
-    {
-        return armnn::TensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
-            armnn::DataType::Float32);
-    }
-
-    static armnn::TensorInfo GetOutputTensorInfo()
-    {
-        return armnn::TensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
-            armnn::DataType::Float32);
-    }
-};
-
-boost::multi_array<float, 4> BoundedReLuRandomInputTest(armnn::IWorkloadFactory& workloadFactory,
-                                                        float lowerBound,
-                                                        float upperBound,
-                                                        const armnn::ActivationDescriptor& activationDescriptor)
-{
-    const armnn::TensorInfo inputTensorInfo = BoundedReLuRandomInputTestTraits::GetInputTensorInfo();
-    const armnn::TensorInfo outputTensorInfo = BoundedReLuRandomInputTestTraits::GetOutputTensorInfo();
-
-    boost::multi_array<float, 4> output(GetTensorShapeAsArray<4>(outputTensorInfo));
-
-    // Min/max random values passed to MakeRandomTensor are purposely outside of the ReLu
-    // range [lowerBound, upperBound].
-    auto input = MakeRandomTensor<float, 4>(inputTensorInfo, 4605828, lowerBound - 5.0f, upperBound * 2.0f);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    // Set up bounded ReLu.
-    armnn::ActivationQueueDescriptor descriptor;
-    armnn::WorkloadInfo workloadInfo;
-    AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get());
-    descriptor.m_Parameters = activationDescriptor;
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(descriptor, workloadInfo);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&output[0][0][0][0], outputHandle.get());
-
-    return output;
-}
-
-} // namespace
-
-LayerTestResult<float, 4> CompareBoundedReLuTest(armnn::IWorkloadFactory& workloadFactory,
-                                          armnn::IWorkloadFactory& otherWorkloadFactory,
-                                          float upperBound,
-                                          float lowerBound)
-{
-    LayerTestResult<float, 4> result(BoundedReLuRandomInputTestTraits::GetOutputTensorInfo());
-
-    armnn::ActivationDescriptor activationDescriptor;
-    activationDescriptor.m_Function = armnn::ActivationFunction::BoundedReLu;
-    activationDescriptor.m_A = upperBound;
-    activationDescriptor.m_B = lowerBound;
-
-    result.output = BoundedReLuRandomInputTest(workloadFactory, 0.0f, upperBound, activationDescriptor);
-    result.outputExpected = BoundedReLuRandomInputTest(otherWorkloadFactory, 0.0f, upperBound, activationDescriptor);
-
-    return result;
-}
-
-template<typename T>
-LayerTestResult<T,4> ConstantLinearActivationTestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                        float qScale = 0.0f,
-                                                        int32_t qOffset = 0)
-{
-    unsigned int inputHeight    = 20;
-    unsigned int inputWidth     = 17;
-    unsigned int inputChannels  = 3;
-    unsigned int batchSize      = 5;
-
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int shape[]  = {batchSize, inputChannels, inputHeight, inputWidth};
-
-    inputTensorInfo = armnn::TensorInfo(4, shape, armnn::GetDataType<T>());
-    outputTensorInfo = armnn::TensorInfo(4, shape, armnn::GetDataType<T>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    LayerTestResult<T, 4> ret(outputTensorInfo);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    // Do linear activation that should leave the tensor unchanged.
-    armnn::ActivationQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-    data.m_Parameters.m_A = 1.0f;
-    data.m_Parameters.m_B = 0.0f;
-    data.m_Parameters.m_Function = armnn::ActivationFunction::Linear;
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(data, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    boost::multi_array<T, 4> input = MakeRandomTensor<T, 4>(inputTensorInfo, 7123561);
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-
-    // Ensure output equals input.
-    ret.outputExpected = input;
-
-    return ret;
-}
-
-LayerTestResult<float, 4> ConstantLinearActivationTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    return ConstantLinearActivationTestCommon<float>(workloadFactory);
-}
-
-LayerTestResult<uint8_t, 4> ConstantLinearActivationUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return ConstantLinearActivationTestCommon<uint8_t>(workloadFactory, 4.0f, 3);
-}
-
-template<typename T>
-LayerTestResult<T, 4> SimpleActivationTest(armnn::IWorkloadFactory& workloadFactory,
-                                           armnn::ActivationFunction activationFunction,
-                                           float activationParameterA,
-                                           float activationParameterB,
-                                           float qScale,
-                                           int32_t qOffset,
-                                           const std::vector<float>& inputData,
-                                           const std::vector<float>& outputExpectedData)
-{
-    constexpr static unsigned int inputWidth = 16u;
-    constexpr static unsigned int inputHeight = 1u;
-    constexpr static unsigned int inputChannels = 1u;
-    constexpr static unsigned int inputBatchSize = 1u;
-
-    constexpr static unsigned int outputWidth = inputWidth;
-    constexpr static unsigned int outputHeight = inputHeight;
-    constexpr static unsigned int outputChannels = inputChannels;
-    constexpr static unsigned int outputBatchSize = inputBatchSize;
-
-    armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
-                                      armnn::GetDataType<T>());
-    armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
-                                       armnn::GetDataType<T>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    LayerTestResult<T, 4> result(inputTensorInfo);
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData));
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    // Setup bounded ReLu.
-    armnn::ActivationQueueDescriptor descriptor;
-    armnn::WorkloadInfo workloadInfo;
-    AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get());
-
-    descriptor.m_Parameters.m_Function = activationFunction;
-    descriptor.m_Parameters.m_A = activationParameterA;
-    descriptor.m_Parameters.m_B = activationParameterB;
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(descriptor, workloadInfo);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-
-    // Calculated manually.
-    result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, outputExpectedData));
-
-    return result;
-}
-
-template<typename T>
-LayerTestResult<T, 4> SimpleSigmoidTestCommon(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset)
-{
-    std::vector<float> inputData = {
-        -0.1f, -0.2f, -0.3f, -0.4f,
-        0.1f,  0.2f,  0.3f,  0.4f,
-        -1.0f, -2.0f, -3.0f, -4.0f,
-        1.0f,  2.0f,  3.0f,  4.0f
-    };
-
-    // Calculate output values for input.
-    auto f = [](float value)
-    {
-        return 1.0f / (1.0f + std::exp(-value));
-    };
-    std::vector<float> outputExpectedData(inputData.size());
-    std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f);
-
-    return SimpleActivationTest<T>(workloadFactory,
-                                   armnn::ActivationFunction::Sigmoid,
-                                   0.f,
-                                   0.f,
-                                   qScale,
-                                   qOffset,
-                                   inputData,
-                                   outputExpectedData);
-}
-
-LayerTestResult<float, 4> SimpleSigmoidTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    return SimpleSigmoidTestCommon<float>(workloadFactory, 0.0f, 0);
-}
-
-LayerTestResult<uint8_t, 4> SimpleSigmoidUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return SimpleSigmoidTestCommon<uint8_t>(workloadFactory, 0.1f, 50);
-}
-
-template<typename T>
-LayerTestResult<T,4> CompareActivationTestImpl(armnn::IWorkloadFactory& workloadFactory,
-                                               armnn::IWorkloadFactory& refWorkloadFactory,
-                                               armnn::ActivationFunction f,
-                                               unsigned int batchSize = 5,
-                                               float qScale = 0.0f,
-                                               int32_t qOffset = 0)
-{
-    unsigned int width     = 17;
-    unsigned int height    = 29;
-    unsigned int channels  = 2;
-
-    float a = 0.234f;
-    float b = -12.345f;
-
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int shape[] = {batchSize, channels, height, width};
-
-    inputTensorInfo = armnn::TensorInfo(4, shape, armnn::GetDataType<T>());
-    outputTensorInfo = armnn::TensorInfo(4, shape, armnn::GetDataType<T>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    float minVal = -10.f;
-    if (f == armnn::ActivationFunction::Sqrt)
-    {
-        minVal = 0.f;
-    }
-
-    boost::multi_array<T, 4> input = MakeRandomTensor<T, 4>(inputTensorInfo, 21453, minVal, 10.f);
-
-
-    LayerTestResult<T,4> ret(outputTensorInfo);
-    auto boostArrayExtents = boost::extents
-        [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(batchSize)]
-    [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(channels)]
-    [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(height)]
-    [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(width)];
-    ret.output.resize(boostArrayExtents);
-    ret.outputExpected.resize(boostArrayExtents);
-
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ActivationQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-    data.m_Parameters.m_A        = a;
-    data.m_Parameters.m_B        = b;
-    data.m_Parameters.m_Function = f;
-
-    armnn::ActivationQueueDescriptor refData = data;
-    armnn::WorkloadInfo refInfo = info;
-    SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get());
-    SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(data, info);
-    BOOST_ASSERT(workload != nullptr);
-    std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateActivation(refData, refInfo);
-    BOOST_ASSERT(workloadRef != nullptr);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-    inputHandleRef->Allocate();
-    outputHandleRef->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-    CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]);
-
-    workload->Execute();
-    workloadRef->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-    CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get());
-
-    return ret;
-}
-
-LayerTestResult<float,4> CompareActivationTest(armnn::IWorkloadFactory& workloadFactory,
-                                               armnn::IWorkloadFactory& refWorkloadFactory,
-                                               armnn::ActivationFunction f,
-                                               unsigned int batchSize)
-{
-    return CompareActivationTestImpl<float>(workloadFactory, refWorkloadFactory, f, batchSize);
-}
-
-LayerTestResult<uint8_t,4> CompareActivationUint8Test(armnn::IWorkloadFactory& workloadFactory,
-                                                      armnn::IWorkloadFactory& refWorkloadFactory,
-                                                      armnn::ActivationFunction f)
-{
-    return CompareActivationTestImpl<uint8_t>(workloadFactory, refWorkloadFactory, f, 5, 0.1f, 50);
-}
diff --git a/src/backends/test/BackendIdTests.cpp b/src/backends/test/BackendIdTests.cpp
deleted file mode 100644
index 0ef0a20d7f..0000000000
--- a/src/backends/test/BackendIdTests.cpp
+++ /dev/null
@@ -1,27 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#include <boost/test/unit_test.hpp>
-
-#include <armnn/BackendId.hpp>
-#include <armnn/Types.hpp>
-
-using namespace armnn;
-
-BOOST_AUTO_TEST_SUITE(BackendIdTests)
-
-BOOST_AUTO_TEST_CASE(CreateBackendIdFromCompute)
-{
-    BackendId fromCompute{Compute::GpuAcc};
-    BOOST_TEST(fromCompute.Get() == GetComputeDeviceAsCString(Compute::GpuAcc));
-}
-
-BOOST_AUTO_TEST_CASE(CreateBackendIdVectorFromCompute)
-{
-    std::vector<BackendId> fromComputes = {Compute::GpuAcc, Compute::CpuRef};
-    BOOST_TEST(fromComputes[0].Get() == GetComputeDeviceAsCString(Compute::GpuAcc));
-    BOOST_TEST(fromComputes[1].Get() == GetComputeDeviceAsCString(Compute::CpuRef));
-}
-
-BOOST_AUTO_TEST_SUITE_END()
diff --git a/src/backends/test/BackendRegistryTests.cpp b/src/backends/test/BackendRegistryTests.cpp
deleted file mode 100644
index bfeefda6bd..0000000000
--- a/src/backends/test/BackendRegistryTests.cpp
+++ /dev/null
@@ -1,101 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#include <boost/test/unit_test.hpp>
-
-#include <backends/BackendRegistry.hpp>
-#include <armnn/Types.hpp>
-
-namespace
-{
-
-class SwapRegistryStorage : public armnn::BackendRegistry
-{
-public:
-    SwapRegistryStorage() : armnn::BackendRegistry()
-    {
-        Swap(armnn::BackendRegistryInstance(),  m_TempStorage);
-    }
-
-    ~SwapRegistryStorage()
-    {
-        Swap(armnn::BackendRegistryInstance(),m_TempStorage);
-    }
-
-private:
-    FactoryStorage m_TempStorage;
-};
-
-}
-
-BOOST_AUTO_TEST_SUITE(BackendRegistryTests)
-
-BOOST_AUTO_TEST_CASE(SwapRegistry)
-{
-    using namespace armnn;
-    auto nFactories = BackendRegistryInstance().Size();
-    {
-        SwapRegistryStorage helper;
-        BOOST_TEST(BackendRegistryInstance().Size() == 0);
-    }
-    BOOST_TEST(BackendRegistryInstance().Size() == nFactories);
-}
-
-BOOST_AUTO_TEST_CASE(TestRegistryHelper)
-{
-    using namespace armnn;
-    SwapRegistryStorage helper;
-
-    bool called = false;
-
-    StaticRegistryInitializer<BackendRegistry> factoryHelper(
-        BackendRegistryInstance(),
-        "HelloWorld",
-        [&called](const EmptyInitializer&)
-        {
-            called = true;
-            return armnn::IBackendInternalUniquePtr(nullptr);
-        }
-    );
-
-    // sanity check: the factory has not been called yet
-    BOOST_TEST(called == false);
-
-    auto factoryFunction = BackendRegistryInstance().GetFactory("HelloWorld");
-
-    // sanity check: the factory still not called
-    BOOST_TEST(called == false);
-
-    factoryFunction(EmptyInitializer());
-    BOOST_TEST(called == true);
-}
-
-BOOST_AUTO_TEST_CASE(TestDirectCallToRegistry)
-{
-    using namespace armnn;
-    SwapRegistryStorage helper;
-
-    bool called = false;
-    BackendRegistryInstance().Register(
-        "HelloWorld",
-        [&called](const EmptyInitializer&)
-        {
-            called = true;
-            return armnn::IBackendInternalUniquePtr(nullptr);
-        }
-    );
-
-    // sanity check: the factory has not been called yet
-    BOOST_TEST(called == false);
-
-    auto factoryFunction = BackendRegistryInstance().GetFactory("HelloWorld");
-
-    // sanity check: the factory still not called
-    BOOST_TEST(called == false);
-
-    factoryFunction(EmptyInitializer());
-    BOOST_TEST(called == true);
-}
-
-BOOST_AUTO_TEST_SUITE_END()
diff --git a/src/backends/test/BatchNormTestImpl.hpp b/src/backends/test/BatchNormTestImpl.hpp
deleted file mode 100644
index 166c44435d..0000000000
--- a/src/backends/test/BatchNormTestImpl.hpp
+++ /dev/null
@@ -1,187 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#pragma once
-
-#include <armnn/ArmNN.hpp>
-#include <armnn/Tensor.hpp>
-
-#include <test/TensorHelpers.hpp>
-
-#include <backends/CpuTensorHandle.hpp>
-#include <backends/WorkloadFactory.hpp>
-
-#include <backends/test/QuantizeHelper.hpp>
-
-template<typename T>
-LayerTestResult<T, 4> BatchNormTestImpl(armnn::IWorkloadFactory& workloadFactory,
-                                        const armnn::TensorShape& inputOutputTensorShape,
-                                        const std::vector<float>& inputValues,
-                                        const std::vector<float>& expectedOutputValues,
-                                        float qScale,
-                                        int32_t qOffset,
-                                        armnn::DataLayout dataLayout)
-{
-    armnn::TensorInfo inputTensorInfo(inputOutputTensorShape, armnn::GetDataType<T>());
-    armnn::TensorInfo outputTensorInfo(inputOutputTensorShape, armnn::GetDataType<T>());
-
-    armnn::DataLayoutIndexed dataLayoutIndexed(dataLayout);
-
-    armnn::TensorInfo tensorInfo({ inputOutputTensorShape[dataLayoutIndexed.GetChannelsIndex()] },
-                                 armnn::GetDataType<T>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if (armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-        tensorInfo.SetQuantizationScale(qScale);
-        tensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    auto inputTensor = MakeTensor<T, 4>(inputTensorInfo,
-                                        QuantizedVector<T>(qScale, qOffset, inputValues));
-
-    // These values are per-channel of the input.
-    auto mean     = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {3, -2}));
-    auto variance = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {4,  9}));
-    auto beta     = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {3,  2}));
-    auto gamma    = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {2,  1}));
-
-    LayerTestResult<T, 4> result(outputTensorInfo);
-
-    result.outputExpected = MakeTensor<T, 4>(inputTensorInfo,
-                                             QuantizedVector<T>(qScale, qOffset, expectedOutputValues));
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ScopedCpuTensorHandle meanTensor(tensorInfo);
-    armnn::ScopedCpuTensorHandle varianceTensor(tensorInfo);
-    armnn::ScopedCpuTensorHandle betaTensor(tensorInfo);
-    armnn::ScopedCpuTensorHandle gammaTensor(tensorInfo);
-
-    armnn::BatchNormalizationQueueDescriptor descriptor;
-    descriptor.m_Mean                    = &meanTensor;
-    descriptor.m_Variance                = &varianceTensor;
-    descriptor.m_Beta                    = &betaTensor;
-    descriptor.m_Gamma                   = &gammaTensor;
-    descriptor.m_Parameters.m_Eps        = 0.0f;
-    descriptor.m_Parameters.m_DataLayout = dataLayout;
-    armnn::WorkloadInfo info;
-
-    AllocateAndCopyDataToITensorHandle(&meanTensor, &mean[0]);
-    AllocateAndCopyDataToITensorHandle(&varianceTensor, &variance[0]);
-    AllocateAndCopyDataToITensorHandle(&betaTensor, &beta[0]);
-    AllocateAndCopyDataToITensorHandle(&gammaTensor, &gamma[0]);
-
-    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateBatchNormalization(descriptor, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-
-    return result;
-}
-
-
-template<typename T>
-LayerTestResult<T,4> BatchNormTestNhwcImpl(armnn::IWorkloadFactory& workloadFactory,
-                                           float qScale,
-                                           int32_t qOffset)
-{
-    const unsigned int width    = 2;
-    const unsigned int height   = 3;
-    const unsigned int channels = 2;
-    const unsigned int num      = 1;
-
-    armnn::TensorInfo inputTensorInfo({num, height, width, channels}, armnn::GetDataType<T>());
-    armnn::TensorInfo outputTensorInfo({num, height, width, channels}, armnn::GetDataType<T>());
-    armnn::TensorInfo tensorInfo({channels}, armnn::GetDataType<T>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-        tensorInfo.SetQuantizationScale(qScale);
-        tensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset,
-        {
-            1.f, 1.f, 4.f, 1.f,
-            4.f, 4.f, 2.f, 1.f,
-            1.f, -2.f, 6.f, 4.f
-        }));
-    // These values are per-channel of the input.
-    auto mean     = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {3, -2}));
-    auto variance = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {4, 9}));
-    auto beta     = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {3, 2}));
-    auto gamma    = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {2, 1}));
-    LayerTestResult<T,4> ret(outputTensorInfo);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::BatchNormalizationQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    armnn::ScopedCpuTensorHandle meanTensor(tensorInfo);
-    armnn::ScopedCpuTensorHandle varianceTensor(tensorInfo);
-    armnn::ScopedCpuTensorHandle betaTensor(tensorInfo);
-    armnn::ScopedCpuTensorHandle gammaTensor(tensorInfo);
-
-    AllocateAndCopyDataToITensorHandle(&meanTensor, &mean[0]);
-    AllocateAndCopyDataToITensorHandle(&varianceTensor, &variance[0]);
-    AllocateAndCopyDataToITensorHandle(&betaTensor, &beta[0]);
-    AllocateAndCopyDataToITensorHandle(&gammaTensor, &gamma[0]);
-
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-    data.m_Mean             = &meanTensor;
-    data.m_Variance         = &varianceTensor;
-    data.m_Beta             = &betaTensor;
-    data.m_Gamma            = &gammaTensor;
-    data.m_Parameters.m_Eps = 0.0f;
-    data.m_Parameters.m_DataLayout = armnn::DataLayout::NHWC;
-
-    // For each channel:
-    // substract mean, divide by standard deviation (with an epsilon to avoid div by 0),
-    // multiply by gamma and add beta
-    ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset,
-        {
-            1.f, 3.f, 4.f, 3.f,
-            4.f, 4.f, 2.f, 3.f,
-            1.f, 2.f, 6.f, 4.f
-        }));
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateBatchNormalization(data, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-
-    return ret;
-}
\ No newline at end of file
diff --git a/src/backends/test/CMakeLists.txt b/src/backends/test/CMakeLists.txt
deleted file mode 100644
index 39038cfd7d..0000000000
--- a/src/backends/test/CMakeLists.txt
+++ /dev/null
@@ -1,41 +0,0 @@
-#
-# Copyright © 2017 Arm Ltd. All rights reserved.
-# SPDX-License-Identifier: MIT
-#
-
-list(APPEND armnnBackendsCommonUnitTests_sources
-    ActivationFixture.hpp
-    ActivationTestImpl.hpp
-    BackendIdTests.cpp
-    BackendRegistryTests.cpp
-    BatchNormTestImpl.hpp
-    Conv2dTestImpl.hpp
-    ConvertFp16ToFp32TestImpl.hpp
-    ConvertFp32ToFp16TestImpl.hpp
-    EndToEndTestImpl.hpp
-    FullyConnectedTestImpl.hpp
-    IsLayerSupportedTestImpl.hpp
-    JsonPrinterTestImpl.hpp
-    LayerReleaseConstantDataTest.cpp
-    LayerTests.cpp
-    LayerTests.hpp
-    LstmTestImpl.hpp
-    NormTestImpl.hpp
-    OptimizedNetworkTests.cpp
-    PermuteTestImpl.hpp
-    Pooling2dTestImpl.hpp
-    QuantizeHelper.hpp
-    ReshapeTestImpl.hpp
-    RuntimeTestImpl.hpp
-    SoftmaxTestImpl.hpp
-    SplitterTestImpl.hpp
-    TensorCopyUtils.cpp
-    TensorCopyUtils.hpp
-    WorkloadDataValidation.cpp
-    WorkloadTestUtils.hpp
-)
-
-add_library(armnnBackendsCommonUnitTests OBJECT ${armnnBackendsCommonUnitTests_sources})
-target_include_directories(armnnBackendsCommonUnitTests PRIVATE ${PROJECT_SOURCE_DIR}/src)
-target_include_directories(armnnBackendsCommonUnitTests PRIVATE ${PROJECT_SOURCE_DIR}/src/armnn)
-target_include_directories(armnnBackendsCommonUnitTests PRIVATE ${PROJECT_SOURCE_DIR}/src/armnnUtils)
\ No newline at end of file
diff --git a/src/backends/test/Conv2dTestImpl.hpp b/src/backends/test/Conv2dTestImpl.hpp
deleted file mode 100755
index 3791fb0a8e..0000000000
--- a/src/backends/test/Conv2dTestImpl.hpp
+++ /dev/null
@@ -1,1240 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#pragma once
-
-#include <string>
-#include <armnn/ArmNN.hpp>
-#include <armnn/Tensor.hpp>
-#include <armnn/TypesUtils.hpp>
-
-#include <test/TensorHelpers.hpp>
-#include "QuantizeHelper.hpp"
-
-#include <backends/CpuTensorHandle.hpp>
-#include <backends/WorkloadFactory.hpp>
-#include "Permute.hpp"
-#include <boost/numeric/conversion/cast.hpp>
-
-// Mapping from input type to bias type for fully connected layers.
-// float => float, uint8_t => int32_t
-template<typename T>
-struct FullyConnectedBiasTypeForInputType;
-
-template<>
-struct FullyConnectedBiasTypeForInputType<float>
-{
-    using Type = float;
-};
-
-template<>
-struct FullyConnectedBiasTypeForInputType<uint8_t>
-{
-    using Type = int32_t;
-};
-
-// Modifies a std::vector in-place using a specified bias.
-template<typename T, typename B>
-void ApplyBias(std::vector<T>& v, float vScale, int32_t vOffset,
-    const std::vector<B>& bias, float bScale, int32_t bOffset, uint32_t w, uint32_t h)
-{
-    BOOST_ASSERT_MSG((armnn::IsQuantizedType<T>() && vScale != 0.0f) || (!armnn::IsQuantizedType<T>()),
-                     "Invalid type and parameter combination.");
-    BOOST_ASSERT_MSG((armnn::IsQuantizedType<B>() && bScale != 0.0f) || (!armnn::IsQuantizedType<B>()),
-                     "Invalid type and parameter combination.");
-
-    // Note we need to dequantize and re-quantize the image value and the bias.
-    for (uint32_t i = 0; i < bias.size(); ++i)
-    {
-        float dBias = SelectiveDequantize(bias[i], bScale, bOffset);
-        for (uint32_t y = 0; y < h; ++y)
-        {
-            for (uint32_t x = 0; x < w; ++x)
-            {
-                uint32_t offset = (i * h + y) * w + x;
-                BOOST_ASSERT(offset < v.size());
-                T& outRef = v[offset];
-                float dOutput = SelectiveDequantize(outRef, vScale, vOffset);
-                outRef = SelectiveQuantize<T>(dOutput + dBias, vScale, vOffset);
-            }
-        }
-    }
-}
-
-template<typename T, typename B>
-LayerTestResult<T, 4> SimpleConvolution2dTestImpl(armnn::IWorkloadFactory& workloadFactory,
-                                                  const boost::multi_array<T, 4>& originalInput,
-                                                  const boost::multi_array<T, 4>& originalKernel,
-                                                  const boost::multi_array<B, 1>& bias,
-                                                  const boost::multi_array<T, 4>& originalOutputExpected,
-                                                  float qScale,
-                                                  int32_t qOffset,
-                                                  const armnn::DataLayoutIndexed& layout = armnn::DataLayout::NCHW,
-                                                  uint32_t padLeft = 0,
-                                                  uint32_t padTop = 0,
-                                                  uint32_t padRight = 0,
-                                                  uint32_t padBottom = 0)
-{
-    unsigned int inputHeight   = boost::numeric_cast<unsigned int>(originalInput.shape()[2]);
-    unsigned int inputWidth    = boost::numeric_cast<unsigned int>(originalInput.shape()[3]);
-    unsigned int inputChannels = boost::numeric_cast<unsigned int>(originalInput.shape()[1]);
-    unsigned int inputNum      = boost::numeric_cast<unsigned int>(originalInput.shape()[0]);
-
-    unsigned int outputHeight   = boost::numeric_cast<unsigned int>(originalOutputExpected.shape()[2]);
-    unsigned int outputWidth    = boost::numeric_cast<unsigned int>(originalOutputExpected.shape()[3]);
-    unsigned int outputChannels = boost::numeric_cast<unsigned int>(originalOutputExpected.shape()[1]);
-    unsigned int outputNum      = boost::numeric_cast<unsigned int>(originalOutputExpected.shape()[0]);
-
-    unsigned int kernelHeight = boost::numeric_cast<unsigned int>(originalKernel.shape()[2]);
-    unsigned int kernelWidth = boost::numeric_cast<unsigned int>(originalKernel.shape()[3]);
-    unsigned int kernelChannels = boost::numeric_cast<unsigned int>(originalKernel.shape()[1]);
-    unsigned int kernelDepthMul = boost::numeric_cast<unsigned int>(originalKernel.shape()[0]);
-
-    bool biasEnabled = bias.size() > 0;
-
-    // This function currently assumes 1 batch of input/output (and duplicates this into 2 batches).
-    BOOST_ASSERT(inputNum == 1);
-    BOOST_ASSERT(outputNum == 1);
-
-    // If a bias is used, its size must equal the number of output channels.
-    BOOST_ASSERT(!biasEnabled || bias.size() == outputChannels);
-
-
-    // Note these tensors will use two (identical) batches.
-    armnn::TensorInfo inputTensorInfo = GetTensorInfo<T>(2*inputNum, inputChannels, inputHeight, inputWidth, layout);
-    armnn::TensorInfo outputTensorInfo = GetTensorInfo<T>(
-            2*outputNum, outputChannels, outputHeight, outputWidth, layout);
-    armnn::TensorInfo kernelDesc = GetTensorInfo<T>(kernelDepthMul, kernelChannels, kernelHeight, kernelWidth, layout);
-    armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, armnn::GetDataType<B>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-        kernelDesc.SetQuantizationScale(qScale);
-        kernelDesc.SetQuantizationOffset(qOffset);
-        biasDesc.SetQuantizationScale(qScale*qScale);
-        biasDesc.SetQuantizationOffset(0);
-    }
-
-    LayerTestResult<T, 4> ret(outputTensorInfo);
-
-    // Construct input data - two batches of the same input image.
-    std::vector<T> inputImage;
-    inputImage.assign(originalInput.data(), originalInput.data() + 1*inputChannels*inputHeight*inputWidth);
-    std::vector<T> inputData;
-    inputData.insert(inputData.end(), inputImage.begin(), inputImage.end());
-    inputData.insert(inputData.end(), inputImage.begin(), inputImage.end());
-
-    // at this point if we require it permute the input data
-    const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
-    if (layout.GetDataLayout() == armnn::DataLayout::NHWC)
-    {
-        std::vector<T> tmp(inputData.size());
-        armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data());
-        inputData = tmp;
-    }
-
-    auto batchedInput = MakeTensor<T, 4>(inputTensorInfo, inputData);
-
-    std::vector<T> outputImage;
-    outputImage.assign(originalOutputExpected.data(),
-            originalOutputExpected.data() + outputChannels*outputHeight*outputWidth);
-
-    // Apply bias to output image if it is enabled.
-    if(biasEnabled)
-    {
-        std::vector<T> biasV;
-        biasV.assign(bias.data(), bias.data() + outputChannels);
-        ApplyBias(outputImage, outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(),
-            biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(),
-            outputWidth, outputHeight);
-    }
-
-    // Construct expected output data - two identical images.
-    std::vector<T> outputData;
-    outputData.insert(outputData.end(), outputImage.begin(), outputImage.end());
-    outputData.insert(outputData.end(), outputImage.begin(), outputImage.end());
-
-    // at this point if we require it permute the expected output
-    if (layout.GetDataLayout() == armnn::DataLayout::NHWC)
-    {
-        std::vector<T> tmp(outputData.size());
-        armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp.data());
-        outputData = tmp;
-    }
-    ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData);
-
-    // Todo: nontrivial padding and strides.
-    uint32_t                    strideX  = 1;
-    uint32_t                    strideY  = 1;
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::Convolution2dQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc);
-    armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
-    // Permute the kernel if necessary
-    boost::multi_array<T, 4> kernel = boost::multi_array<T, 4>(originalKernel);
-    if (layout.GetDataLayout() == armnn::DataLayout::NHWC)
-    {
-        armnnUtils::Permute(kernelDesc.GetShape(), NCHWToNHWC, originalKernel.data(), kernel.data());
-    }
-    AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]);
-
-    if(biasEnabled)
-    {
-        AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]);
-    }
-
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    data.m_Weight = &weightsTensor;
-    data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled - can be a source of bugs.
-    data.m_Parameters.m_StrideX = strideX;
-    data.m_Parameters.m_StrideY = strideY;
-    data.m_Parameters.m_PadLeft = padLeft;
-    data.m_Parameters.m_PadRight = padRight;
-    data.m_Parameters.m_PadTop = padTop;
-    data.m_Parameters.m_PadBottom = padBottom;
-    data.m_Parameters.m_BiasEnabled = biasEnabled;
-    data.m_Parameters.m_DataLayout = layout.GetDataLayout();
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConvolution2d(data, info);
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &batchedInput[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-
-    return ret;
-}
-
-template<typename T, typename B>
-LayerTestResult<T, 4> SimpleConvolution2dNhwcTestImpl(armnn::IWorkloadFactory& workloadFactory,
-                                                      const boost::multi_array<T, 4>& input,
-                                                      const boost::multi_array<T, 4>& kernel,
-                                                      const boost::multi_array<B, 1>& bias,
-                                                      const boost::multi_array<T, 4>& outputExpected,
-                                                      armnn::DataLayout dataLayout,
-                                                      float qScale,
-                                                      int32_t qOffset,
-                                                      uint32_t padLeft = 1,
-                                                      uint32_t padTop = 1,
-                                                      uint32_t padRight = 1,
-                                                      uint32_t padBottom = 1,
-                                                      uint32_t strideX  = 1,
-                                                      uint32_t strideY  = 1)
-{
-    unsigned int inputNum       = boost::numeric_cast<unsigned int>(input.shape()[0]);
-    unsigned int inputChannels  = boost::numeric_cast<unsigned int>(input.shape()[3]);
-    unsigned int inputHeight    = boost::numeric_cast<unsigned int>(input.shape()[1]);
-    unsigned int inputWidth     = boost::numeric_cast<unsigned int>(input.shape()[2]);
-
-    unsigned int kernelChanMul  = boost::numeric_cast<unsigned int>(kernel.shape()[0]);
-    unsigned int kernelChannels = boost::numeric_cast<unsigned int>(kernel.shape()[3]);
-    unsigned int kernelHeight   = boost::numeric_cast<unsigned int>(kernel.shape()[1]);
-    unsigned int kernelWidth    = boost::numeric_cast<unsigned int>(kernel.shape()[2]);
-
-    unsigned int outputNum      = boost::numeric_cast<unsigned int>(outputExpected.shape()[0]);
-    unsigned int outputChannels = boost::numeric_cast<unsigned int>(outputExpected.shape()[3]);
-    unsigned int outputHeight   = boost::numeric_cast<unsigned int>(outputExpected.shape()[1]);
-    unsigned int outputWidth    = boost::numeric_cast<unsigned int>(outputExpected.shape()[2]);
-
-    bool biasEnabled = bias.size() > 0;
-
-    // Creates the tensors.
-    armnn::TensorInfo inputTensorInfo({inputNum, inputHeight, inputWidth, inputChannels}, armnn::GetDataType<T>());
-    armnn::TensorInfo outputTensorInfo({outputNum, outputHeight, outputWidth, outputChannels},
-                                       armnn::GetDataType<T>());
-    armnn::TensorInfo kernelDesc({kernelChanMul, kernelHeight, kernelWidth, kernelChannels}, armnn::GetDataType<T>());
-    armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, armnn::GetDataType<B>());
-
-    // Construct the input data.
-    std::vector<T> inputData;
-    inputData.assign(input.data(), input.data() + inputHeight*inputWidth*inputChannels);
-    auto batchedInput = MakeTensor<T, 4>(inputTensorInfo, inputData);
-
-    // Construct the output data, with bias applied, as appropriate.
-    std::vector<T> outputData;
-    outputData.assign(outputExpected.data(), outputExpected.data() + outputHeight*outputWidth*outputChannels);
-
-    LayerTestResult<T, 4> ret(outputTensorInfo);
-    ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc);
-    AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]);
-
-    armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
-
-    armnn::Convolution2dQueueDescriptor data;
-
-    data.m_Weight = &weightsTensor;
-    data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled - can be a source of bugs.
-    data.m_Parameters.m_StrideX = strideX;
-    data.m_Parameters.m_StrideY = strideY;
-    data.m_Parameters.m_PadLeft = padLeft;
-    data.m_Parameters.m_PadRight = padRight;
-    data.m_Parameters.m_PadTop = padTop;
-    data.m_Parameters.m_PadBottom = padBottom;
-    data.m_Parameters.m_BiasEnabled = biasEnabled;
-    data.m_Parameters.m_DataLayout = dataLayout;
-
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConvolution2d(data, info);
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &batchedInput[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-
-    return ret;
-}
-
-template<typename T, typename B>
-LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestImpl(armnn::IWorkloadFactory& workloadFactory,
-                                                               const boost::multi_array<T, 4>& input,
-                                                               const boost::multi_array<T, 4>& originalKernel,
-                                                               const boost::multi_array<B, 1>& bias,
-                                                               const boost::multi_array<T, 4>& outputExpected,
-                                                               float qScale,
-                                                               int32_t qOffset,
-                                                               const armnn::DataLayoutIndexed& layout,
-                                                               uint32_t padLeft = 0,
-                                                               uint32_t padTop = 0,
-                                                               uint32_t padRight = 0,
-                                                               uint32_t padBottom = 0,
-                                                               uint32_t strideX = 1,
-                                                               uint32_t strideY = 1)
-{
-    unsigned int inputNum       = boost::numeric_cast<unsigned int>(input.shape()[0]);
-    unsigned int inputChannels  = boost::numeric_cast<unsigned int>(input.shape()[1]);
-    unsigned int inputHeight    = boost::numeric_cast<unsigned int>(input.shape()[2]);
-    unsigned int inputWidth     = boost::numeric_cast<unsigned int>(input.shape()[3]);
-    unsigned int kernelChanMul  = boost::numeric_cast<unsigned int>(originalKernel.shape()[0]);
-    unsigned int kernelChannels = boost::numeric_cast<unsigned int>(originalKernel.shape()[1]);
-    unsigned int kernelHeight   = boost::numeric_cast<unsigned int>(originalKernel.shape()[2]);
-    unsigned int kernelWidth    = boost::numeric_cast<unsigned int>(originalKernel.shape()[3]);
-    unsigned int outputNum      = boost::numeric_cast<unsigned int>(outputExpected.shape()[0]);
-    unsigned int outputChannels = boost::numeric_cast<unsigned int>(outputExpected.shape()[1]);
-    unsigned int outputHeight   = boost::numeric_cast<unsigned int>(outputExpected.shape()[2]);
-    unsigned int outputWidth    = boost::numeric_cast<unsigned int>(outputExpected.shape()[3]);
-
-    // If a bias is used, its size must equal the number of output channels.
-    bool biasEnabled = bias.size() > 0;
-    BOOST_ASSERT(!biasEnabled || bias.size() == outputChannels);
-
-    // Creates the tensors.
-    armnn::TensorInfo inputTensorInfo = GetTensorInfo<T>(inputNum, inputChannels, inputHeight, inputWidth, layout);
-    armnn::TensorInfo outputTensorInfo = GetTensorInfo<T>(outputNum, outputChannels, outputHeight, outputWidth, layout);
-    armnn::TensorInfo kernelDesc = GetTensorInfo<T>(kernelChanMul, kernelChannels, kernelHeight, kernelWidth, layout);
-    armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, armnn::GetDataType<B>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if (armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-        kernelDesc.SetQuantizationScale(qScale);
-        kernelDesc.SetQuantizationOffset(qOffset);
-        biasDesc.SetQuantizationScale(qScale*qScale);
-        biasDesc.SetQuantizationOffset(0);
-    }
-
-    // Construct the input data.
-    std::vector<T> inputData;
-    inputData.assign(input.data(), input.data() + inputChannels*inputHeight*inputWidth);
-
-    // At this point if we require it permute the input data
-    const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
-    if (layout.GetDataLayout() == armnn::DataLayout::NHWC)
-    {
-        std::vector<T> tmp(inputData.size());
-        armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data());
-        inputData = tmp;
-    }
-
-    auto batchedInput = MakeTensor<T, 4>(inputTensorInfo, inputData);
-
-    // Construct the output data, with bias applied, as appropriate.
-    std::vector<T> outputData;
-    outputData.assign(outputExpected.data(), outputExpected.data() + outputChannels*outputHeight*outputWidth);
-    if (biasEnabled)
-    {
-        std::vector<T> biasV;
-        biasV.assign(bias.data(), bias.data() + outputChannels);
-        ApplyBias(outputData, outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(),
-            biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(),
-            outputWidth, outputHeight);
-    }
-
-    LayerTestResult<T, 4> ret(outputTensorInfo);
-
-    // At this point if we require it permute the expected output
-    if (layout.GetDataLayout() == armnn::DataLayout::NHWC)
-    {
-        std::vector<T> tmp(outputData.size());
-        armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp.data());
-        outputData = tmp;
-    }
-
-    ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc);
-
-    // Permute the kernel if necessary
-    boost::multi_array<T, 4> kernel = boost::multi_array<T, 4>(originalKernel);
-    if (layout.GetDataLayout() == armnn::DataLayout::NHWC)
-    {
-        armnnUtils::Permute(kernelDesc.GetShape(), NCHWToNHWC, originalKernel.data(), kernel.data());
-    }
-
-    AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]);
-
-    armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
-    if (biasEnabled)
-    {
-        AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]);
-    }
-
-    armnn::DepthwiseConvolution2dQueueDescriptor data;
-    data.m_Weight = &weightsTensor;
-    data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled - it can be a source of bugs.
-    data.m_Parameters.m_StrideX = strideX;
-    data.m_Parameters.m_StrideY = strideY;
-    data.m_Parameters.m_PadLeft = padLeft;
-    data.m_Parameters.m_PadRight = padRight;
-    data.m_Parameters.m_PadTop = padTop;
-    data.m_Parameters.m_PadBottom = padBottom;
-    data.m_Parameters.m_BiasEnabled = biasEnabled;
-    data.m_Parameters.m_DataLayout = layout.GetDataLayout();
-
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDepthwiseConvolution2d(data, info);
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &batchedInput[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-
-    return ret;
-}
-
-template<typename T, typename B>
-LayerTestResult<T, 4> DepthwiseConvolution2dDepthMul1TestImpl(armnn::IWorkloadFactory& workloadFactory,
-                                                              float qScale,
-                                                              int32_t qOffset,
-                                                              bool biasEnabled,
-                                                              const armnn::DataLayoutIndexed& layout)
-{
-    unsigned int inputHeight = 3;
-    unsigned int inputWidth = 3;
-    unsigned int inputChannels = 2;
-    unsigned int inputNum = 1;
-
-    unsigned int kernelHeight = 3;
-    unsigned int kernelWidth = 3;
-    unsigned int kernelChannels = inputChannels;
-
-    unsigned int outputHeight = 1;
-    unsigned int outputWidth = 1;
-    unsigned int outputChannels = kernelChannels;
-    unsigned int outputNum = inputNum;
-
-    armnn::TensorInfo inputTensorInfo = GetTensorInfo<T>(inputNum, inputChannels, inputHeight, inputWidth, layout);
-    armnn::TensorInfo outputTensorInfo = GetTensorInfo<T>(outputNum, outputChannels, outputHeight, outputWidth, layout);
-    armnn::TensorInfo kernelDesc = GetTensorInfo<T>(1, outputChannels, kernelHeight, kernelWidth, layout);
-    armnn::TensorInfo biasDesc({ outputChannels }, armnn::GetDataType<B>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-        kernelDesc.SetQuantizationScale(qScale);
-        kernelDesc.SetQuantizationOffset(qOffset);
-        biasDesc.SetQuantizationScale(qScale*qScale);
-        biasDesc.SetQuantizationOffset(0);
-    }
-    std::vector<T> inputData = std::vector<T>(
-            QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), {
-                    1.f, 2.f, 1.f,
-                    2.f, 1.f, 2.f,
-                    1.f, 2.f, 1.f,
-
-                    1.f, 2.f, 1.f,
-                    2.f, 1.f, 2.f,
-                    1.f, 2.f, 1.f,
-            }));
-    // at this point if we require it permute the input data
-    const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
-    if (layout.GetDataLayout() == armnn::DataLayout::NHWC)
-    {
-        std::vector<T> tmp(inputData.size());
-        armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data());
-        inputData = tmp;
-    }
-    auto input = MakeTensor<T, 4>(inputTensorInfo, inputData);
-
-    std::vector<B> biasV(QuantizedVector<B>(biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(),
-                                            {0, 2}));
-    auto bias = MakeTensor<B, 1>(biasDesc, biasV);
-
-    std::vector<T> kernelData = std::vector<T>(
-            QuantizedVector<T>(kernelDesc.GetQuantizationScale(), kernelDesc.GetQuantizationOffset(), {
-                    1.f, 0.f,  1.f,
-                    0.f, 0.f,  0.f,
-                    -1.f, 0.f, -1.f,
-
-                    1.f, 0.f,  1.f,
-                    0.f, 0.f,  0.f,
-                    -1.f, 0.f, -1.f,
-            }));
-    if (layout.GetDataLayout() == armnn::DataLayout::NHWC)
-    {
-        std::vector<T> tmp(kernelData.size());
-        armnnUtils::Permute(kernelDesc.GetShape(), NCHWToNHWC, kernelData.data(), tmp.data());
-        kernelData = tmp;
-    }
-    auto kernel = MakeTensor<T, 4>(kernelDesc, kernelData);
-
-    // Manually calculated.
-    std::vector<T> outputImage(
-        QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(),
-                           outputTensorInfo.GetQuantizationOffset(),
-                           {0.f, 0.f})
-    );
-
-    // Optionally apply bias to output image.
-    if(biasEnabled)
-    {
-        ApplyBias(outputImage, outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(),
-                  biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(),
-                  outputWidth, outputHeight);
-    }
-
-    LayerTestResult<T, 4> ret(outputTensorInfo);
-    if (layout.GetDataLayout() == armnn::DataLayout::NHWC)
-    {
-        std::vector<T> tmp(outputImage.size());
-        armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputImage.data(), tmp.data());
-        outputImage = tmp;
-    }
-
-    ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputImage);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::DepthwiseConvolution2dQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc);
-    armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
-
-    AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]);
-    AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]);
-
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    data.m_Weight = &weightsTensor;
-    data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled.
-    data.m_Parameters.m_StrideX = 1;
-    data.m_Parameters.m_StrideY = 1;
-    data.m_Parameters.m_PadLeft = 0;
-    data.m_Parameters.m_PadRight = 0;
-    data.m_Parameters.m_PadTop = 0;
-    data.m_Parameters.m_PadBottom = 0;
-    data.m_Parameters.m_BiasEnabled = biasEnabled;
-    data.m_Parameters.m_DataLayout = layout.GetDataLayout();
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDepthwiseConvolution2d(data, info);
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-
-    return ret;
-}
-
-template<typename T, typename B>
-LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(armnn::IWorkloadFactory& workloadFactory,
-                                                     float qScale,
-                                                     int32_t qOffset,
-                                                     bool biasEnabled,
-                                                     const armnn::DataLayoutIndexed& layout)
-{
-    unsigned int depthMultiplier = 2;
-
-    unsigned int inputHeight    = 8;
-    unsigned int inputWidth     = 16;
-    unsigned int inputChannels  = 2;
-    unsigned int inputBatchSize = 1;
-
-    unsigned int kernelHeight = 5;
-    unsigned int kernelWidth  = 3;
-
-    unsigned int outputHeight    = inputHeight - kernelHeight + 1 + 2;
-    unsigned int outputWidth     = (inputWidth - kernelWidth + 1)/2;
-    unsigned int outputChannels  = inputChannels * depthMultiplier;
-    unsigned int outputBatchSize = inputBatchSize;
-
-    armnn::TensorInfo inputTensorInfo = GetTensorInfo<T>(
-            inputBatchSize, inputChannels, inputHeight, inputWidth, layout);
-    armnn::TensorInfo outputTensorInfo = GetTensorInfo<T>(
-            outputBatchSize, outputChannels, outputHeight, outputWidth, layout);
-    armnn::TensorInfo kernelDesc = GetTensorInfo<T>(
-            depthMultiplier, inputChannels, kernelHeight, kernelWidth, layout);
-    armnn::TensorInfo biasDesc({outputChannels}, armnn::GetDataType<B>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-        kernelDesc.SetQuantizationScale(qScale);
-        kernelDesc.SetQuantizationOffset(qOffset);
-        biasDesc.SetQuantizationScale(qScale*qScale);
-        biasDesc.SetQuantizationOffset(0);
-    }
-
-    // NOTE: originalInputData is in NCHW format
-    std::vector<T> originalInputData = std::vector<T>(
-            QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), {
-                    0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
-                    0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
-                    0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
-                    0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
-                    0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
-                    0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
-                    0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
-                    0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
-                    0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                    0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                    0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                    0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                    0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                    0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                    0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                    0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-            }));
-    std::vector<T> inputData = originalInputData;
-    // at this point if we require it permute the input data
-    const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
-    if (layout.GetDataLayout() == armnn::DataLayout::NHWC)
-    {
-        armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, originalInputData.data(), inputData.data());
-    }
-    auto input = MakeTensor<T, 4>(inputTensorInfo, inputData);
-
-    std::vector<B> biasV(QuantizedVector<B>(biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(),
-        {0, 2, 1, -1}));
-    auto bias = MakeTensor<B, 1>(biasDesc, biasV);
-
-    std::vector<T> originalKernelData = std::vector<T>(
-            QuantizedVector<T>(kernelDesc.GetQuantizationScale(), kernelDesc.GetQuantizationOffset(), {
-                    1, 1, 1,
-                    1, -1, 1,
-                    1, 1, 1,
-                    1, 1, 1,
-                    1, 1, 1,
-
-                    2, 2, 2,
-                    2, 2, 2,
-                    2, 2, 2,
-                    2, 2, 2,
-                    2, 2, 2,
-
-                    0, 0, 0,
-                    0, -1, 0,
-                    0, 0, 0,
-                    0, 0, 0,
-                    0, 0, 0,
-
-                    0, 0, 0,
-                    0, 0, 0,
-                    0, 1, 0,
-                    0, 0, 0,
-                    0, 0, 0
-            }));
-    std::vector<T> kernelData = originalKernelData;
-    if (layout.GetDataLayout() == armnn::DataLayout::NHWC)
-    {
-        armnnUtils::Permute(kernelDesc.GetShape(), NCHWToNHWC, originalKernelData.data(), kernelData.data());
-    }
-    auto kernel = MakeTensor<T, 4>(kernelDesc, kernelData);
-
-    // Manually calculated.
-    std::vector<T> originalOutputImage = std::vector<T>(
-        QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), {
-            3.5f,  3.5f,  3.5f,  3.5f,  3.5f,  3.5f,  3.5f,
-            6.0f,  6.0f,  6.0f,  6.0f,  6.0f,  6.0f,  6.0f,
-            5.0f,  5.0f,  5.0f,  5.0f,  5.0f,  5.0f,  5.0f,
-            6.5f,  6.5f,  6.5f,  6.5f,  6.5f,  6.5f,  6.5f,
-            6.5f,  6.5f,  6.5f,  6.5f,  6.5f,  6.5f,  6.5f,
-            5.0f,  5.0f,  5.0f,  5.0f,  5.0f,  5.0f,  5.0f,
-
-            -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f,
-            0.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,
-            -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f,
-            -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f,
-            -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f,
-            -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f,
-
-            8.0f,  8.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,
-            10.0f, 10.0f, 0.0f,  0.0f,  0.0f,  0.0f,  0.0f,
-            10.0f, 10.0f, 0.0f,  0.0f,  0.0f,  0.0f,  0.0f,
-            10.0f, 10.0f, 0.0f,  0.0f,  0.0f,  0.0f,  0.0f,
-            10.0f, 10.0f, 0.0f,  0.0f,  0.0f,  0.0f,  0.0f,
-            8.0f,  8.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,
-
-            0.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,
-            0.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,
-            0.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,
-            0.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,
-            0.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,
-            0.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f
-        }));
-
-    // Optionally apply bias to output image.
-    if(biasEnabled)
-    {
-        ApplyBias(originalOutputImage,
-                  outputTensorInfo.GetQuantizationScale(),
-                  outputTensorInfo.GetQuantizationOffset(),
-                  biasV,
-                  biasDesc.GetQuantizationScale(),
-                  biasDesc.GetQuantizationOffset(),
-                  outputWidth,
-                  outputHeight);
-    }
-
-    LayerTestResult<T, 4> ret(outputTensorInfo);
-    std::vector<T> outputImage = originalOutputImage;
-    if (layout.GetDataLayout() == armnn::DataLayout::NHWC)
-    {
-        armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, originalOutputImage.data(), outputImage.data());
-    }
-
-    ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputImage);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::DepthwiseConvolution2dQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc);
-    armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
-
-    AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]);
-    AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]);
-
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    data.m_Weight = &weightsTensor;
-    data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled.
-    data.m_Parameters.m_StrideX = 2;
-    data.m_Parameters.m_StrideY = 1;
-    data.m_Parameters.m_PadLeft = 0;
-    data.m_Parameters.m_PadRight = 0;
-    data.m_Parameters.m_PadTop = 1;
-    data.m_Parameters.m_PadBottom = 1;
-    data.m_Parameters.m_BiasEnabled = biasEnabled;
-    data.m_Parameters.m_DataLayout = layout.GetDataLayout();
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDepthwiseConvolution2d(data, info);
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-
-    return ret;
-}
-
-template<typename T, typename B>
-LayerTestResult<T, 4> DepthwiseConvolution2dNhwcTestImpl(armnn::IWorkloadFactory& workloadFactory,
-                                                         const boost::multi_array<T, 4>& input,
-                                                         const boost::multi_array<T, 4>& kernel,
-                                                         const boost::multi_array<B, 1>& bias,
-                                                         const boost::multi_array<T, 4>& outputExpected,
-                                                         float qScale,
-                                                         int32_t qOffset,
-                                                         uint32_t padLeft = 0,
-                                                         uint32_t padTop = 0,
-                                                         uint32_t padRight = 0,
-                                                         uint32_t padBottom = 0,
-                                                         uint32_t strideX = 1,
-                                                         uint32_t strideY = 1)
-{
-    unsigned int inputNum       = boost::numeric_cast<unsigned int>(input.shape()[0]);
-    unsigned int inputChannels  = boost::numeric_cast<unsigned int>(input.shape()[3]);
-    unsigned int inputHeight    = boost::numeric_cast<unsigned int>(input.shape()[1]);
-    unsigned int inputWidth     = boost::numeric_cast<unsigned int>(input.shape()[2]);
-
-    unsigned int kernelChanMul  = boost::numeric_cast<unsigned int>(kernel.shape()[0]);
-    unsigned int kernelChannels = boost::numeric_cast<unsigned int>(kernel.shape()[3]);
-    unsigned int kernelHeight   = boost::numeric_cast<unsigned int>(kernel.shape()[1]);
-    unsigned int kernelWidth    = boost::numeric_cast<unsigned int>(kernel.shape()[2]);
-
-    unsigned int outputNum      = boost::numeric_cast<unsigned int>(outputExpected.shape()[0]);
-    unsigned int outputChannels = boost::numeric_cast<unsigned int>(outputExpected.shape()[3]);
-    unsigned int outputHeight   = boost::numeric_cast<unsigned int>(outputExpected.shape()[1]);
-    unsigned int outputWidth    = boost::numeric_cast<unsigned int>(outputExpected.shape()[2]);
-
-    // Creates the tensors.
-    armnn::TensorInfo inputTensorInfo({inputNum, inputHeight, inputWidth, inputChannels}, armnn::GetDataType<T>());
-    armnn::TensorInfo outputTensorInfo({outputNum, outputHeight, outputWidth, outputChannels},
-                                       armnn::GetDataType<T>());
-    armnn::TensorInfo kernelDesc({kernelChanMul, kernelHeight, kernelWidth, kernelChannels}, armnn::GetDataType<T>());
-    armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, armnn::GetDataType<B>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if (armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-        kernelDesc.SetQuantizationScale(qScale);
-        kernelDesc.SetQuantizationOffset(qOffset);
-        biasDesc.SetQuantizationScale(qScale*qScale);
-        biasDesc.SetQuantizationOffset(0);
-    }
-
-    // Construct the input data.
-    std::vector<T> inputData;
-    inputData.assign(input.data(), input.data() + inputHeight*inputWidth*inputChannels);
-    auto batchedInput = MakeTensor<T, 4>(inputTensorInfo, inputData);
-
-    // Construct the output data, with bias applied, as appropriate.
-    std::vector<T> outputData;
-    outputData.assign(outputExpected.data(), outputExpected.data() + outputHeight*outputWidth*outputChannels);
-
-    LayerTestResult<T, 4> ret(outputTensorInfo);
-    ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc);
-    AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]);
-
-    armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
-
-    armnn::DepthwiseConvolution2dQueueDescriptor data;
-    data.m_Weight = &weightsTensor;
-    data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled - it can be a source of bugs.
-    data.m_Parameters.m_StrideX = strideX;
-    data.m_Parameters.m_StrideY = strideY;
-    data.m_Parameters.m_PadLeft = padLeft;
-    data.m_Parameters.m_PadRight = padRight;
-    data.m_Parameters.m_PadTop = padTop;
-    data.m_Parameters.m_PadBottom = padBottom;
-    data.m_Parameters.m_DataLayout = armnn::DataLayout::NHWC;
-
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDepthwiseConvolution2d(data, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &batchedInput[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-
-    return ret;
-}
-
-template<typename T>
-LayerTestResult<T,4> Convolution1dTestImpl(armnn::IWorkloadFactory& workloadFactory,
-                                           float qScale,
-                                           int32_t qOffset,
-                                           bool biasEnabled)
-{
-    using B = typename FullyConnectedBiasTypeForInputType<T>::Type;
-
-    // Until we have a specialist 1D convolution layer, we can fake one using
-    // 2D convolution with the final dimension set to 1.
-    // I don't anticipate this being particularly slow, given that convolution is implemented
-    // as a matrix multiplication, at which point dimension doesn't matter.
-
-    unsigned int batchSize      = 1;
-    unsigned int inputChannels  = 2;
-    unsigned int outputChannels = 3;
-    unsigned int inputSize      = 5; // The 1D size (could view as 'width' or 'height').
-    unsigned int kernelSize     = 3;
-    unsigned int padSize        = 2;
-    unsigned int stride         = 1;
-    unsigned int outputSize     = 7; // (inputSize + 2 * padSize - kernelSize + 1) / stride.
-
-    armnn::TensorInfo inputInfo({batchSize, inputChannels, inputSize, 1}, armnn::GetDataType<T>());
-    armnn::TensorInfo outputInfo({batchSize, outputChannels, outputSize, 1}, armnn::GetDataType<T>());
-    armnn::TensorInfo kernelInfo({outputChannels, inputChannels, kernelSize, 1}, armnn::GetDataType<T>());
-    armnn::TensorInfo biasInfo({outputChannels}, armnn::GetDataType<B>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputInfo.SetQuantizationScale(qScale);
-        inputInfo.SetQuantizationOffset(qOffset);
-        outputInfo.SetQuantizationScale(qScale);
-        outputInfo.SetQuantizationOffset(qOffset);
-        kernelInfo.SetQuantizationScale(qScale);
-        kernelInfo.SetQuantizationOffset(qOffset);
-        biasInfo.SetQuantizationScale(inputInfo.GetQuantizationScale()*kernelInfo.GetQuantizationScale());
-        biasInfo.SetQuantizationOffset(0);
-    }
-
-    std::vector<T> inputData(
-        QuantizedVector<T>(inputInfo.GetQuantizationScale(), inputInfo.GetQuantizationOffset(), {
-            5.0f, -2.0f, 2.5f, 0.0f, 1.0f,
-            -3.0f, 3.2f, 5.0f, 2.0f, 3.0f,
-        }));
-
-    std::vector<T> kernelData(
-        QuantizedVector<T>(kernelInfo.GetQuantizationScale(), kernelInfo.GetQuantizationOffset(), {
-            1.0f, 0.0f, 0.0f,
-            0.0f, 2.0f, -1.5f,
-
-            0.0f, 0.0f, 0.0f,
-            0.2f, 0.2f, 0.2f,
-
-            0.5f, 0.0f, 0.5f,
-            0.0f, -1.0f, 0.0f
-        }));
-
-    std::vector<B> biasData(
-        QuantizedVector<B>(biasInfo.GetQuantizationScale(), biasInfo.GetQuantizationOffset(), {
-            1.0f, 0.0f, 0.0f
-        }));
-
-    std::vector<T> outputData(
-        QuantizedVector<T>(outputInfo.GetQuantizationScale(), outputInfo.GetQuantizationOffset(), {
-            4.5f, -10.8f, 5.0f + 6.4f - 7.5f, -2.0f + 10.0f -3.0f, 2.5f + 4.0f - 4.5f, 6.0f, 1.0f,
-            -0.6f, -0.6f + 0.64f, -0.6f + 0.64f + 1.0f, 0.64f + 1.0f + 0.4f, 1.0f + 0.4f + 0.6f, 0.4f + 0.6f, 0.6f,
-            2.5f, -1.0f + 3.0f, 1.25f - 3.2f + 2.5f, -1.0f - 5.0f, 1.25f + 0.5f - 2.0f, -3.0f, 0.5f
-        }));
-
-    // Optionally apply bias to output image.
-    if(biasEnabled)
-    {
-        ApplyBias(outputData, outputInfo.GetQuantizationScale(), outputInfo.GetQuantizationOffset(),
-            biasData, biasInfo.GetQuantizationScale(), biasInfo.GetQuantizationOffset(),
-            1, outputSize);
-    }
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle  = workloadFactory.CreateTensorHandle(inputInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputInfo);
-
-    armnn::Convolution2dQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    armnn::ScopedCpuTensorHandle         weightsTensor(kernelInfo);
-    armnn::ScopedCpuTensorHandle         biasTensor(biasInfo);
-
-    AllocateAndCopyDataToITensorHandle(&weightsTensor, kernelData.data());
-    AllocateAndCopyDataToITensorHandle(&biasTensor, biasData.data());
-
-    AddInputToWorkload(data, info, inputInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputInfo, outputHandle.get());
-
-    data.m_Weight         = &weightsTensor;
-    data.m_Bias           = &biasTensor;
-    data.m_Parameters.m_StrideX        = 1;
-    data.m_Parameters.m_StrideY        = stride;
-    data.m_Parameters.m_PadLeft        = 0;
-    data.m_Parameters.m_PadRight       = 0;
-    data.m_Parameters.m_PadTop         = padSize;
-    data.m_Parameters.m_PadBottom      = padSize;
-    data.m_Parameters.m_BiasEnabled    = biasEnabled;
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConvolution2d(data, info);
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), inputData.data());
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    // Output
-    LayerTestResult<T,4> ret(outputInfo);
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-    ret.outputExpected = MakeTensor<T, 4>(outputInfo, outputData);
-    return ret;
-}
-
-
-
-template<typename T>
-LayerTestResult<T,4> CompareConvolution2dTestImpl(armnn::IWorkloadFactory& workloadFactory,
-                                                armnn::IWorkloadFactory& refWorkloadFactory)
-{
-    unsigned int inputHeight   = 8;
-    unsigned int inputWidth    = 16;
-    unsigned int inputChannels = 3;
-    unsigned int inputNum      = 5;
-
-    unsigned int kernelHeight = 3;
-    unsigned int kernelWidth  = 3;
-
-    unsigned int strideX = 2;
-    unsigned int strideY = 3;
-    unsigned int padX    = 1;
-    unsigned int padY    = 1;
-
-    unsigned int outputNum      = inputNum;
-    unsigned int outputChannels = 2;
-    unsigned int outputHeight   = (inputHeight + 2 * padY - kernelHeight + strideY) / strideY;
-    unsigned int outputWidth    = (inputWidth + 2 * padX - kernelWidth + strideX) / strideX;
-
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-    armnn::TensorInfo kernelDesc;
-    armnn::TensorInfo biasDesc;
-
-    unsigned int inputShape[]    = {inputNum, inputChannels, inputHeight, inputWidth};
-    unsigned int outputShape[]   = {outputNum, outputChannels, outputHeight, outputWidth};
-    unsigned int kernelShape[]   = {outputChannels, inputChannels, kernelHeight, kernelWidth};
-    unsigned int biasShape[]     = {outputChannels};
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::GetDataType<T>());
-    outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::GetDataType<T>());
-    kernelDesc = armnn::TensorInfo(4, kernelShape, armnn::GetDataType<T>());
-    biasDesc = armnn::TensorInfo(1, biasShape, armnn::GetDataType<T>());
-
-    LayerTestResult<T,4> ret(outputTensorInfo);
-
-    auto input  = MakeRandomTensor<T, 4>(inputTensorInfo, 124908);
-    auto kernel = MakeRandomTensor<T, 4>(kernelDesc, 891234);
-    auto bias   = MakeRandomTensor<T, 1>(biasDesc, 1028);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::Convolution2dQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc);
-    armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
-
-    AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]);
-    AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]);
-
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-    data.m_Weight = &weightsTensor;
-    data.m_Bias = &biasTensor;
-    data.m_Parameters.m_StrideX = strideX;
-    data.m_Parameters.m_StrideY = strideY;
-    data.m_Parameters.m_PadLeft = padX;
-    data.m_Parameters.m_PadRight = padX;
-    data.m_Parameters.m_PadTop = padY;
-    data.m_Parameters.m_PadBottom = padY;
-    data.m_Parameters.m_BiasEnabled = true;
-
-    std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo);
-
-    armnn::Convolution2dQueueDescriptor refData = data;
-    armnn::WorkloadInfo               refInfo = info;
-    SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get());
-    SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());
-
-    std::unique_ptr<armnn::IWorkload> workload  = workloadFactory.CreateConvolution2d(data, info);
-    std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateConvolution2d(refData, refInfo);
-
-    outputHandleRef->Allocate();
-    inputHandleRef->Allocate();
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-    CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-    refWorkloadFactory.Finalize();
-    workloadRef->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-    CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get());
-
-    return ret;
-}
-
-template<typename T>
-LayerTestResult<T, 4> CompareDepthwiseConvolution2dTestImpl(armnn::IWorkloadFactory& workloadFactory,
-                                                            armnn::IWorkloadFactory& refWorkloadFactory,
-                                                            const armnn::DataLayoutIndexed& layout)
-{
-    unsigned int inputHeight = 8;
-    unsigned int inputWidth = 16;
-    unsigned int inputChannels = 3;
-    unsigned int inputNum = 5;
-
-    unsigned int kernelHeight = 3;
-    unsigned int kernelWidth = 3;
-    unsigned int channelMultiplier = 1;
-
-    unsigned int strideX = 2;
-    unsigned int strideY = 3;
-    unsigned int padX = 1;
-    unsigned int padY = 1;
-
-    unsigned int outputNum = inputNum;
-    unsigned int outputChannels = inputChannels * channelMultiplier;
-    unsigned int outputHeight = (inputHeight + 2 * padY - kernelHeight + strideY) / strideY;
-    unsigned int outputWidth = (inputWidth + 2 * padX - kernelWidth + strideX) / strideX;
-
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-    armnn::TensorInfo kernelDesc;
-    armnn::TensorInfo biasDesc;
-
-
-    std::vector<unsigned int> inputShape;
-    std::vector<unsigned int> outputShape;
-    std::vector<unsigned int> kernelShape;
-    std::vector<unsigned int> biasShape= { outputChannels };
-    switch (layout.GetDataLayout())
-    {
-        case armnn::DataLayout::NCHW:
-            inputShape =  { inputNum, inputChannels, inputHeight, inputWidth };
-            outputShape = { outputNum, outputChannels, outputHeight, outputWidth };
-            kernelShape = { channelMultiplier, inputChannels, kernelHeight, kernelWidth };
-            break;
-        case armnn::DataLayout ::NHWC:
-            inputShape =  { inputNum, inputHeight, inputWidth, inputChannels };
-            outputShape = { outputNum, outputHeight, outputWidth, outputChannels };
-            kernelShape = { channelMultiplier, kernelHeight, kernelWidth, inputChannels };
-            break;
-        default:
-            throw armnn::InvalidArgumentException("unknown data layout ["
-                                                  + std::to_string(static_cast<int>(layout.GetDataLayout())) + "]");
-    }
-
-    float inputsQScale = armnn::IsQuantizedType<T>() ? 1.0f : 0;
-    float outputQScale = armnn::IsQuantizedType<T>() ? 2.0f : 0;
-    int32_t qOffset = 0;
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape.data(), armnn::GetDataType<T>(), inputsQScale, qOffset);
-    outputTensorInfo = armnn::TensorInfo(4, outputShape.data(), armnn::GetDataType<T>(), outputQScale, qOffset);
-    kernelDesc = armnn::TensorInfo(4, kernelShape.data(), armnn::GetDataType<T>(), inputsQScale, qOffset);
-    biasDesc = armnn::TensorInfo(
-            1, biasShape.data(), armnn::GetBiasDataType(armnn::GetDataType<T>()), inputsQScale, qOffset);
-
-    LayerTestResult<T, 4> ret(outputTensorInfo);
-
-    auto input = MakeRandomTensor<T, 4>(inputTensorInfo, 124908, 0.0f, 255.0f);
-    auto kernel = MakeRandomTensor<T, 4>(kernelDesc, 891234, 0.0f, 255.0f);
-    auto bias = MakeRandomTensor<typename FullyConnectedBiasTypeForInputType<T>::Type, 1>(
-            biasDesc, 1028, 0.0f, 255.0f);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::DepthwiseConvolution2dQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc);
-    armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
-
-    AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]);
-    AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]);
-
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-    data.m_Weight = &weightsTensor;
-    data.m_Bias = &biasTensor;
-    data.m_Parameters.m_StrideX = strideX;
-    data.m_Parameters.m_StrideY = strideY;
-    data.m_Parameters.m_PadLeft = padX;
-    data.m_Parameters.m_PadRight = padX;
-    data.m_Parameters.m_PadTop = padY;
-    data.m_Parameters.m_PadBottom = padY;
-    data.m_Parameters.m_BiasEnabled = true;
-    data.m_Parameters.m_DataLayout = layout.GetDataLayout();
-
-    std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo);
-
-    armnn::DepthwiseConvolution2dQueueDescriptor refData = data;
-    armnn::WorkloadInfo refInfo = info;
-    SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get());
-    SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDepthwiseConvolution2d(data, info);
-    std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateDepthwiseConvolution2d(refData, refInfo);
-
-    outputHandleRef->Allocate();
-    inputHandleRef->Allocate();
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-    CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-    refWorkloadFactory.Finalize();
-    workloadRef->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-    CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get());
-
-    return ret;
-}
diff --git a/src/backends/test/ConvertFp16ToFp32TestImpl.hpp b/src/backends/test/ConvertFp16ToFp32TestImpl.hpp
deleted file mode 100644
index 2455e9691a..0000000000
--- a/src/backends/test/ConvertFp16ToFp32TestImpl.hpp
+++ /dev/null
@@ -1,54 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#pragma once
-
-#include <armnn/ArmNN.hpp>
-#include <armnn/Tensor.hpp>
-#include <armnn/TypesUtils.hpp>
-#include <armnnUtils/Half.hpp>
-
-#include <backends/CpuTensorHandle.hpp>
-
-#include <test/TensorHelpers.hpp>
-
-
-LayerTestResult<float, 4> SimpleConvertFp16ToFp32Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    using namespace half_float::literal;
-
-    const armnn::TensorInfo inputTensorInfo({1, 3, 2, 3}, armnn::DataType::Float16);
-    const armnn::TensorInfo outputTensorInfo({1, 3, 2, 3}, armnn::DataType::Float32);
-
-    auto input = MakeTensor<armnn::Half, 4>(inputTensorInfo,
-        { -37.5_h, -15.2_h, -8.76_h, -2.0_h, -1.5_h, -1.3_h, -0.5_h, -0.4_h, 0.0_h,
-          1.0_h, 0.4_h, 0.5_h, 1.3_h, 1.5_h, 2.0_h, 8.76_h, 15.2_h, 37.5_h });
-
-    LayerTestResult<float, 4> ret(outputTensorInfo);
-    ret.outputExpected = MakeTensor<float, 4>(outputTensorInfo,
-        { -37.5f, -15.2f, -8.76f, -2.0f, -1.5f, -1.3f, -0.5f, -0.4f, 0.0f,
-          1.0f, 0.4f, 0.5f, 1.3f, 1.5f, 2.0f, 8.76f, 15.2f, 37.5f });
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ConvertFp16ToFp32QueueDescriptor data;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConvertFp16ToFp32(data, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-
-    return ret;
-}
diff --git a/src/backends/test/ConvertFp32ToFp16TestImpl.hpp b/src/backends/test/ConvertFp32ToFp16TestImpl.hpp
deleted file mode 100644
index 4eee274357..0000000000
--- a/src/backends/test/ConvertFp32ToFp16TestImpl.hpp
+++ /dev/null
@@ -1,54 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#pragma once
-
-#include <armnn/ArmNN.hpp>
-#include <armnn/Tensor.hpp>
-#include <armnn/TypesUtils.hpp>
-#include <armnnUtils/Half.hpp>
-
-#include <backends/CpuTensorHandle.hpp>
-
-#include <test/TensorHelpers.hpp>
-
-
-LayerTestResult<armnn::Half, 4> SimpleConvertFp32ToFp16Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    using namespace half_float::literal;
-
-    const armnn::TensorInfo inputTensorInfo({1, 3, 2, 3}, armnn::DataType::Float32);
-    const armnn::TensorInfo outputTensorInfo({1, 3, 2, 3}, armnn::DataType::Float16);
-
-    auto input = MakeTensor<float, 4>(inputTensorInfo,
-        { -37.5f, -15.2f, -8.76f, -2.0f, -1.5f, -1.3f, -0.5f, -0.4f, 0.0f,
-          1.0f, 0.4f, 0.5f, 1.3f, 1.5f, 2.0f, 8.76f, 15.2f, 37.5f });
-
-    LayerTestResult<armnn::Half, 4> ret(outputTensorInfo);
-    ret.outputExpected = MakeTensor<armnn::Half, 4>(outputTensorInfo,
-        { -37.5_h, -15.2_h, -8.76_h, -2.0_h, -1.5_h, -1.3_h, -0.5_h, -0.4_h, 0.0_h,
-          1.0_h, 0.4_h, 0.5_h, 1.3_h, 1.5_h, 2.0_h, 8.76_h, 15.2_h, 37.5_h });
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ConvertFp32ToFp16QueueDescriptor data;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConvertFp32ToFp16(data, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-
-    return ret;
-}
diff --git a/src/backends/test/EndToEndTestImpl.hpp b/src/backends/test/EndToEndTestImpl.hpp
deleted file mode 100644
index 5f17f782f3..0000000000
--- a/src/backends/test/EndToEndTestImpl.hpp
+++ /dev/null
@@ -1,102 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#pragma once
-
-#include <armnn/ArmNN.hpp>
-
-#include <backends/test/QuantizeHelper.hpp>
-
-#include <vector>
-
-namespace
-{
-
-using namespace armnn;
-
-template<typename T>
-bool ConstantUsageTest(const std::vector<BackendId>& computeDevice,
-                       const TensorInfo& commonTensorInfo,
-                       const std::vector<T>& inputData,
-                       const std::vector<T>& constantData,
-                       const std::vector<T>& expectedOutputData)
-{
-    // Create runtime in which test will run
-    IRuntime::CreationOptions options;
-    IRuntimePtr runtime(IRuntime::Create(options));
-
-    // Builds up the structure of the network.
-    INetworkPtr net(INetwork::Create());
-
-    IConnectableLayer* input = net->AddInputLayer(0);
-    IConnectableLayer* constant = net->AddConstantLayer(ConstTensor(commonTensorInfo, constantData));
-    IConnectableLayer* add = net->AddAdditionLayer();
-    IConnectableLayer* output = net->AddOutputLayer(0);
-
-    input->GetOutputSlot(0).Connect(add->GetInputSlot(0));
-    constant->GetOutputSlot(0).Connect(add->GetInputSlot(1));
-    add->GetOutputSlot(0).Connect(output->GetInputSlot(0));
-
-    // Sets the tensors in the network.
-    input->GetOutputSlot(0).SetTensorInfo(commonTensorInfo);
-    constant->GetOutputSlot(0).SetTensorInfo(commonTensorInfo);
-    add->GetOutputSlot(0).SetTensorInfo(commonTensorInfo);
-
-    // optimize the network
-    IOptimizedNetworkPtr optNet = Optimize(*net, computeDevice, runtime->GetDeviceSpec());
-
-    // Loads it into the runtime.
-    NetworkId netId;
-    runtime->LoadNetwork(netId, std::move(optNet));
-
-    // Creates structures for input & output.
-    std::vector<T> outputData(inputData.size());
-
-    InputTensors inputTensors
-    {
-        {0, ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data())}
-    };
-    OutputTensors outputTensors
-    {
-        {0, Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())}
-    };
-
-    // Does the inference.
-    runtime->EnqueueWorkload(netId, inputTensors, outputTensors);
-
-    // Checks the results.
-    return outputData == expectedOutputData;
-}
-
-inline bool ConstantUsageFloat32Test(const std::vector<BackendId>& backends)
-{
-    const TensorInfo commonTensorInfo({ 2, 3 }, DataType::Float32);
-
-    return ConstantUsageTest(backends,
-        commonTensorInfo,
-        std::vector<float>{ 1.f, 2.f, 3.f, 4.f, 5.f, 6.f }, // Input.
-        std::vector<float>{ 6.f, 5.f, 4.f, 3.f, 2.f, 1.f }, // Const input.
-        std::vector<float>{ 7.f, 7.f, 7.f, 7.f, 7.f, 7.f }  // Expected output.
-    );
-}
-
-inline bool ConstantUsageUint8Test(const std::vector<BackendId>& backends)
-{
-    TensorInfo commonTensorInfo({ 2, 3 }, DataType::QuantisedAsymm8);
-
-    const float scale = 0.023529f;
-    const int8_t offset = -43;
-
-    commonTensorInfo.SetQuantizationScale(scale);
-    commonTensorInfo.SetQuantizationOffset(offset);
-
-    return ConstantUsageTest(backends,
-        commonTensorInfo,
-        QuantizedVector<uint8_t>(scale, offset, { 1.f, 2.f, 3.f, 4.f, 5.f, 6.f }), // Input.
-        QuantizedVector<uint8_t>(scale, offset, { 6.f, 5.f, 4.f, 3.f, 2.f, 1.f }), // Const input.
-        QuantizedVector<uint8_t>(scale, offset, { 7.f, 7.f, 7.f, 7.f, 7.f, 7.f })  // Expected output.
-    );
-}
-
-} // anonymous namespace
\ No newline at end of file
diff --git a/src/backends/test/FullyConnectedTestImpl.hpp b/src/backends/test/FullyConnectedTestImpl.hpp
deleted file mode 100644
index 125b7e62b1..0000000000
--- a/src/backends/test/FullyConnectedTestImpl.hpp
+++ /dev/null
@@ -1,287 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-template<typename T, typename B>
-LayerTestResult<T, 2> SimpleFullyConnectedTestImpl(
-    armnn::IWorkloadFactory& workloadFactory,
-    armnn::TensorInfo inputTensorInfo,
-    armnn::TensorInfo outputTensorInfo,
-    armnn::TensorInfo weightsDesc,
-    armnn::TensorInfo biasesDesc,
-    boost::multi_array<T, 2>& weights,
-    boost::multi_array<B, 1>& bias,
-    boost::multi_array<T, 4>& input,
-    bool biasEnabled,
-    bool transposeWeights)
-{
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::FullyConnectedQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    armnn::ScopedCpuTensorHandle weightsTensor(weightsDesc);
-    armnn::ScopedCpuTensorHandle biasTensor(biasesDesc);
-
-    AllocateAndCopyDataToITensorHandle(&weightsTensor, &weights[0][0]);
-    AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]);
-
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-    data.m_Weight = &weightsTensor;
-    data.m_Bias = &biasTensor;
-    data.m_Parameters.m_BiasEnabled = biasEnabled;
-    data.m_Parameters.m_TransposeWeightMatrix = transposeWeights;
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateFullyConnected(data, info);
-    LayerTestResult<T, 2> result(outputTensorInfo);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0], outputHandle.get());
-
-    return result;
-}
-
-LayerTestResult<float, 2> FullyConnectedFloat32Test(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled,
-    bool transposeWeights)
-{
-    unsigned int inputWidth = 1;
-    unsigned int inputHeight = 1;
-    unsigned int inputChannels = 5;
-    unsigned int inputNum = 2;
-
-    unsigned int outputChannels = 3;
-    unsigned int outputNum = 2;
-
-    // Define the tensor descriptors.
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-    armnn::TensorInfo weightsDesc;
-    armnn::TensorInfo biasesDesc;
-
-    unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth };
-    unsigned int outputShape[] = { outputNum, outputChannels };
-    unsigned int weightsShape[] = { inputChannels, outputChannels };
-    if (transposeWeights)
-    {
-        std::swap(weightsShape[0], weightsShape[1]);
-    }
-    unsigned int biasShape[] = { outputChannels };
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
-    outputTensorInfo = armnn::TensorInfo(2, outputShape, armnn::DataType::Float32);
-    weightsDesc = armnn::TensorInfo(2, weightsShape, armnn::DataType::Float32);
-    biasesDesc = armnn::TensorInfo(1, biasShape, armnn::DataType::Float32);
-
-    LayerTestResult<float, 2> result(outputTensorInfo);
-
-    boost::multi_array<float, 4> input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>(
-        {
-            1.0f, 2.0f, 3.0f, 4.0f, 5.0f,
-
-            5.0f, 4.0f, 3.0f, 2.0f, 1.0f
-        })
-    );
-
-    boost::multi_array<float, 2> weights = MakeTensor<float, 2>(weightsDesc, std::vector<float>(
-        {
-            .5f, 2.f, .5f,
-            .5f, 2.f, 1.f,
-            .5f, 2.f, 2.f,
-            .5f, 2.f, 3.f,
-            .5f, 2.f, 4.f
-        }));
-
-    if (transposeWeights)
-    {
-        weights = MakeTensor<float, 2>(weightsDesc, std::vector<float>(
-        {
-            .5f, .5f, .5f, .5f, .5f,
-            2.f, 2.f, 2.f, 2.f, 2.f,
-            .5f, 1.f, 2.f, 3.f, 4.f
-        }));
-    }
-
-
-    std::vector<float> biasValues({0.f, 0.f, 0.f});
-    if (biasEnabled)
-    {
-        biasValues =  std::vector<float>({10.f, 20.f, 30.f});
-    }
-    boost::multi_array<float, 1> bias = MakeTensor<float, 1>(biasesDesc, biasValues);
-
-    result = SimpleFullyConnectedTestImpl<float>(
-        workloadFactory,
-        inputTensorInfo, outputTensorInfo,
-        weightsDesc, biasesDesc,
-        weights, bias, input,
-        biasEnabled, transposeWeights
-    );
-
-    result.outputExpected = MakeTensor<float, 2>(outputTensorInfo, std::vector<float>(
-        {
-            0.5f + 1.0f + 1.5f + 2.0f + 2.5f + biasValues[0],
-            2.0f + 4.0f + 6.0f + 8.0f + 10.f + biasValues[1],
-            0.5f + 2.0f + 6.0f + 12.f + 20.f + biasValues[2],
-
-            2.5f + 2.0f + 1.5f + 1.0f + 0.5f + biasValues[0],
-            10.0f + 8.0f + 6.0f + 4.0f + 2.f + biasValues[1],
-            2.5f + 4.0f + 6.0f + 6.f + 4.f   + biasValues[2]
-        })
-    );
-
-    return result;
-}
-
-LayerTestResult<uint8_t, 2> FullyConnectedUint8Test(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled)
-{
-    constexpr static unsigned int inputWidth = 3u;
-    constexpr static unsigned int inputHeight = 2u;
-    constexpr static unsigned int inputChannels = 1u;
-
-    constexpr static unsigned int inputSize = inputWidth * inputHeight * inputChannels;
-
-    constexpr static unsigned int outputChannels = 2u;
-
-    armnn::TensorInfo inputTensorInfo({ 1, inputChannels, inputHeight, inputWidth }, armnn::DataType::QuantisedAsymm8);
-    inputTensorInfo.SetQuantizationScale(0.1f);
-    inputTensorInfo.SetQuantizationOffset(63);
-
-    armnn::TensorInfo outputTensorInfo({ 1, outputChannels }, armnn::DataType::QuantisedAsymm8);
-    outputTensorInfo.SetQuantizationScale(5.f);
-    outputTensorInfo.SetQuantizationOffset(biasEnabled ? -50 : 10);
-
-    armnn::TensorInfo weightsDesc({ outputChannels, inputSize }, armnn::DataType::QuantisedAsymm8);
-    weightsDesc.SetQuantizationScale(0.2f);
-    weightsDesc.SetQuantizationOffset(93);
-
-    armnn::TensorInfo biasesDesc({ outputChannels }, armnn::DataType::Signed32);
-    biasesDesc.SetQuantizationScale(inputTensorInfo.GetQuantizationScale() * weightsDesc.GetQuantizationScale());
-    biasesDesc.SetQuantizationOffset(0);
-
-    LayerTestResult<uint8_t, 2> result(outputTensorInfo);
-
-    auto input = MakeTensor<uint8_t, 4>(inputTensorInfo, std::vector<uint8_t>{51, 124, 28,
-        251, 8, 92});
-
-    auto weights = MakeTensor<uint8_t, 2>(weightsDesc, std::vector<uint8_t>{51, 193, 42, 53, 175, 34,
-        210, 145, 23, 74, 34, 150});
-
-        // scale = 0.02
-        // offset = 0
-    auto bias = MakeTensor<int32_t, 1>(biasesDesc, std::vector<int32_t>{9250, 67500});
-
-    result = SimpleFullyConnectedTestImpl<uint8_t>(
-        workloadFactory,
-        inputTensorInfo, outputTensorInfo,
-        weightsDesc, biasesDesc,
-        weights, bias, input,
-        biasEnabled, true
-    );
-
-    // Manually calculated.
-    // Note one of these values has been clamped to 0.
-    if (biasEnabled)
-    {
-        result.outputExpected = MakeTensor<uint8_t, 2>(outputTensorInfo, std::vector<uint8_t>{0, 242});
-    }
-    else
-    {
-        result.outputExpected = MakeTensor<uint8_t, 2>(outputTensorInfo, std::vector<uint8_t>{0, 32});
-    }
-
-    return result;
-}
-
-
-
-//
-// ArmNN variant of the AndroidNN fully_connected_float_large test.
-//
-// Tests the fully connected layer with large values, optionally transposing weights.
-// Note this is templated for consistency, but the nature of this tests makes it unlikely to be useful in Uint8 mode.
-//
-template<typename T>
-LayerTestResult<T, 2> FullyConnectedLargeTestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                        bool transposeWeights,
-                                                        float qScale = 0.0f,
-                                                        int32_t qOffset = 0)
-{
-    unsigned int inputWidth = 1;
-    unsigned int inputHeight = 1;
-    unsigned int inputChannels = 5;
-    unsigned int inputNum = 1;
-
-    unsigned int outputChannels = 1;
-    unsigned int outputNum = 1;
-
-    // Define the tensor descriptors.
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-    armnn::TensorInfo weightsDesc;
-    armnn::TensorInfo biasesDesc;
-
-    unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth };
-    unsigned int outputShape[] = { outputNum, outputChannels };
-    unsigned int weightsShape[] = { inputChannels, outputChannels };
-    if (transposeWeights)
-    {
-        std::swap(weightsShape[0], weightsShape[1]);
-    }
-
-    unsigned int biasShape[] = { outputChannels };
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::GetDataType<T>());
-    outputTensorInfo = armnn::TensorInfo(2, outputShape, armnn::GetDataType<T>());
-    weightsDesc = armnn::TensorInfo(2, weightsShape, armnn::GetDataType<T>());
-    biasesDesc = armnn::TensorInfo(1, biasShape, armnn::GetDataType<T>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    LayerTestResult<T, 2> result(outputTensorInfo);
-
-    boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-            1.0f, 10.0f, 100.0f, 1000.0f, 10000.0f,
-        })
-    );
-
-    boost::multi_array<T, 2> weights = MakeTensor<T, 2>(weightsDesc,
-        QuantizedVector<T>(qScale, qOffset, {
-            2.0f, 3.0f, 4.0f, 5.0f, 6.0f
-        })
-    );
-
-    std::vector<T> biasValues({900000.f});
-    boost::multi_array<T, 1> bias = MakeTensor<T, 1>(biasesDesc, biasValues);
-
-    result = SimpleFullyConnectedTestImpl<T>(
-        workloadFactory,
-        inputTensorInfo, outputTensorInfo,
-        weightsDesc, biasesDesc,
-        weights, bias, input,
-        true, transposeWeights
-    );
-
-    result.outputExpected = MakeTensor<T, 2>(outputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-            965432.0f,
-        })
-    );
-
-    return result;
-}
diff --git a/src/backends/test/IsLayerSupportedTestImpl.hpp b/src/backends/test/IsLayerSupportedTestImpl.hpp
deleted file mode 100644
index 722d82d8ab..0000000000
--- a/src/backends/test/IsLayerSupportedTestImpl.hpp
+++ /dev/null
@@ -1,563 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#pragma once
-
-#include "Graph.hpp"
-
-#include <boost/core/ignore_unused.hpp>
-
-namespace
-{
-armnn::Graph dummyGraph;
-
-// Make a dummy TensorInfo object.
-template<armnn::DataType DataType>
-armnn::TensorInfo MakeDummyTensorInfo()
-{
-    return armnn::TensorInfo({2,2,2,2}, DataType);
-}
-
-
-// Make a dummy WorkloadInfo using a dummy TensorInfo.
-template<armnn::DataType DataType>
-armnn::WorkloadInfo MakeDummyWorkloadInfo(unsigned int numInputs, unsigned int numOutputs)
-{
-    armnn::WorkloadInfo info;
-    for (unsigned int i=0; i < numInputs; i++)
-    {
-        info.m_InputTensorInfos.push_back(MakeDummyTensorInfo<DataType>());
-    }
-    for (unsigned int o=0; o < numOutputs; o++)
-    {
-        info.m_OutputTensorInfos.push_back(MakeDummyTensorInfo<DataType>());
-    }
-    return info;
-}
-
-// Template class to create a dummy layer (2 parameters).
-template<typename LayerType, typename DescType = typename LayerType::DescriptorType>
-struct DummyLayer
-{
-    DummyLayer()
-    {
-        m_Layer = dummyGraph.AddLayer<LayerType>(DescType(), "");
-    }
-    ~DummyLayer()
-    {
-        dummyGraph.EraseLayer(m_Layer);
-    }
-    LayerType* m_Layer;
-};
-
-// Template class to create a dummy layer (1 parameter).
-template<typename LayerType>
-struct DummyLayer<LayerType, void>
-{
-    DummyLayer()
-    {
-        m_Layer = dummyGraph.AddLayer<LayerType>("");
-    }
-    ~DummyLayer()
-    {
-        dummyGraph.EraseLayer(m_Layer);
-    }
-    LayerType* m_Layer;
-};
-
-template<>
-struct DummyLayer<armnn::BatchNormalizationLayer>
-{
-    DummyLayer()
-    {
-        m_Layer = dummyGraph.AddLayer<armnn::BatchNormalizationLayer>(armnn::BatchNormalizationDescriptor(), "");
-        m_Layer->m_Mean = std::make_unique<armnn::ScopedCpuTensorHandle>(
-            armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
-        m_Layer->m_Variance = std::make_unique<armnn::ScopedCpuTensorHandle>(
-            armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
-        m_Layer->m_Beta = std::make_unique<armnn::ScopedCpuTensorHandle>(
-            armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
-        m_Layer->m_Gamma = std::make_unique<armnn::ScopedCpuTensorHandle>(
-            armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
-    }
-    ~DummyLayer()
-    {
-        dummyGraph.EraseLayer(m_Layer);
-    }
-    armnn::BatchNormalizationLayer* m_Layer;
-
-};
-
-template<>
-struct DummyLayer<armnn::ConstantLayer, void>
-{
-    DummyLayer()
-    {
-        m_Layer = dummyGraph.AddLayer<armnn::ConstantLayer>("");
-    }
-    ~DummyLayer()
-    {
-        dummyGraph.EraseLayer(m_Layer);
-    }
-    armnn::ConstantLayer* m_Layer;
-};
-
-template<>
-struct DummyLayer<armnn::InputLayer, armnn::LayerBindingId>
-{
-    DummyLayer()
-    {
-        m_Layer = dummyGraph.AddLayer<armnn::InputLayer>(armnn::LayerBindingId(), "");
-
-    }
-    ~DummyLayer()
-    {
-        dummyGraph.EraseLayer(m_Layer);
-    }
-    armnn::InputLayer* m_Layer;
-};
-
-template<>
-struct DummyLayer<armnn::MergerLayer>
-{
-    DummyLayer()
-    {
-        armnn::OriginsDescriptor desc(2);
-        m_Layer = dummyGraph.AddLayer<armnn::MergerLayer>(desc, "");
-
-    }
-    ~DummyLayer()
-    {
-        dummyGraph.EraseLayer(m_Layer);
-    }
-    armnn::MergerLayer* m_Layer;
-};
-
-template<>
-struct DummyLayer<armnn::OutputLayer, armnn::LayerBindingId>
-{
-    DummyLayer()
-    {
-        m_Layer = dummyGraph.AddLayer<armnn::OutputLayer>(armnn::LayerBindingId(), "");
-
-    }
-    ~DummyLayer()
-    {
-        dummyGraph.EraseLayer(m_Layer);
-    }
-    armnn::OutputLayer* m_Layer;
-};
-
-template<>
-struct DummyLayer<armnn::SplitterLayer>
-{
-    DummyLayer()
-    {
-        armnn::ViewsDescriptor desc(1);
-        m_Layer = dummyGraph.AddLayer<armnn::SplitterLayer>(desc, "");
-
-    }
-    ~DummyLayer()
-    {
-        dummyGraph.EraseLayer(m_Layer);
-    }
-    armnn::SplitterLayer* m_Layer;
-};
-
-template <typename ConvolutionLayerType>
-struct DummyConvolutionLayer
-{
-    DummyConvolutionLayer()
-    {
-        typename ConvolutionLayerType::DescriptorType desc;
-        m_Layer = dummyGraph.AddLayer<ConvolutionLayerType>(desc, "");
-        m_Layer->m_Weight = std::make_unique<armnn::ScopedCpuTensorHandle>(
-            armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
-        m_Layer->m_Bias = std::make_unique<armnn::ScopedCpuTensorHandle>(
-            armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
-    }
-    ~DummyConvolutionLayer()
-    {
-        dummyGraph.EraseLayer(m_Layer);
-    }
-    ConvolutionLayerType* m_Layer;
-};
-
-template<>
-struct DummyLayer<armnn::Convolution2dLayer>
-    : public DummyConvolutionLayer<armnn::Convolution2dLayer>
-{
-};
-
-template<>
-struct DummyLayer<armnn::DepthwiseConvolution2dLayer>
-    : public DummyConvolutionLayer<armnn::DepthwiseConvolution2dLayer>
-{
-};
-
-template <typename LstmLayerType>
-struct DummyLstmLayer
-{
-    DummyLstmLayer()
-    {
-        typename LstmLayerType::DescriptorType desc;
-        desc.m_CifgEnabled = false;
-
-        m_Layer = dummyGraph.AddLayer<LstmLayerType>(armnn::LstmDescriptor(), "");
-        m_Layer->m_BasicParameters.m_InputToForgetWeights     = std::make_unique<armnn::ScopedCpuTensorHandle>(
-                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
-        m_Layer->m_BasicParameters.m_InputToCellWeights       = std::make_unique<armnn::ScopedCpuTensorHandle>(
-                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
-        m_Layer->m_BasicParameters.m_InputToOutputWeights     = std::make_unique<armnn::ScopedCpuTensorHandle>(
-                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
-        m_Layer->m_BasicParameters.m_RecurrentToForgetWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
-                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
-        m_Layer->m_BasicParameters.m_RecurrentToCellWeights   = std::make_unique<armnn::ScopedCpuTensorHandle>(
-                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
-        m_Layer->m_BasicParameters.m_RecurrentToOutputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>(
-                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
-        m_Layer->m_BasicParameters.m_ForgetGateBias           = std::make_unique<armnn::ScopedCpuTensorHandle>(
-                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
-        m_Layer->m_BasicParameters.m_CellBias                 = std::make_unique<armnn::ScopedCpuTensorHandle>(
-                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
-        m_Layer->m_BasicParameters.m_OutputGateBias           = std::make_unique<armnn::ScopedCpuTensorHandle>(
-                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
-
-        m_Layer->m_CifgParameters.m_InputToInputWeights        = std::make_unique<armnn::ScopedCpuTensorHandle>(
-                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
-        m_Layer->m_CifgParameters.m_RecurrentToInputWeights    = std::make_unique<armnn::ScopedCpuTensorHandle>(
-                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
-        m_Layer->m_CifgParameters.m_CellToInputWeights         = std::make_unique<armnn::ScopedCpuTensorHandle>(
-                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
-        m_Layer->m_CifgParameters.m_InputGateBias              = std::make_unique<armnn::ScopedCpuTensorHandle>(
-                armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
-    }
-    ~DummyLstmLayer()
-    {
-        dummyGraph.EraseLayer(m_Layer);
-    }
-    armnn::LstmLayer* m_Layer;
-};
-
-template<>
-struct DummyLayer<armnn::LstmLayer>
-        : public DummyLstmLayer<armnn::LstmLayer>
-{
-};
-
-template<>
-struct DummyLayer<armnn::FullyConnectedLayer>
-{
-    DummyLayer()
-    {
-        armnn::FullyConnectedLayer::DescriptorType desc;
-        m_Layer = dummyGraph.AddLayer<armnn::FullyConnectedLayer>(desc, "");
-        m_Layer->m_Weight = std::make_unique<armnn::ScopedCpuTensorHandle>(
-            armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32));
-    }
-    ~DummyLayer()
-    {
-        dummyGraph.EraseLayer(m_Layer);
-    }
-    armnn::FullyConnectedLayer* m_Layer;
-};
-
-// Tag for giving LayerType entries a unique strong type each.
-template<armnn::LayerType>
-struct Tag{};
-
-#define DECLARE_LAYER_POLICY_CUSTOM_PARAM(name, descType) \
-template<armnn::DataType DataType> \
-struct LayerTypePolicy<armnn::LayerType::name, DataType> \
-{ \
-    using Type = armnn::name##Layer; \
-    using Desc = descType; \
-    using QueueDesc = armnn::name##QueueDescriptor; \
-    constexpr static const char* NameStr = #name; \
-    \
-    static std::unique_ptr<armnn::IWorkload> MakeDummyWorkload(armnn::IWorkloadFactory *factory, \
-        unsigned int nIn, unsigned int nOut) \
-    { \
-        QueueDesc desc; \
-        armnn::WorkloadInfo info = MakeDummyWorkloadInfo<DataType>(nIn, nOut); \
-        return factory->Create##name(desc, info); \
-    } \
-};
-
-// Define a layer policy specialization for use with the IsLayerSupported tests.
-// Use this version for layers whose constructor takes 1 parameter(name).
-#define DECLARE_LAYER_POLICY_1_PARAM(name) DECLARE_LAYER_POLICY_CUSTOM_PARAM(name, void)
-
-// Define a layer policy specialization for use with the IsLayerSupported tests.
-// Use this version for layers whose constructor takes 2 parameters(descriptor and name).
-#define DECLARE_LAYER_POLICY_2_PARAM(name) DECLARE_LAYER_POLICY_CUSTOM_PARAM(name, armnn::name##Descriptor)
-
-// Layer policy template.
-template<armnn::LayerType Type, armnn::DataType DataType>
-struct LayerTypePolicy;
-
-// Every entry in the armnn::LayerType enum must be accounted for below.
-DECLARE_LAYER_POLICY_2_PARAM(Activation)
-
-DECLARE_LAYER_POLICY_1_PARAM(Addition)
-
-DECLARE_LAYER_POLICY_2_PARAM(BatchNormalization)
-
-DECLARE_LAYER_POLICY_1_PARAM(Constant)
-
-DECLARE_LAYER_POLICY_1_PARAM(ConvertFp16ToFp32)
-
-DECLARE_LAYER_POLICY_1_PARAM(ConvertFp32ToFp16)
-
-DECLARE_LAYER_POLICY_2_PARAM(Convolution2d)
-
-DECLARE_LAYER_POLICY_1_PARAM(MemCopy)
-
-DECLARE_LAYER_POLICY_2_PARAM(DepthwiseConvolution2d)
-
-DECLARE_LAYER_POLICY_2_PARAM(FakeQuantization)
-
-DECLARE_LAYER_POLICY_1_PARAM(Floor)
-
-DECLARE_LAYER_POLICY_2_PARAM(FullyConnected)
-
-DECLARE_LAYER_POLICY_CUSTOM_PARAM(Input, armnn::LayerBindingId)
-
-DECLARE_LAYER_POLICY_2_PARAM(L2Normalization)
-
-DECLARE_LAYER_POLICY_2_PARAM(Lstm)
-
-DECLARE_LAYER_POLICY_2_PARAM(Mean)
-
-DECLARE_LAYER_POLICY_2_PARAM(Merger)
-
-DECLARE_LAYER_POLICY_1_PARAM(Multiplication)
-
-DECLARE_LAYER_POLICY_2_PARAM(Normalization)
-
-DECLARE_LAYER_POLICY_CUSTOM_PARAM(Output, armnn::LayerBindingId)
-
-DECLARE_LAYER_POLICY_2_PARAM(Pad)
-
-DECLARE_LAYER_POLICY_2_PARAM(Permute)
-
-DECLARE_LAYER_POLICY_2_PARAM(Pooling2d)
-
-DECLARE_LAYER_POLICY_1_PARAM(Division)
-
-DECLARE_LAYER_POLICY_2_PARAM(ResizeBilinear)
-
-DECLARE_LAYER_POLICY_2_PARAM(Reshape)
-
-DECLARE_LAYER_POLICY_2_PARAM(Softmax)
-
-DECLARE_LAYER_POLICY_2_PARAM(SpaceToBatchNd)
-
-DECLARE_LAYER_POLICY_2_PARAM(Splitter)
-
-DECLARE_LAYER_POLICY_1_PARAM(Subtraction)
-
-
-// Generic implementation to get the number of input slots for a given layer type;
-template<armnn::LayerType Type>
-unsigned int GetNumInputs(const armnn::Layer& layer)
-{
-    return layer.GetNumInputSlots();
-}
-
-// Generic implementation to get the number of output slots for a given layer type;
-template<armnn::LayerType Type>
-unsigned int GetNumOutputs(const armnn::Layer& layer)
-{
-    return layer.GetNumOutputSlots();
-}
-
-template<>
-unsigned int GetNumInputs<armnn::LayerType::Merger>(const armnn::Layer& layer)
-{
-    boost::ignore_unused(layer);
-    return 2;
-}
-
-// Tests that the IsLayerSupported() function returns the correct value.
-// We determined the correct value by *trying* to create the relevant workload and seeing if it matches what we expect.
-// Returns true if expectations are met, otherwise returns false.
-template<typename FactoryType, armnn::DataType DataType, armnn::LayerType Type>
-bool IsLayerSupportedTest(FactoryType *factory, Tag<Type>)
-{
-    using LayerPolicy = LayerTypePolicy<Type, DataType>;
-    using LayerType = typename LayerPolicy::Type;
-    using LayerDesc = typename LayerPolicy::Desc;
-    DummyLayer<LayerType, LayerDesc> layer;
-
-    unsigned int numIn = GetNumInputs<Type>(*layer.m_Layer);
-    unsigned int numOut = GetNumOutputs<Type>(*layer.m_Layer);
-
-    // Make another dummy layer just to make IsLayerSupported have valid inputs.
-    DummyLayer<armnn::ConstantLayer, void> previousLayer;
-    // Set output of the previous layer to a dummy tensor.
-    armnn::TensorInfo output = MakeDummyTensorInfo<DataType>();
-    previousLayer.m_Layer->GetOutputSlot(0).SetTensorInfo(output);
-    // Connect all outputs of the previous layer to inputs of tested layer.
-    for (unsigned int i = 0; i < numIn; i++)
-    {
-        armnn::IOutputSlot& previousLayerOutputSlot = previousLayer.m_Layer->GetOutputSlot(0);
-        armnn::IInputSlot& layerInputSlot = layer.m_Layer->GetInputSlot(i);
-        previousLayerOutputSlot.Connect(layerInputSlot);
-    }
-    // Set outputs of tested layer to a dummy tensor.
-    for (unsigned int i = 0; i < numOut; i++)
-    {
-        layer.m_Layer->GetOutputSlot(0).SetTensorInfo(output);
-    }
-
-    std::string layerName = LayerPolicy::NameStr;
-    std::string reasonIfUnsupported;
-    if (FactoryType::IsLayerSupported(*layer.m_Layer, DataType, reasonIfUnsupported))
-    {
-        std::string errorMsg = " layer expected support but found none.";
-        try
-        {
-            bool retVal = LayerPolicy::MakeDummyWorkload(factory, numIn, numOut).get() != nullptr;
-            BOOST_CHECK_MESSAGE(retVal, layerName << errorMsg);
-            return retVal;
-        }
-        catch(const armnn::InvalidArgumentException& e)
-        {
-            boost::ignore_unused(e);
-            // This is ok since we throw InvalidArgumentException when creating the dummy workload.
-            return true;
-        }
-        catch(const std::exception& e)
-        {
-            errorMsg = e.what();
-            BOOST_TEST_ERROR(layerName << ": " << errorMsg);
-            return false;
-        }
-        catch(...)
-        {
-            errorMsg = "Unexpected error while testing support for ";
-            BOOST_TEST_ERROR(errorMsg << layerName);
-            return false;
-        }
-    }
-    else
-    {
-        std::string errorMsg = "layer expected no support (giving reason: " + reasonIfUnsupported + ") but found some.";
-        try
-        {
-            bool retVal = LayerPolicy::MakeDummyWorkload(factory, numIn, numOut).get() == nullptr;
-            BOOST_CHECK_MESSAGE(retVal, layerName << errorMsg);
-            return retVal;
-        }
-        // These two exceptions are ok: For workloads that are partially supported, attempting to instantiate them
-        // using parameters that make IsLayerSupported() return false should throw an
-        // InvalidArgumentException or UnimplementedException.
-        catch(const armnn::InvalidArgumentException& e)
-        {
-            boost::ignore_unused(e);
-            return true;
-        }
-        catch(const armnn::UnimplementedException& e)
-        {
-            boost::ignore_unused(e);
-            return true;
-        }
-        catch(const std::exception& e)
-        {
-            errorMsg = e.what();
-            BOOST_TEST_ERROR(layerName << ": " << errorMsg);
-            return false;
-        }
-        catch(...)
-        {
-            errorMsg = "Unexpected error while testing support for ";
-            BOOST_TEST_ERROR(errorMsg << layerName);
-            return false;
-        }
-    }
-}
-
-// Helper function to compute the next type in the LayerType enum.
-constexpr armnn::LayerType NextType(armnn::LayerType type)
-{
-    return static_cast<armnn::LayerType>(static_cast<int>(type)+1);
-}
-
-// Termination function for determining the end of the LayerType enumeration.
-template<typename FactoryType, armnn::DataType DataType, armnn::LayerType Type>
-bool IsLayerSupportedTestsImpl(FactoryType *factory, Tag<armnn::LayerType::LastLayer>)
-{
-    return IsLayerSupportedTest<FactoryType, DataType, Type>(factory, Tag<Type>());
-};
-
-// Recursive function to test and enter in the LayerType enum and then iterate on the next entry.
-template<typename FactoryType, armnn::DataType DataType, armnn::LayerType Type>
-bool IsLayerSupportedTestsImpl(FactoryType *factory, Tag<Type>)
-{
-    bool v = IsLayerSupportedTest<FactoryType, DataType, Type>(factory, Tag<Type>());
-
-    return v &&
-    IsLayerSupportedTestsImpl<FactoryType, DataType, NextType(Type)>
-        (factory, Tag<NextType(Type)>());
-};
-
-// Helper function to pass through to the test framework.
-template<typename FactoryType, armnn::DataType DataType>
-bool IsLayerSupportedTests(FactoryType *factory)
-{
-    return IsLayerSupportedTestsImpl<FactoryType, DataType>(factory, Tag<armnn::LayerType::FirstLayer>());
-};
-
-template<armnn::LayerType Type>
-bool TestLayerTypeMatches()
-{
-    using LayerPolicy = LayerTypePolicy<Type, armnn::DataType::Float32>;
-    using LayerType = typename LayerPolicy::Type;
-    using LayerDesc = typename LayerPolicy::Desc;
-    DummyLayer<LayerType, LayerDesc> layer;
-
-    std::stringstream ss;
-    ss << LayerPolicy::NameStr << " layer type mismatches expected layer type value.";
-    bool v = Type == layer.m_Layer->GetType();
-    BOOST_CHECK_MESSAGE(v, ss.str());
-    return v;
-};
-
-template<armnn::LayerType Type>
-bool LayerTypeMatchesTestImpl(Tag<armnn::LayerType::LastLayer>)
-{
-    return TestLayerTypeMatches<Type>();
-};
-
-template<armnn::LayerType Type>
-bool LayerTypeMatchesTestImpl(Tag<Type>)
-{
-    return TestLayerTypeMatches<Type>() &&
-        LayerTypeMatchesTestImpl<NextType(Type)>(Tag<NextType(Type)>());
-};
-
-template<typename FactoryType, typename LayerType, armnn::DataType InputDataType , armnn::DataType OutputDataType>
-bool IsConvertLayerSupportedTests(std::string& reasonIfUnsupported)
-{
-    armnn::Graph graph;
-    LayerType* const layer = graph.AddLayer<LayerType>("LayerName");
-
-    armnn::Layer* const input = graph.AddLayer<armnn::InputLayer>(0, "input");
-    armnn::Layer* const output = graph.AddLayer<armnn::OutputLayer>(0, "output");
-
-    armnn::TensorInfo inputTensorInfo({1, 3, 2, 3}, InputDataType);
-    armnn::TensorInfo outputTensorInfo({1, 3, 2, 3}, OutputDataType);
-
-    input->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
-    input->GetOutputHandler(0).SetTensorInfo(inputTensorInfo);
-    layer->GetOutputSlot(0).Connect(output->GetInputSlot(0));
-    layer->GetOutputHandler(0).SetTensorInfo(outputTensorInfo);
-
-    bool result = FactoryType::IsLayerSupported(*layer, InputDataType, reasonIfUnsupported);
-
-    return result;
-};
-
-} //namespace
diff --git a/src/backends/test/JsonPrinterTestImpl.hpp b/src/backends/test/JsonPrinterTestImpl.hpp
deleted file mode 100644
index 47e0ec761b..0000000000
--- a/src/backends/test/JsonPrinterTestImpl.hpp
+++ /dev/null
@@ -1,354 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include <armnn/Descriptors.hpp>
-#include <armnn/IRuntime.hpp>
-#include <armnn/INetwork.hpp>
-#include <armnn/Profiling.hpp>
-
-#include <boost/test/unit_test.hpp>
-#include <boost/algorithm/string.hpp>
-#include <boost/lexical_cast.hpp>
-
-#include <sstream>
-#include <stack>
-#include <string>
-#include <vector>
-
-inline bool AreMatchingPair(const char opening, const char closing)
-{
-    return (opening == '{' && closing == '}') || (opening == '[' && closing == ']');
-}
-
-inline bool AreParenthesesMatching(const std::string& exp)
-{
-    std::stack<char> expStack;
-    for (size_t i = 0; i < exp.length(); ++i)
-    {
-        if (exp[i] == '{' || exp[i] == '[')
-        {
-            expStack.push(exp[i]);
-        }
-        else if (exp[i] == '}' || exp[i] == ']')
-        {
-            if (expStack.empty() || !AreMatchingPair(expStack.top(), exp[i]))
-            {
-                return false;
-            }
-            else
-            {
-                expStack.pop();
-            }
-        }
-    }
-    return expStack.empty();
-}
-
-inline std::vector<double> ExtractMeasurements(const std::string& exp)
-{
-    std::vector<double> numbers;
-    bool inArray = false;
-    std::string numberString;
-    for (size_t i = 0; i < exp.size(); ++i)
-    {
-        if (exp[i] == '[')
-        {
-            inArray = true;
-        }
-        else if (exp[i] == ']' && inArray)
-        {
-            try
-            {
-                boost::trim_if(numberString, boost::is_any_of("\t,\n"));
-                numbers.push_back(std::stod(numberString));
-            }
-            catch (std::invalid_argument const& e)
-            {
-                BOOST_FAIL("Could not convert measurements to double: " + numberString);
-            }
-
-            numberString.clear();
-            inArray = false;
-        }
-        else if (exp[i] == ',' && inArray)
-        {
-            try
-            {
-                boost::trim_if(numberString, boost::is_any_of("\t,\n"));
-                numbers.push_back(std::stod(numberString));
-            }
-            catch (std::invalid_argument const& e)
-            {
-                BOOST_FAIL("Could not convert measurements to double: " + numberString);
-            }
-            numberString.clear();
-        }
-        else if (exp[i] != '[' && inArray && exp[i] != ',' && exp[i] != ' ')
-        {
-            numberString += exp[i];
-        }
-    }
-    return numbers;
-}
-
-inline std::vector<std::string> ExtractSections(const std::string& exp)
-{
-    std::vector<std::string> sections;
-
-    std::stack<size_t> s;
-    for (size_t i = 0; i < exp.size(); i++)
-    {
-        if (exp.at(i) == '{')
-        {
-            s.push(i);
-        }
-        else if (exp.at(i) == '}')
-        {
-            size_t from = s.top();
-            s.pop();
-            sections.push_back(exp.substr(from, i - from + 1));
-        }
-    }
-
-    return sections;
-}
-
-inline std::string SoftmaxProfilerTestSetupHelper(const std::vector<armnn::BackendId>& backends)
-{
-    using namespace armnn;
-
-    BOOST_CHECK(!backends.empty());
-
-    ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance();
-
-    // Create runtime in which test will run
-    IRuntime::CreationOptions options;
-    options.m_EnableGpuProfiling = backends.front() == armnn::Compute::GpuAcc;
-    IRuntimePtr runtime(IRuntime::Create(options));
-
-    // build up the structure of the network
-    INetworkPtr net(INetwork::Create());
-
-    IConnectableLayer* input = net->AddInputLayer(0, "input");
-    IConnectableLayer* softmax = net->AddSoftmaxLayer(SoftmaxDescriptor(), "softmax");
-    IConnectableLayer* output  = net->AddOutputLayer(0, "output");
-
-    input->GetOutputSlot(0).Connect(softmax->GetInputSlot(0));
-    softmax->GetOutputSlot(0).Connect(output->GetInputSlot(0));
-
-    // set the tensors in the network
-    TensorInfo inputTensorInfo(TensorShape({1, 5}), DataType::QuantisedAsymm8);
-    inputTensorInfo.SetQuantizationOffset(100);
-    inputTensorInfo.SetQuantizationScale(10000.0f);
-    input->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
-
-    TensorInfo outputTensorInfo(TensorShape({1, 5}), DataType::QuantisedAsymm8);
-    outputTensorInfo.SetQuantizationOffset(0);
-    outputTensorInfo.SetQuantizationScale(1.0f / 256.0f);
-    softmax->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
-
-    // optimize the network
-    IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec());
-    if(!optNet)
-    {
-        BOOST_FAIL("Error occurred during Optimization, Optimize() returned nullptr.");
-    }
-    // load it into the runtime
-    NetworkId netId;
-    auto error = runtime->LoadNetwork(netId, std::move(optNet));
-    BOOST_TEST(error == Status::Success);
-
-    // create structures for input & output
-    std::vector<uint8_t> inputData
-        {
-            1, 10, 3, 200, 5
-            // one of inputs is sufficiently larger than the others to saturate softmax
-        };
-    std::vector<uint8_t> outputData(5);
-
-    armnn::InputTensors inputTensors
-        {
-            {0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data())}
-        };
-    armnn::OutputTensors outputTensors
-        {
-            {0, armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())}
-        };
-
-    runtime->GetProfiler(netId)->EnableProfiling(true);
-
-    // do the inferences
-    runtime->EnqueueWorkload(netId, inputTensors, outputTensors);
-    runtime->EnqueueWorkload(netId, inputTensors, outputTensors);
-    runtime->EnqueueWorkload(netId, inputTensors, outputTensors);
-
-    // retrieve the Profiler.Print() output
-    std::stringstream ss;
-    profilerManager.GetProfiler()->Print(ss);
-
-    return ss.str();
-}
-
-inline void SoftmaxProfilerTestValidationHelper(std::string& result, const std::string& testData)
-{
-    // ensure all measurements are greater than zero
-    std::vector<double> measurementsVector = ExtractMeasurements(result);
-    BOOST_CHECK(!measurementsVector.empty());
-
-    // check sections contain raw and unit tags
-    // first ensure Parenthesis are balanced
-    if (AreParenthesesMatching(result))
-    {
-        // remove parent sections that will not have raw or unit tag
-        std::vector<std::string> sectionVector = ExtractSections(result);
-        for (size_t i = 0; i < sectionVector.size(); ++i)
-        {
-            if (boost::contains(sectionVector[i], "\"ArmNN\":")
-                || boost::contains(sectionVector[i], "\"inference_measurements\":"))
-            {
-                sectionVector.erase(sectionVector.begin() + static_cast<int>(i));
-            }
-        }
-        BOOST_CHECK(!sectionVector.empty());
-
-        BOOST_CHECK(std::all_of(sectionVector.begin(), sectionVector.end(),
-                                [](std::string i) { return boost::contains(i, "\"raw\":"); }));
-
-        BOOST_CHECK(std::all_of(sectionVector.begin(), sectionVector.end(),
-                                [](std::string i) { return boost::contains(i, "\"unit\":"); }));
-    }
-
-    // remove the time measurements as they vary from test to test
-    result.erase(std::remove_if (result.begin(),result.end(),
-                                 [](char c) { return c == '.'; }), result.end());
-    result.erase(std::remove_if (result.begin(), result.end(), &isdigit), result.end());
-    result.erase(std::remove_if (result.begin(),result.end(),
-                                 [](char c) { return c == '\t'; }), result.end());
-
-    BOOST_CHECK(boost::contains(result, "ArmNN"));
-    BOOST_CHECK(boost::contains(result, "inference_measurements"));
-    BOOST_CHECK(boost::contains(result, "layer_measurements"));
-    BOOST_CHECK_EQUAL(result, testData);
-
-    // ensure no spare parenthesis present in print output
-    BOOST_CHECK(AreParenthesesMatching(result));
-}
-
-inline void SetupSoftmaxProfilerWithSpecifiedBackendsAndValidateJsonPrinterResult(
-        const std::vector<armnn::BackendId>& backends)
-{
-    // setup the test fixture and obtain JSON Printer result
-    std::string result = SoftmaxProfilerTestSetupHelper(backends);
-
-    std::string backend = "Ref";
-    std::string changeLine31 = "\n},\n\"CopyMemGeneric_Execute\": {";
-    std::string changeLine39 = "us\"";
-    std::string changeLine40;
-    std::string changeLine45;
-
-    if (backends[0] == armnn::Compute::GpuAcc) {
-        backend = "Cl";
-        changeLine31 = ",\n\"OpenClKernelTimer/: softmax_layer_max_shift_exp_sum_quantized_serial GWS[,,]\": {";
-        changeLine39 = R"(us"
-},
-"OpenClKernelTimer/: softmax_layer_norm_quantized GWS[,,]": {
-"raw": [
-,
-,
-
-],
-"unit": "us")";
-
-        changeLine40 = R"(
-},
-"CopyMemGeneric_Execute": {
-"raw": [
-,
-,
-
-],
-"unit": "us")";
-        changeLine45 = "}\n";
-    }
-    else if (backends[0] == armnn::Compute::CpuAcc)
-    {
-        backend = "Neon";
-        changeLine31 = ",\n\"NeonKernelTimer/: NEFillBorderKernel\": {";
-        changeLine39 = R"(us"
-},
-"NeonKernelTimer/: NELogitsDMaxKernel": {
-"raw": [
-,
-,
-
-],
-"unit": "us"
-},
-"NeonKernelTimer/: NELogitsDSoftmaxKernel": {
-"raw": [
-,
-,
-
-],
-"unit": "us")";
-        changeLine40 = R"(
-},
-"CopyMemGeneric_Execute": {
-"raw": [
-,
-,
-
-],
-"unit": "us")";
-        changeLine45 = "}\n";
-    }
-
-    std::string testData = R"({
-"ArmNN": {
-"inference_measurements": {
-"raw": [
-,
-,
-
-],
-"unit": "us",
-"layer_measurements": {
-"raw": [
-,
-,
-
-],
-"unit": "us",
-"CopyMemGeneric_Execute": {
-"raw": [
-,
-,
-
-],
-"unit": "us"
-},
-")" + backend + R"(SoftmaxUintWorkload_Execute": {
-"raw": [
-,
-,
-
-],
-"unit": "us")" + changeLine31 + R"(
-"raw": [
-,
-,
-
-],
-"unit": ")" + changeLine39 + R"(
-})" + changeLine40 + R"(
-}
-}
-}
-}
-)" + changeLine45 + R"()";
-
-    // validate the JSON Printer result
-    SoftmaxProfilerTestValidationHelper(result, testData);
-}
diff --git a/src/backends/test/LayerReleaseConstantDataTest.cpp b/src/backends/test/LayerReleaseConstantDataTest.cpp
deleted file mode 100644
index 7549dfd5f8..0000000000
--- a/src/backends/test/LayerReleaseConstantDataTest.cpp
+++ /dev/null
@@ -1,212 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include <boost/test/unit_test.hpp>
-#include <boost/cast.hpp>
-
-#include <backends/WorkloadData.hpp>
-#include <Graph.hpp>
-
-#include <utility>
-
-#include <backends/CpuTensorHandle.hpp>
-#include <backends/cl/ClWorkloadFactory.hpp>
-
-using namespace armnn;
-using namespace std;
-
-// connects two layers
-void Connect(Layer* from, Layer* to, const TensorInfo& tensorInfo, unsigned int fromIndex = 0, unsigned int toIndex = 0)
-{
-    from->GetOutputSlot(fromIndex).Connect(to->GetInputSlot(toIndex));
-    from->GetOutputHandler(fromIndex).SetTensorInfo(tensorInfo);
-}
-
-/////////////////////////////////////////////////////////////////////////////////////////////
-// The following test are created specifically to test ReleaseConstantData() method in the Layer
-// They build very simple graphs including the layer will be checked.
-// Checks weights and biases before the method called and after.
-/////////////////////////////////////////////////////////////////////////////////////////////
-
-BOOST_AUTO_TEST_SUITE(LayerReleaseConstantDataTest)
-
-BOOST_AUTO_TEST_CASE(ReleaseBatchNormalizationLayerConstantDataTest)
-{
-    Graph             graph;
-    ClWorkloadFactory factory;
-
-    // create the layer we're testing
-    BatchNormalizationDescriptor layerDesc;
-    layerDesc.m_Eps = 0.05f;
-    BatchNormalizationLayer* const layer = graph.AddLayer<BatchNormalizationLayer>(layerDesc, "layer");
-
-    armnn::TensorInfo weightInfo({3}, armnn::DataType::Float32);
-    layer->m_Mean     = std::make_unique<ScopedCpuTensorHandle>(weightInfo);
-    layer->m_Variance = std::make_unique<ScopedCpuTensorHandle>(weightInfo);
-    layer->m_Beta     = std::make_unique<ScopedCpuTensorHandle>(weightInfo);
-    layer->m_Gamma    = std::make_unique<ScopedCpuTensorHandle>(weightInfo);
-    layer->m_Mean->Allocate();
-    layer->m_Variance->Allocate();
-    layer->m_Beta->Allocate();
-    layer->m_Gamma->Allocate();
-
-    // create extra layers
-    Layer* const input = graph.AddLayer<InputLayer>(0, "input");
-    Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
-
-    // connect up
-    armnn::TensorInfo tensorInfo({2, 3, 1, 1}, armnn::DataType::Float32);
-    Connect(input, layer, tensorInfo);
-    Connect(layer, output, tensorInfo);
-
-    // check the constants that they are not NULL
-    BOOST_CHECK(layer->m_Mean != nullptr);
-    BOOST_CHECK(layer->m_Variance != nullptr);
-    BOOST_CHECK(layer->m_Beta != nullptr);
-    BOOST_CHECK(layer->m_Gamma != nullptr);
-
-    // free up the constants..
-    layer->ReleaseConstantData();
-
-    // check the constants that they are NULL now
-    BOOST_CHECK(layer->m_Mean == nullptr);
-    BOOST_CHECK(layer->m_Variance == nullptr);
-    BOOST_CHECK(layer->m_Beta == nullptr);
-    BOOST_CHECK(layer->m_Gamma == nullptr);
-
- }
-
-
- BOOST_AUTO_TEST_CASE(ReleaseConvolution2dLayerConstantDataTest)
- {
-     Graph             graph;
-     ClWorkloadFactory factory;
-
-     // create the layer we're testing
-     Convolution2dDescriptor layerDesc;
-     layerDesc.m_PadLeft = 3;
-     layerDesc.m_PadRight = 3;
-     layerDesc.m_PadTop = 1;
-     layerDesc.m_PadBottom = 1;
-     layerDesc.m_StrideX = 2;
-     layerDesc.m_StrideY = 4;
-     layerDesc.m_BiasEnabled = true;
-
-     Convolution2dLayer* const layer = graph.AddLayer<Convolution2dLayer>(layerDesc, "layer");
-
-     layer->m_Weight = std::make_unique<ScopedCpuTensorHandle>(TensorInfo({2, 3, 5, 3},
-                                                                          armnn::DataType::Float32));
-     layer->m_Bias   = std::make_unique<ScopedCpuTensorHandle>
-             (TensorInfo({2}, GetBiasDataType(armnn::DataType::Float32)));
-
-     layer->m_Weight->Allocate();
-     layer->m_Bias->Allocate();
-
-     // create extra layers
-     Layer* const input = graph.AddLayer<InputLayer>(0, "input");
-     Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
-
-     // connect up
-     Connect(input, layer, TensorInfo({2, 3, 8, 16}, armnn::DataType::Float32));
-     Connect(layer, output, TensorInfo({2, 2, 2, 10}, armnn::DataType::Float32));
-
-     // check the constants that they are not NULL
-     BOOST_CHECK(layer->m_Weight != nullptr);
-     BOOST_CHECK(layer->m_Bias != nullptr);
-
-     // free up the constants..
-     layer->ReleaseConstantData();
-
-     // check the constants that they are NULL now
-     BOOST_CHECK(layer->m_Weight == nullptr);
-     BOOST_CHECK(layer->m_Bias == nullptr);
-}
-
-BOOST_AUTO_TEST_CASE(ReleaseDepthwiseConvolution2dLayerConstantDataTest)
-{
-    Graph             graph;
-    ClWorkloadFactory factory;
-
-    // create the layer we're testing
-    DepthwiseConvolution2dDescriptor layerDesc;
-    layerDesc.m_PadLeft         = 3;
-    layerDesc.m_PadRight        = 3;
-    layerDesc.m_PadTop          = 1;
-    layerDesc.m_PadBottom       = 1;
-    layerDesc.m_StrideX         = 2;
-    layerDesc.m_StrideY         = 4;
-    layerDesc.m_BiasEnabled     = true;
-
-    DepthwiseConvolution2dLayer* const layer = graph.AddLayer<DepthwiseConvolution2dLayer>(layerDesc, "layer");
-
-    layer->m_Weight = std::make_unique<ScopedCpuTensorHandle>(TensorInfo({3, 3, 5, 3}, DataType::Float32));
-    layer->m_Bias   = std::make_unique<ScopedCpuTensorHandle>(TensorInfo({9}, DataType::Float32));
-    layer->m_Weight->Allocate();
-    layer->m_Bias->Allocate();
-
-    // create extra layers
-    Layer* const input = graph.AddLayer<InputLayer>(0, "input");
-    Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
-
-    // connect up
-    Connect(input, layer, TensorInfo({2, 3, 8, 16}, armnn::DataType::Float32));
-    Connect(layer, output, TensorInfo({2, 9, 2, 10}, armnn::DataType::Float32));
-
-    // check the constants that they are not NULL
-    BOOST_CHECK(layer->m_Weight != nullptr);
-    BOOST_CHECK(layer->m_Bias != nullptr);
-
-    // free up the constants..
-    layer->ReleaseConstantData();
-
-    // check the constants that they are NULL now
-    BOOST_CHECK(layer->m_Weight == nullptr);
-    BOOST_CHECK(layer->m_Bias == nullptr);
-}
-
-BOOST_AUTO_TEST_CASE(ReleaseFullyConnectedLayerConstantDataTest)
-{
-    Graph             graph;
-    ClWorkloadFactory factory;
-
-    // create the layer we're testing
-    FullyConnectedDescriptor layerDesc;
-    layerDesc.m_BiasEnabled = true;
-    layerDesc.m_TransposeWeightMatrix = true;
-
-    FullyConnectedLayer* const layer = graph.AddLayer<FullyConnectedLayer>(layerDesc, "layer");
-
-    float inputsQScale = 1.0f;
-    float outputQScale = 2.0f;
-
-    layer->m_Weight = std::make_unique<ScopedCpuTensorHandle>(TensorInfo({7, 20},
-                                                          DataType::QuantisedAsymm8, inputsQScale, 0));
-    layer->m_Bias   = std::make_unique<ScopedCpuTensorHandle>(TensorInfo({7},
-                                                          GetBiasDataType(DataType::QuantisedAsymm8), inputsQScale));
-    layer->m_Weight->Allocate();
-    layer->m_Bias->Allocate();
-
-    // create extra layers
-    Layer* const input = graph.AddLayer<InputLayer>(0, "input");
-    Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
-
-    // connect up
-    Connect(input, layer, TensorInfo({3, 1, 4, 5}, DataType::QuantisedAsymm8, inputsQScale));
-    Connect(layer, output, TensorInfo({3, 7}, DataType::QuantisedAsymm8, outputQScale));
-
-    // check the constants that they are not NULL
-    BOOST_CHECK(layer->m_Weight != nullptr);
-    BOOST_CHECK(layer->m_Bias != nullptr);
-
-    // free up the constants..
-    layer->ReleaseConstantData();
-
-    // check the constants that they are NULL now
-    BOOST_CHECK(layer->m_Weight == nullptr);
-    BOOST_CHECK(layer->m_Bias == nullptr);
-}
-
-BOOST_AUTO_TEST_SUITE_END()
-
diff --git a/src/backends/test/LayerTests.cpp b/src/backends/test/LayerTests.cpp
deleted file mode 100755
index b5fd629d66..0000000000
--- a/src/backends/test/LayerTests.cpp
+++ /dev/null
@@ -1,6125 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#include "LayerTests.hpp"
-
-#include "test/TensorHelpers.hpp"
-#include "TensorCopyUtils.hpp"
-#include "Permute.hpp"
-
-#include <boost/test/unit_test.hpp>
-#include <boost/assert.hpp>
-
-#include <armnn/LayerSupport.hpp>
-
-#include <backends/CpuTensorHandle.hpp>
-#include <backends/WorkloadFactory.hpp>
-
-#include <algorithm>
-#include <boost/cast.hpp>
-
-#include "WorkloadTestUtils.hpp"
-#include "Conv2dTestImpl.hpp"
-#include "BatchNormTestImpl.hpp"
-#include "ActivationTestImpl.hpp"
-#include "Pooling2dTestImpl.hpp"
-#include "ReshapeTestImpl.hpp"
-#include "FullyConnectedTestImpl.hpp"
-#include "SplitterTestImpl.hpp"
-#include "SoftmaxTestImpl.hpp"
-#include "NormTestImpl.hpp"
-#include "PermuteTestImpl.hpp"
-#include "LstmTestImpl.hpp"
-#include "ConvertFp16ToFp32TestImpl.hpp"
-#include "ConvertFp32ToFp16TestImpl.hpp"
-
-// 3-channel 16x8 image used as common input data for a number of Conv2d tests.
-static std::vector<float> ConvInput3x8x16({
-    0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
-    0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
-    0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
-    0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
-    0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
-    0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
-    0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
-    0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
-    0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-    0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-    0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-    0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-    0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-    0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-    0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-    0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-    -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-    -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-    -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-    -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-    -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-    -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-    -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-    -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1
-});
-
-// 2-channel bias used by a number of Conv2d tests.
-static std::vector<float> Bias2({0, 2});
-
-// Helper function that returns either Bias2 or an empty vector depending on whether bias is enabled.
-template<typename T>
-boost::multi_array<T, 1> GetBias2(bool biasEnabled, float qScale, int32_t qOffset)
-{
-    if(biasEnabled)
-    {
-        armnn::TensorInfo biasDesc({static_cast<unsigned int>(Bias2.size())}, armnn::GetDataType<T>());
-        boost::multi_array<T, 1> bias = MakeTensor<T, 1>(biasDesc, QuantizedVector<T>(qScale, qOffset, Bias2));
-        return bias;
-    }
-    else
-    {
-        return boost::multi_array<T, 1>();
-    }
-}
-
-template<typename T>
-LayerTestResult<T, 4> SimpleConvolution2d3x5TestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                       float                    qScale,
-                                                       int32_t                  qOffset,
-                                                       bool                     biasEnabled,
-                                                       const armnn::DataLayoutIndexed& layout)
-{
-    // Use common single-batch 3-channel 16x8 image.
-    armnn::TensorInfo inputDesc({1, 3, 8, 16}, armnn::GetDataType<T>());
-    boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, QuantizedVector<T>(qScale, qOffset, ConvInput3x8x16));
-
-    // Use a 2-element batch with 3-channel 3x5 kernels.
-    armnn::TensorInfo kernelDesc({2, 3, 5, 3}, armnn::GetDataType<T>());
-    boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>(
-        QuantizedVector<T>(qScale, qOffset, {
-            1, 1, 1,
-            1, -1, 1,
-            1, 1, 1,
-            1, 1, 1,
-            1, 1, 1,
-
-            0, 0, 0,
-            0, 0, 0,
-            0, 0, 0,
-            0, 0, 0,
-            0, 0, 0,
-
-            2, 2, 2,
-            2, 2, 2,
-            2, 2, 2,
-            2, 2, 2,
-            2, 2, 2,
-
-
-            0, 0, 0,
-            0, 0, 0,
-            0, 0, 0,
-            0, 0, 0,
-            0, 0, 0,
-
-            1, 1, 1,
-            1, 1, 1,
-            1, 1, 1,
-            1, 1, 1,
-            1, 1, 1,
-
-            0, 0, 0,
-            0, 0, 0,
-            0, 0, 0,
-            0, 0, 0,
-            0, 0, 0
-        })));
-
-    // Expected output is 2 batch elements of a 1-channel 14x4 image.
-    armnn::TensorInfo outputDesc({1, 2, 4, 14}, armnn::GetDataType<T>());
-    boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>(
-        QuantizedVector<T>(qScale, qOffset, {
-            -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24,
-            -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25,
-            -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f,
-            -23.5f, -23.5f, -23.5f,
-            -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f,
-            -23.5f, -23.5f, -23.5f,
-
-            5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-            5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-            5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-            5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-        })));
-
-    return SimpleConvolution2dTestImpl<T>(workloadFactory,
-      input,
-      kernel,
-      GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(biasEnabled, qScale, qOffset),
-      expectedOutput,
-      qScale,
-      qOffset,
-      layout);
-}
-
-template<typename T>
-LayerTestResult<T, 4> SimpleConvolution2d3x3TestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                       float                    qScale,
-                                                       int32_t                  qOffset,
-                                                       bool                     biasEnabled,
-                                                       const armnn::DataLayoutIndexed& layout)
-{
-    // Use a 3x3 kernel, which exercises ArmCompute's direct convolution path.
-
-    // Use common single-batch 3-channel 16x8 image.
-    armnn::TensorInfo inputDesc({1, 3, 8, 16}, armnn::GetDataType<T>());
-    boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, QuantizedVector<T>(qScale, qOffset, ConvInput3x8x16));
-
-    // Use a 2-element batch of 3-channel 3x3 kernels.
-    armnn::TensorInfo kernelDesc({2, 3, 3, 3}, armnn::GetDataType<T>());
-    boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>(
-        QuantizedVector<T>(qScale, qOffset, {
-            1, 1, 1,
-            1, -1, 1,
-            1, 1, 1,
-
-            0, 0, 0,
-            0, 0, 0,
-            0, 0, 0,
-
-            2, 2, 2,
-            2, 2, 2,
-            2, 2, 2,
-
-
-            0, 0, 0,
-            0, 0, 0,
-            0, 0, 0,
-
-            1, 1, 1,
-            1, 1, 1,
-            1, 1, 1,
-
-            0, 0, 0,
-            0, 0, 0,
-            0, 0, 0
-        })));
-
-    // Expected output is 1 batch of a 2-channel 14x6 image.
-    armnn::TensorInfo outputDesc({1, 2, 6, 14}, armnn::GetDataType<T>());
-    boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>(
-        QuantizedVector<T>(qScale, qOffset, {
-            -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15,
-            -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16,
-            -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,
-            -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,
-            -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,
-            -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,
-
-            3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-            3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-            3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-            3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-            3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-            3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-        })));
-
-    return SimpleConvolution2dTestImpl<T>(workloadFactory,
-      input,
-      kernel,
-      GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(biasEnabled, qScale, qOffset),
-      expectedOutput,
-      qScale,
-      qOffset,
-      layout);
-}
-
-template<typename T>
-LayerTestResult<T, 4> SimpleConvolution2d3x3NhwcTestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                           float                    qScale,
-                                                           int32_t                  qOffset,
-                                                           bool                     biasEnabled,
-                                                           armnn::DataLayout        dataLayout)
-{
-    // Use common single-batch 5x5 image.
-
-    armnn::TensorInfo inputDesc({1, 3, 4, 1}, armnn::GetDataType<T>());
-    boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc,
-                                                      {
-                                                       1, 5, 2, 3,
-                                                       8, 7, 3, 6,
-                                                       3, 3, 9, 1
-                                                       });
-
-
-    // Use a 2-element batch of 3-channel 3x3 kernels.
-    armnn::TensorInfo kernelDesc({1, 3, 3, 1}, armnn::GetDataType<T>());
-    boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, {
-                                                                    4, 5, 6,
-                                                                    0, 0, 0,
-                                                                    3, 2, 1
-                                                                    });
-
-    // Expected output is 1 batch of a 5x5 image.
-    armnn::TensorInfo outputDesc({1, 3, 4, 1}, armnn::GetDataType<T>());
-
-    const std::vector<float> outputData =
-            {
-                    23, 41, 33, 21,
-                    44, 65, 76, 52,
-                    82, 85, 79, 42
-            };
-
-    boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, outputData);
-
-    return SimpleConvolution2dNhwcTestImpl<T>(workloadFactory,
-                                              input,
-                                              kernel,
-                                              boost::multi_array<T, 1>(),
-                                              expectedOutput,
-                                              dataLayout,
-                                              qScale,
-                                              qOffset);
-}
-
-LayerTestResult<float, 4> SimpleConvolution2d3x5Test(armnn::IWorkloadFactory& workloadFactory,
-                                                     bool                     biasEnabled,
-                                                     const armnn::DataLayoutIndexed& layout)
-{
-    return SimpleConvolution2d3x5TestCommon<float>(workloadFactory, 0.f, 0, biasEnabled, layout);
-}
-
-LayerTestResult<uint8_t, 4> SimpleConvolution2d3x5Uint8Test(armnn::IWorkloadFactory& workloadFactory,
-                                                            bool                     biasEnabled,
-                                                            const armnn::DataLayoutIndexed& layout)
-{
-    return SimpleConvolution2d3x5TestCommon<uint8_t>(workloadFactory, 0.5f, 50, biasEnabled, layout);
-}
-
-LayerTestResult<float, 4> SimpleConvolution2d3x3Test(armnn::IWorkloadFactory& workloadFactory,
-                                                     bool                     biasEnabled,
-                                                     const armnn::DataLayoutIndexed& layout)
-{
-    return SimpleConvolution2d3x3TestCommon<float>(workloadFactory, 0.f, 0, biasEnabled, layout);
-}
-
-LayerTestResult<float, 4> SimpleConvolution2d3x3NhwcTest(armnn::IWorkloadFactory& workloadFactory,
-                                                         bool                     biasEnabled)
-{
-    return SimpleConvolution2d3x3NhwcTestCommon<float>(workloadFactory, 0.f, 0, biasEnabled, armnn::DataLayout::NHWC);
-}
-
-LayerTestResult<uint8_t, 4> SimpleConvolution2d3x3Uint8Test(armnn::IWorkloadFactory& workloadFactory,
-                                                            bool                     biasEnabled,
-                                                            const armnn::DataLayoutIndexed& layout)
-{
-    return SimpleConvolution2d3x3TestCommon<uint8_t>(workloadFactory, 0.5f, 50, biasEnabled, layout);
-}
-
-template<typename T>
-LayerTestResult<T, 4> Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon(
-    armnn::IWorkloadFactory& workloadFactory,
-    const armnn::DataLayoutIndexed& layout,
-    float                    qScale,
-    int32_t                  qOffset)
-{
-    // Use a single-batch 1-channel 3x3 image as input.
-    armnn::TensorInfo inputDesc({1, 1, 3, 3}, armnn::GetDataType<T>());
-    boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, std::vector<T>(
-        QuantizedVector<T>(qScale, qOffset, {
-            11,21,31,
-            12,22,32,
-            13,23,33
-        })));
-
-    // Use 1 batch of a 1-channel 2x2 kernel.
-    armnn::TensorInfo kernelDesc({1, 1, 2, 2}, armnn::GetDataType<T>());
-    boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>(
-        QuantizedVector<T>(qScale, qOffset, {
-            -11,-21,
-            -12,-22,
-        })));
-
-// Expected output is 1 batch of a 1-channel 6x8 image.
-// Manually calculated like this:
-//[-11*0 -21*0  -12*0 -22*0  ; -11*0  -21*0  -12*0  -22*0  ; -11*0  -21*0  -12*0  -22*0  ; -11*0  -21*0 -12*0  -22*0 ..]
-//[-11*0 -21*0  -12*0 -22*11 ; -11*0  -21*0  -12*11 -22*21 ; -11*0  -21*0  -12*21 -22*31 ; -11*0  -21*0 -12*31 -22*0 ..]
-//[-11*0 -21*11 -12*0 -22*12 ; -11*11 -21*21 -12*12 -22*22 ; -11*21 -21*31 -12*22 -22*32 ; -11*31 -21*0 -12*32 -22*0 ..]
-//[-11*0 -21*12 -12*0 -22*13 ; -11*12 -21*22 -12*13 -22*23 ; -11*22 -21*32 -12*23 -22*33 ; -11*32 -21*0 -12*33 -22*0 ..]
-//[-11*0 -21*13 -12*0 -22*0  ; -11*13 -21*23 -12*0  -22*0  ; -11*23 -21*33 -12*0  -22*0  ; -11*33 -21*0 -12*0  -22*0 ..]
-//[-11*0 -21*0  -12*0 -22*0  ; -11*0  -21*0  -12*0  -22*0  ; -11*0  -21*0  -12*0  -22*0  ; -11*0  -21*0 -12*0  -22*0 ..]
-//[..... .....  ..... .....  ; .....  .....  .....  .....  ; .....  .....  .....  .....  ; .....  ..... .....  ..... ..]
-    armnn::TensorInfo outputDesc({1, 1, 8, 6}, armnn::GetDataType<T>());
-    boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>(
-        QuantizedVector<T>(qScale, qOffset, {
-               0,    0,      0,    0,    0,    0,
-            -242,  -594,  -934, -372,    0,    0,
-            -495, -1190, -1850, -725,    0,    0,
-            -538, -1256, -1916, -748,    0,    0,
-            -273, -626,  -946,  -363,    0,    0,
-               0,    0,     0,     0,    0,    0,
-               0,    0,     0,     0,    0,    0,
-               0,    0,     0,     0,    0,    0
-        })));
-
-    return SimpleConvolution2dTestImpl<T>(workloadFactory,
-      input,
-      kernel,
-      GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(false, qScale, qOffset),
-      expectedOutput,
-      qScale,
-      qOffset,
-      layout,
-      1,  // Padding left.
-      2,  // Padding top.
-      3,  // Padding right.
-      4); // Padding bottom.
-}
-
-template<typename T>
-LayerTestResult<T, 4> SimpleConvolution2dAsymmetricPaddingTestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                                     const armnn::DataLayoutIndexed& layout,
-                                                                     float qScale,
-                                                                     int32_t qOffset)
-{
-    // Use a single-batch 1-channel 5x5 image as input.
-    armnn::TensorInfo inputDesc({ 1, 1, 5, 5 }, armnn::GetDataType<T>());
-    boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, std::vector<T>(
-        QuantizedVector<T>(qScale, qOffset, {
-            11,21,31,41,51,
-            12,22,32,42,52,
-            13,23,33,43,53,
-            14,24,34,44,54,
-            15,25,35,45,55,
-        })));
-
-    // Use 1 batch of a 1-channel 4x4 kernel.
-    armnn::TensorInfo kernelDesc({ 1, 1, 4, 4 }, armnn::GetDataType<T>());
-    boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>(
-        QuantizedVector<T>(qScale, qOffset, {
-            -11,-21,-31,-41,
-            -12,-22,-32,-42,
-            -13,-23,-33,-43,
-            -14,-24,-34,-44,
-        })));
-
-    // Expected output is 1 batch of a 1-channel 5x5 image.
-    armnn::TensorInfo outputDesc({ 1, 1, 5, 5 }, armnn::GetDataType<T>());
-    std::vector<T> myVec(outputDesc.GetNumElements(), 0);
-    boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>(
-        QuantizedVector<T>(qScale, qOffset, {
-            -7140, -10580, -13940,  -9300, -5230,
-            -9590, -14120, -18520, -12290, -6860,
-            -9980, -14560, -18960, -12560, -7000,
-            -7518, -10904, -14144,  -9318, -5152,
-            -5032,  -7256,  -9376,  -6142, -3368,
-        })));
-
-    return SimpleConvolution2dTestImpl<T>(workloadFactory,
-        input,
-        kernel,
-        GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(false, qScale, qOffset),
-        expectedOutput,
-        qScale,
-        qOffset,
-        layout,
-        1,  // Padding left.
-        1,  // Padding top.
-        2,  // Padding right.
-        2); // Padding bottom.
-}
-
-template<typename T>
-LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                                 float qScale,
-                                                                 int32_t qOffset,
-                                                                 bool biasEnabled,
-                                                                 const armnn::DataLayoutIndexed& layout)
-{
-    // Use a single-batch 2-channel 5x5 image as input.
-    armnn::TensorInfo inputTensorInfo({ 1, 2, 5, 5 }, armnn::GetDataType<T>());
-    auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>(
-        QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), {
-             0,  1,  2,  3,  4,
-             5,  6,  7,  8,  9,
-            10, 11, 12, 13, 14,
-            15, 16, 17, 18, 19,
-            20, 21, 22, 23, 24,
-
-            25, 26, 27, 28, 29,
-            30, 31, 32, 33, 34,
-            35, 36, 37, 38, 39,
-            40, 41, 42, 43, 44,
-            45, 46, 47, 48, 49
-        })));
-
-    // Use a depth multiplier of 1 on a 2-channel 4x4 kernel.
-    armnn::TensorInfo kernelTensorInfo({ 1, 2, 4, 4 }, armnn::GetDataType<T>());
-    auto kernel = MakeTensor<T, 4>(kernelTensorInfo, std::vector<T>(
-        QuantizedVector<T>(kernelTensorInfo.GetQuantizationScale(), kernelTensorInfo.GetQuantizationOffset(), {
-            32, 31, 30, 29,
-            28, 27, 26, 25,
-            24, 23, 22, 21,
-            20, 19, 18, 17,
-
-            16, 15, 14, 13,
-            12, 11, 10,  9,
-             8,  7,  6,  5,
-             4,  3,  2,  1
-        })));
-
-    // Expected output is 1 batch of a 2-channel 5x5 image.
-    // Calculated using the python tensorflow library with strideX=1, strideY=1.
-    armnn::TensorInfo outputTensorInfo({ 1, 2, 5, 5 }, armnn::GetDataType<T>());
-    boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputTensorInfo, std::vector<T>(
-        QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), {
-            1062, 1580, 1850, 1530, 1117,
-            2140, 3108, 3500, 2842, 2042,
-            3580, 5068, 5460, 4342, 3062,
-            3618, 5072, 5390, 4248, 2971,
-            3074, 4282, 4510, 3533, 2457,
-            1550, 2284, 2362, 1955, 1428,
-            2910, 4206, 4342, 3528, 2536,
-            3390, 4886, 5022, 4068, 2916,
-            3566, 5056, 5182, 4133, 2922,
-            3100, 4352, 4452, 3517, 2465
-        })));
-
-    return DepthwiseConvolution2dAsymmetricTestImpl<T>(workloadFactory,
-        input,
-        kernel,
-        GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(biasEnabled, qScale, qOffset),
-        expectedOutput,
-        qScale,
-        qOffset,
-        layout,
-        1,  // Padding left.
-        1,  // Padding top.
-        2,  // Padding right.
-        2,  // Padding bottom.
-        1,  // strideX
-        1); // strideY
-}
-
-template<typename T>
-LayerTestResult<T, 4> DepthwiseConvolution2dNhwcTestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                           float qScale,
-                                                           int32_t qOffset,
-                                                           bool biasEnabled)
-{
-    armnn::TensorInfo inputTensorInfo({ 1, 5, 5, 2}, armnn::GetDataType<T>());
-    auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>(
-        QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), {
-            0, 25,
-            1, 26,
-            2, 27,
-            3, 28,
-            4, 29,
-
-            5, 30,
-            6, 31,
-            7, 32,
-            8, 33,
-            9, 34,
-
-            10, 35,
-            11, 36,
-            12, 37,
-            13, 38,
-            14, 39,
-
-            15, 40,
-            16, 41,
-            17, 42,
-            18, 43,
-            19, 44,
-
-            20, 45,
-            21, 46,
-            22, 47,
-            23, 48,
-            24, 49
-        })));
-
-    armnn::TensorInfo kernelTensorInfo({ 1, 4, 4, 2}, armnn::GetDataType<T>());
-    auto kernel = MakeTensor<T, 4>(kernelTensorInfo, std::vector<T>(
-        QuantizedVector<T>(kernelTensorInfo.GetQuantizationScale(), kernelTensorInfo.GetQuantizationOffset(), {
-             32, 16,
-             31, 15,
-             30, 14,
-             29, 13,
-
-             28, 12,
-             27, 11,
-             26, 10,
-             25,  9,
-
-             24,  8,
-             23,  7,
-             22,  6,
-             21,  5,
-
-             20,  4,
-             19,  3,
-             18,  2,
-             17,  1
-        })));
-
-    armnn::TensorInfo outputTensorInfo({ 1, 5, 5, 2}, armnn::GetDataType<T>());
-    boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputTensorInfo, std::vector<T>(
-        QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), {
-        1062, 1550,
-        1580, 2284,
-        1850, 2362,
-        1530, 1955,
-        1117, 1428,
-
-        2140, 2910,
-        3108, 4206,
-        3500, 4342,
-        2842, 3528,
-        2042, 2536,
-
-        3580, 3390,
-        5068, 4886,
-        5460, 5022,
-        4342, 4068,
-        3062, 2916,
-
-        3618, 3566,
-        5072, 5056,
-        5390, 5182,
-        4248, 4133,
-        2971, 2922,
-
-        3074, 3100,
-        4282, 4352,
-        4510, 4452,
-        3533, 3517,
-        2457, 2465
-        })));
-
-    return DepthwiseConvolution2dNhwcTestImpl<T>(workloadFactory,
-        input,
-        kernel,
-        GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(biasEnabled, qScale, qOffset),
-        expectedOutput,
-        qScale,
-        qOffset,
-        1,  // Padding left.
-        1,  // Padding top.
-        2,  // Padding right.
-        2,  // Padding bottom.
-        1,  // strideX
-        1);  // strideY
-}
-
-LayerTestResult<float, 4>
-Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest(armnn::IWorkloadFactory& workloadFactory,
-                                                           const armnn::DataLayoutIndexed& layout)
-{
-    return Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon<float>(workloadFactory, layout, 0.0f, 0);
-}
-
-LayerTestResult<float, 4> Convolution2dAsymmetricPaddingTest(armnn::IWorkloadFactory& workloadFactory,
-                                                             const armnn::DataLayoutIndexed& layout)
-{
-    return SimpleConvolution2dAsymmetricPaddingTestCommon<float>(workloadFactory, layout, 0.0f, 0);
-}
-
-LayerTestResult<float, 4> DepthwiseConvolution2dTest(armnn::IWorkloadFactory& workloadFactory,
-                                                     bool                     biasEnabled,
-                                                     const armnn::DataLayoutIndexed& layout)
-{
-    return DepthwiseConvolution2dTestImpl<float, float>(workloadFactory, 0.0f, 0, biasEnabled, layout);
-}
-
-LayerTestResult<float, 4> DepthwiseConvolution2dDepthNhwcTest(armnn::IWorkloadFactory& workloadFactory,
-                                                              bool biasEnabled)
-{
-    return DepthwiseConvolution2dNhwcTestCommon<float>(workloadFactory, 0.0f, 0, biasEnabled);
-}
-
-LayerTestResult<float, 4> DepthwiseConvolution2dDepthMul1Test(armnn::IWorkloadFactory& workloadFactory,
-                                                              bool biasEnabled,
-                                                              const armnn::DataLayoutIndexed& layout)
-{
-    return DepthwiseConvolution2dDepthMul1TestImpl<float, float>(workloadFactory, 0.0f, 0, biasEnabled, layout);
-}
-
-LayerTestResult<float, 4> DepthwiseConvolution2dAsymmetricTest(armnn::IWorkloadFactory& workloadFactory,
-                                                               bool biasEnabled,
-                                                               const armnn::DataLayoutIndexed& layout)
-{
-    return DepthwiseConvolution2dAsymmetricTestCommon<float>(workloadFactory, 0.0f, 0, biasEnabled, layout);
-}
-
-LayerTestResult<uint8_t, 4> DepthwiseConvolution2dUint8Test(armnn::IWorkloadFactory& workloadFactory,
-                                                            bool                     biasEnabled,
-                                                            const armnn::DataLayoutIndexed& layout)
-{
-    return DepthwiseConvolution2dTestImpl<uint8_t, int32_t>(workloadFactory, 0.5f, 50, biasEnabled, layout);
-}
-
-LayerTestResult<uint8_t, 4> DepthwiseConvolution2dDepthMul1Uint8Test(armnn::IWorkloadFactory& workloadFactory,
-                                                                     bool biasEnabled,
-                                                                     const armnn::DataLayoutIndexed& layout)
-{
-    return DepthwiseConvolution2dDepthMul1TestImpl<uint8_t, int32_t>(workloadFactory, 0.5f, 50, biasEnabled, layout);
-}
-
-LayerTestResult<float, 4> Convolution1dTest(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled)
-{
-    return Convolution1dTestImpl<float>(workloadFactory, 0.0f, 0, biasEnabled);
-}
-
-LayerTestResult<uint8_t, 4> Convolution1dUint8Test(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled)
-{
-    return Convolution1dTestImpl<uint8_t>(workloadFactory, 0.1f, 128, biasEnabled);
-}
-
-LayerTestResult<float,4> CompareConvolution2dTest(armnn::IWorkloadFactory& workloadFactory,
-                                                  armnn::IWorkloadFactory& refWorkloadFactory)
-{
-    return CompareConvolution2dTestImpl<float>(workloadFactory, refWorkloadFactory);
-}
-
-template<typename T>
-LayerTestResult<T,4> CompareDepthwiseConvolution2dTest(armnn::IWorkloadFactory& workloadFactory,
-                                                       armnn::IWorkloadFactory& refWorkloadFactory,
-                                                       const armnn::DataLayoutIndexed& layout)
-{
-    return CompareDepthwiseConvolution2dTestImpl<T>(workloadFactory, refWorkloadFactory, layout);
-}
-
-template LayerTestResult<float, 4> CompareDepthwiseConvolution2dTest<float>(
-    armnn::IWorkloadFactory&, armnn::IWorkloadFactory&, const armnn::DataLayoutIndexed&);
-template LayerTestResult<uint8_t, 4> CompareDepthwiseConvolution2dTest<uint8_t>(
-    armnn::IWorkloadFactory&, armnn::IWorkloadFactory&, const armnn::DataLayoutIndexed&);
-
-LayerTestResult<float,4> SimpleNormalizationAcrossTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness;
-    auto normChannel = armnn::NormalizationAlgorithmChannel::Across;
-    return SimpleNormalizationTestImpl(workloadFactory, normChannel, normMethod);
-}
-
-LayerTestResult<float,4> SimpleNormalizationWithinTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness;
-    auto normChannel = armnn::NormalizationAlgorithmChannel::Within;
-    return SimpleNormalizationTestImpl(workloadFactory, normChannel, normMethod);
-}
-
-LayerTestResult<float,4> SimpleNormalizationAcrossNhwcTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness;
-    auto normChannel = armnn::NormalizationAlgorithmChannel::Across;
-    return SimpleNormalizationNhwcTestImpl(workloadFactory, normChannel, normMethod);
-}
-
-LayerTestResult<float,2> SimpleSoftmaxTest(armnn::IWorkloadFactory& workloadFactory, float beta)
-{
-    return SimpleSoftmaxTestImpl<float>(workloadFactory, beta);
-}
-
-LayerTestResult<uint8_t,2> SimpleSoftmaxUint8Test(armnn::IWorkloadFactory& workloadFactory, float beta)
-{
-    return SimpleSoftmaxTestImpl<uint8_t>(workloadFactory, beta);
-}
-
-LayerTestResult<float,4> CompareNormalizationTest(armnn::IWorkloadFactory& workloadFactory,
-                                                  armnn::IWorkloadFactory& refWorkloadFactory,
-                                                  armnn::NormalizationAlgorithmChannel normChannel,
-                                                  armnn::NormalizationAlgorithmMethod normMethod)
-{
-    return CompareNormalizationTestImpl(workloadFactory, refWorkloadFactory, normChannel, normMethod);
-}
-
-LayerTestResult<float,2> CompareSoftmaxTest(armnn::IWorkloadFactory& workloadFactory,
-    armnn::IWorkloadFactory& refWorkloadFactory,
-    float beta)
-{
-    return CompareSoftmaxTestImpl<float>(workloadFactory, refWorkloadFactory, beta);
-}
-
-LayerTestResult<uint8_t,2> CompareSoftmaxUint8Test(armnn::IWorkloadFactory& workloadFactory,
-    armnn::IWorkloadFactory& refWorkloadFactory,
-    float beta)
-{
-    return CompareSoftmaxTestImpl<uint8_t>(workloadFactory, refWorkloadFactory, beta);
-}
-
-std::vector<LayerTestResult<float,3>> SplitterTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    return SplitterTestCommon<float>(workloadFactory);
-}
-
-std::vector<LayerTestResult<uint8_t,3>> SplitterUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return SplitterTestCommon<uint8_t>(workloadFactory, 1.0f, 0);
-}
-
-LayerTestResult<float, 3> CopyViaSplitterTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    return CopyViaSplitterTestImpl<float>(workloadFactory, 0.0f, 0);
-}
-
-LayerTestResult<uint8_t, 3> CopyViaSplitterUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return CopyViaSplitterTestImpl<uint8_t>(workloadFactory, 1.0f, 0);
-}
-
-LayerTestResult<float, 2> LstmLayerFloat32WithCifgWithPeepholeNoProjectionTest(
-        armnn::IWorkloadFactory& workloadFactory)
-{
-    armnn::TensorInfo inputDesc({ 2, 2 }, armnn::GetDataType<float>());
-    boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>(
-            { 2., 3., 3., 4. }));
-
-    armnn::TensorInfo outputDesc({ 2, 4 }, armnn::GetDataType<float>());
-    boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(outputDesc, std::vector<float>(
-            {-0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f,
-             -0.42734814f, -0.00478661f,  0.13455015f, -0.03560682f}));
-    return LstmLayerWithCifgWithPeepholeNoProjectionTestImpl(workloadFactory, input, expectedOutput);
-}
-
-LayerTestResult<float, 2> LstmLayerFloat32NoCifgWithPeepholeWithProjectionTest(
-        armnn::IWorkloadFactory& workloadFactory)
-{
-    armnn::TensorInfo inputDesc({ 2, 5 }, armnn::GetDataType<float>());
-    boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>(
-            {0.787926f, 0.151646f, 0.071352f, 0.118426f, 0.458058f,
-             0.295743f, 0.544053f, 0.690064f, 0.858138f, 0.497181f}));
-
-    armnn::TensorInfo outputDesc({ 2, 16 }, armnn::GetDataType<float>());
-    boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(outputDesc, std::vector<float>(
-            {-0.00396806f, 0.029352f,     -0.00279226f, 0.0159977f,   -0.00835576f,
-             -0.0211779f,  0.0283512f,    -0.0114597f,  0.00907307f,  -0.0244004f,
-             -0.0152191f,  -0.0259063f,   0.00914318f,  0.00415118f,  0.017147f,
-             0.0134203f, -0.013869f,    0.0287268f,   -0.00334693f, 0.00733398f,  -0.0287926f,
-             -0.0186926f,   0.0193662f,   -0.0115437f,  0.00422612f,  -0.0345232f,
-             0.00223253f,   -0.00957321f, 0.0210624f,   0.013331f,    0.0150954f,
-             0.02168f}));
-    return LstmLayerFloat32NoCifgWithPeepholeWithProjectionTestImpl(workloadFactory, input, expectedOutput);
-}
-
-LayerTestResult<float, 2> LstmLayerFloat32NoCifgNoPeepholeNoProjectionTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    armnn::TensorInfo inputDesc({2, 2}, armnn::GetDataType<float>());
-    boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>(
-            {2., 3., 3., 4.}));
-
-
-    armnn::TensorInfo outputDesc({2, 4}, armnn::GetDataType<float>());
-    boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(outputDesc, std::vector<float>(
-            {{-0.02973187f, 0.1229473f,   0.20885126f, -0.15358765f,
-              -0.0185422f,   0.11281417f,  0.24466537f, -0.1826292f}}));
-
-    return LstmNoCifgNoPeepholeNoProjectionTestImpl(workloadFactory, input, expectedOutput);
-}
-
-LayerTestResult<float,3> MergerTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    unsigned int outputWidth = 3;
-    unsigned int outputHeight = 6;
-    unsigned int outputChannels = 3;
-
-    unsigned int inputWidth1 = 3;
-    unsigned int inputHeight1 = 6;
-    unsigned int inputChannels1 = 2;
-
-    unsigned int inputWidth2 = 3;
-    unsigned int inputHeight2 = 6;
-    unsigned int inputChannels2 = 1;
-
-    // Define the tensor descriptors.
-    armnn::TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, armnn::DataType::Float32);
-    armnn::TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, armnn::DataType::Float32);
-    armnn::TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, armnn::DataType::Float32);
-
-    LayerTestResult<float,3> ret(outputTensorInfo);
-
-    ret.outputExpected = MakeTensor<float, 3>(outputTensorInfo, std::vector<float>(
-    {
-            1.0f, 2.0f, 3.0f,
-            4.0f, 5.0f, 6.0f,
-            7.0f, 8.0f, 9.0f,
-            10.0f, 11.0f, 12.0f,
-            13.0f, 14.0f, 15.0f,
-            16.0f, 17.0f, 18.0f,
-
-            19.0f, 20.0f, 21.0f,
-            22.0f, 23.0f, 24.0f,
-            25.0f, 26.0f, 27.0f,
-            28.0f, 29.0f, 30.0f,
-            31.0f, 32.0f, 33.0f,
-            34.0f, 35.0f, 36.0f,
-
-            37.0f, 38.0f, 39.0f,
-            40.0f, 41.0f, 42.0f,
-            43.0f, 44.0f, 45.0f,
-            46.0f, 47.0f, 48.0f,
-            49.0f, 50.0f, 51.0f,
-            52.0f, 53.0f, 54.0f,
-        })
-    );
-
-    auto input1 = MakeTensor<float, 3>(inputTensorInfo1, std::vector<float>(
-        {
-            1.0f, 2.0f, 3.0f,
-            4.0f, 5.0f, 6.0f,
-            7.0f, 8.0f, 9.0f,
-            10.0f, 11.0f, 12.0f,
-            13.0f, 14.0f, 15.0f,
-            16.0f, 17.0f, 18.0f,
-
-            19.0f, 20.0f, 21.0f,
-            22.0f, 23.0f, 24.0f,
-            25.0f, 26.0f, 27.0f,
-            28.0f, 29.0f, 30.0f,
-            31.0f, 32.0f, 33.0f,
-            34.0f, 35.0f, 36.0f,
-        })
-    );
-
-    auto input2 = MakeTensor<float, 3>(inputTensorInfo2, std::vector<float>(
-        {
-            37.0f, 38.0f, 39.0f,
-            40.0f, 41.0f, 42.0f,
-            43.0f, 44.0f, 45.0f,
-            46.0f, 47.0f, 48.0f,
-            49.0f, 50.0f, 51.0f,
-            52.0f, 53.0f, 54.0f,
-        })
-    );
-
-    std::vector<unsigned int> wOrigin1 = {0, 0, 0}; //Extent of the window is defined by size of input[0].
-    armnn::MergerQueueDescriptor::ViewOrigin window1(wOrigin1);
-
-    std::vector<unsigned int> wOrigin2 = {2, 0, 0}; //Extent of the window is defined by size of input[1].
-    armnn::MergerQueueDescriptor::ViewOrigin window2(wOrigin2);
-
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    bool subTensorsSupported = workloadFactory.SupportsSubTensors();
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle1 =
-        subTensorsSupported ?
-            workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) :
-            workloadFactory.CreateTensorHandle(inputTensorInfo1);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle2  =
-        subTensorsSupported ?
-            workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) :
-            workloadFactory.CreateTensorHandle(inputTensorInfo2);
-
-    armnn::MergerQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
-    AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    data.m_ViewOrigins.push_back(window1);
-    data.m_ViewOrigins.push_back(window2);
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMerger(data, info);
-
-    inputHandle1->Allocate();
-    inputHandle2->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]);
-    CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get());
-
-    return ret;
-}
-
-LayerTestResult<float,4> AdditionTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    unsigned int batchSize = 2;
-    unsigned int channels  = 2;
-    unsigned int height    = 2;
-    unsigned int width     = 3;
-
-    armnn::TensorInfo inputTensorInfo1, inputTensorInfo2;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int shape[] = {batchSize, channels, height, width};
-
-    inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
-    inputTensorInfo2 = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
-    outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
-
-
-    auto input1 = MakeTensor<float, 4>(inputTensorInfo1, std::vector<float>(
-        {
-            0.0f, 2.0f, 1.0f,
-            0.2f, 1.0f, 2.0f,
-
-            1.0f, 2.0f, 1.0f,
-            0.2f, 1.0f, 2.0f,
-
-            0.0f, 2.0f, 1.0f,
-            4.2f, 1.0f, 2.0f,
-
-            0.0f, 0.0f, 1.0f,
-            0.2f, 1.0f, 2.0f,
-        }));
-
-    auto input2 = MakeTensor<float, 4>(inputTensorInfo2, std::vector<float>(
-        {
-            1.0f, 2.0f, 1.0f,
-            0.0f, 1.0f, 2.0f,
-
-            1.0f, 2.0f, -2.0f,
-            0.2f, 1.0f, 2.0f,
-
-            0.0f, 2.0f, 1.0f,
-            4.2f, 0.0f, -3.0f,
-
-            0.0f, 0.0f, 1.0f,
-            0.7f, 1.0f, 5.0f,
-        }));
-
-    LayerTestResult<float,4> ret(outputTensorInfo);
-    ret.outputExpected = MakeTensor<float, 4>(outputTensorInfo, std::vector<float>(
-        {
-            1.0f, 4.0f, 2.0f,
-            0.2f, 2.0f, 4.0f,
-
-            2.0f, 4.0f, -1.0f,
-            0.4f, 2.0f, 4.0f,
-
-            0.0f, 4.0f, 2.0f,
-            8.4f, 1.0f, -1.0f,
-
-            0.0f, 0.0f, 2.0f,
-            0.9f, 2.0f, 7.0f,
-        }));
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
-    std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::AdditionQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
-    AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info);
-
-    inputHandle1->Allocate();
-    inputHandle2->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
-    CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-
-    return ret;
-}
-
-template <typename T>
-LayerTestResult<T, 4> AdditionBroadcastTestImpl(armnn::IWorkloadFactory& workloadFactory,
-    float qScale,
-    int32_t qOffset)
-{
-    armnn::TensorInfo inputTensorInfo1 = armnn::TensorInfo({1, 3, 2, 1}, armnn::GetDataType<T>());
-    armnn::TensorInfo inputTensorInfo2 = armnn::TensorInfo({1, 1, 2, 3}, armnn::GetDataType<T>());
-    armnn::TensorInfo outputTensorInfo = armnn::TensorInfo({1, 3, 2, 3}, armnn::GetDataType<T>());
-
-    if (armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo1.SetQuantizationScale(qScale);
-        inputTensorInfo1.SetQuantizationOffset(qOffset);
-        inputTensorInfo2.SetQuantizationScale(qScale);
-        inputTensorInfo2.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(qScale, qOffset,
-        {
-            0.0f,
-            1.0f,
-
-            2.0f,
-            3.0f,
-
-            4.0f,
-            5.0f,
-        }));
-
-    auto input2 = MakeTensor<T, 4>(inputTensorInfo2, QuantizedVector<T>(qScale, qOffset,
-        {
-            0.5f, 1.5f, 2.5f,
-            3.5f, 4.5f, 5.5f,
-        }));
-
-    LayerTestResult<T,4> ret(outputTensorInfo);
-    ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset,
-        {
-            0.5f, 1.5f, 2.5f,
-            4.5f, 5.5f, 6.5f,
-
-            2.5f, 3.5f, 4.5f,
-            6.5f, 7.5f, 8.5f,
-
-            4.5f, 5.5f, 6.5f,
-            8.5f, 9.5f, 10.5f,
-        }));
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
-    std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::AdditionQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
-    AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info);
-
-    inputHandle1->Allocate();
-    inputHandle2->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
-    CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-
-    return ret;
-}
-
-template <typename T>
-LayerTestResult<T, 4> AdditionBroadcast1ElementTestImpl(armnn::IWorkloadFactory& workloadFactory,
-    float qScale,
-    int32_t qOffset)
-{
-    armnn::TensorInfo inputTensorInfo1 = armnn::TensorInfo({1, 3, 2, 3}, armnn::GetDataType<T>());
-    armnn::TensorInfo inputTensorInfo2 = armnn::TensorInfo({1, 1, 1, 1}, armnn::GetDataType<T>());
-    armnn::TensorInfo outputTensorInfo = armnn::TensorInfo({1, 3, 2, 3}, armnn::GetDataType<T>());
-
-    if (armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo1.SetQuantizationScale(qScale);
-        inputTensorInfo1.SetQuantizationOffset(qOffset);
-        inputTensorInfo2.SetQuantizationScale(qScale);
-        inputTensorInfo2.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(qScale, qOffset,
-        {
-             0.0f,  1.0f,  2.0f,
-             3.0f,  4.0f,  5.0f,
-             6.0f,  7.0f,  8.0f,
-             9.0f, 10.0f, 11.0f,
-            12.0f, 13.0f, 14.0f,
-            15.0f, 16.0f, 17.0f,
-        }));
-
-    auto input2 = MakeTensor<T, 4>(inputTensorInfo2, QuantizedVector<T>(qScale, qOffset,
-        {
-            0.5f,
-        }));
-
-    LayerTestResult<T,4> ret(outputTensorInfo);
-    ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset,
-        {
-             0.5f,  1.5f,  2.5f,
-             3.5f,  4.5f,  5.5f,
-             6.5f,  7.5f,  8.5f,
-             9.5f, 10.5f, 11.5f,
-            12.5f, 13.5f, 14.5f,
-            15.5f, 16.5f, 17.5f,
-        }));
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
-    std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::AdditionQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
-    AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info);
-
-    inputHandle1->Allocate();
-    inputHandle2->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
-    CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-
-    return ret;
-}
-
-LayerTestResult<float, 4> AdditionBroadcastTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    return AdditionBroadcastTestImpl<float>(workloadFactory, 0.0f, 0);
-}
-
-LayerTestResult<uint8_t, 4> AdditionBroadcastUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return AdditionBroadcastTestImpl<uint8_t>(workloadFactory, 2.f, 0);
-}
-
-LayerTestResult<float, 4> AdditionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    return AdditionBroadcast1ElementTestImpl<float>(workloadFactory, 0.0f, 0);
-}
-
-LayerTestResult<uint8_t, 4> AdditionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return AdditionBroadcast1ElementTestImpl<uint8_t>(workloadFactory, 0.1333333f, 128);
-}
-
-LayerTestResult<float,4> CompareAdditionTest(armnn::IWorkloadFactory& workloadFactory,
-                                             armnn::IWorkloadFactory& refWorkloadFactory)
-{
-    unsigned int batchSize = 4;
-    unsigned int channels  = 1;
-    unsigned int height    = 2;
-    unsigned int width     = 3;
-
-    armnn::TensorInfo inputTensorInfo1, inputTensorInfo2;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int shape[] = {batchSize, channels, height, width};
-
-    inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
-    inputTensorInfo2 = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
-    outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
-
-    auto input1 = MakeRandomTensor<float, 4>(inputTensorInfo1, 1232);
-    auto input2 = MakeRandomTensor<float, 4>(inputTensorInfo2, 456);
-
-    LayerTestResult<float,4> ret(outputTensorInfo);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
-    std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle1Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo1);
-    std::unique_ptr<armnn::ITensorHandle> inputHandle2Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo2);
-    std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::AdditionQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
-    AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    armnn::AdditionQueueDescriptor refData = data;
-    armnn::WorkloadInfo refInfo = info;
-    SetWorkloadInput(refData, refInfo, 0, inputTensorInfo1, inputHandle1Ref.get());
-    SetWorkloadInput(refData, refInfo, 1, inputTensorInfo2, inputHandle2Ref.get());
-    SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info);
-    std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateAddition(refData, refInfo);
-
-    inputHandle1->Allocate();
-    inputHandle2->Allocate();
-    outputHandle->Allocate();
-    inputHandle1Ref->Allocate();
-    inputHandle2Ref->Allocate();
-    outputHandleRef->Allocate();
-
-    CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
-    CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]);
-    CopyDataToITensorHandle(inputHandle1Ref.get(), &input1[0][0][0][0]);
-    CopyDataToITensorHandle(inputHandle2Ref.get(), &input2[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-    refWorkloadFactory.Finalize();
-    workloadRef->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-    CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get());
-
-    return ret;
-}
-
-namespace {
-template <typename T>
-LayerTestResult<T, 4> DivisionTestHelper(armnn::IWorkloadFactory& workloadFactory,
-                                         const unsigned int shape0[4],
-                                         const std::vector<T>& values0,
-                                         float scale0,
-                                         int32_t offset0,
-                                         const unsigned int shape1[4],
-                                         const std::vector<T> & values1,
-                                         float scale1,
-                                         int32_t offset1,
-                                         const unsigned int outShape[4],
-                                         const std::vector<T> & outValues,
-                                         float outScale,
-                                         int32_t outOffset)
-{
-    auto dataType = (std::is_same<T, uint8_t>::value ?
-                     armnn::DataType::QuantisedAsymm8 :
-                     armnn::DataType::Float32);
-
-    armnn::TensorInfo inputTensorInfo0(4, shape0, dataType);
-    armnn::TensorInfo inputTensorInfo1(4, shape1, dataType);
-    armnn::TensorInfo outputTensorInfo(4, outShape, dataType);
-
-    inputTensorInfo0.SetQuantizationScale(scale0);
-    inputTensorInfo0.SetQuantizationOffset(offset0);
-
-    inputTensorInfo1.SetQuantizationScale(scale1);
-    inputTensorInfo1.SetQuantizationOffset(offset1);
-
-    outputTensorInfo.SetQuantizationScale(outScale);
-    outputTensorInfo.SetQuantizationOffset(outOffset);
-
-    auto input0 = MakeTensor<T, 4>(inputTensorInfo0, values0);
-    auto input1 = MakeTensor<T, 4>(inputTensorInfo1, values1);
-
-    LayerTestResult<T, 4> result(outputTensorInfo);
-    result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outValues);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0);
-    std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::DivisionQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data,  info, inputTensorInfo0, inputHandle0.get());
-    AddInputToWorkload(data,  info, inputTensorInfo1, inputHandle1.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDivision(data, info);
-
-    inputHandle0->Allocate();
-    inputHandle1->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]);
-    CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-
-    return result;
-}
-} // anonymous namespace
-
-LayerTestResult<float,4> DivisionByZeroTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    const unsigned int width = 2;
-    const unsigned int height = 2;
-    const unsigned int channelCount = 2;
-    const unsigned int batchSize = 2;
-
-    unsigned int shape[] = { batchSize, channelCount, height, width };
-
-    std::vector<float> input0({
-                                1.f,  1.f,  1.f,  1.f,  0.f, 0.f, 0.f, 0.f,
-                               -1.f, -1.f, -1.f, -1.f,  5.f, 5.f, 5.f, 5.f });
-
-    std::vector<float> input1({
-                               0.f, 0.f, -0.f, -0.f,  0.f, 0.f, -0.f, -0.f,
-                               0.f, 0.f, -0.f, -0.f,  5.f, 5.f,  5.f,  5.f });
-
-    std::vector<float> output({
-                               INFINITY, INFINITY, -INFINITY, -INFINITY,  NAN, NAN, -NAN, -NAN,
-                               -INFINITY, -INFINITY, INFINITY, INFINITY,  1, 1, 1, 1 });
-
-    return DivisionTestHelper<float>(workloadFactory,
-                                     shape, input0, 1.0f, 0,
-                                     shape, input1, 1.0f, 0,
-                                     shape, output, 1.0f, 0);
-}
-
-LayerTestResult<float,4> DivisionTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    const unsigned int width = 2;
-    const unsigned int height = 2;
-    const unsigned int channelCount = 2;
-    const unsigned int batchSize = 2;
-
-    unsigned int shape[] = { batchSize, channelCount, height, width };
-
-    std::vector<float> input0({
-                                      2,  2,  2,  2,    3,  3,  3,  3,
-                                      4,  4,  4,  4,    5,  5,  5,  5 });
-
-    std::vector<float> input1({
-                                      1,  1,  1,  1,    2,  2,  2,  2,
-                                      4,  4,  4,  4,    4,  4,  4,  4 });
-
-    std::vector<float> output({
-                                      2,  2,  2,  2,    1.5,  1.5,  1.5,  1.5,
-                                      1, 1, 1, 1,  1.25, 1.25, 1.25, 1.25 });
-
-
-    return DivisionTestHelper<float>(workloadFactory,
-                                     shape, input0, 1.0f, 0,
-                                     shape, input1, 1.0f, 0,
-                                     shape, output, 1.0f, 0);
-}
-
-LayerTestResult<float, 4> DivisionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    unsigned int shape0[] = { 1, 2, 2, 2 };
-    std::vector<float> input0({ 2, 4, 6, 8, 10, 12, 14, 16});
-
-    unsigned int shape1[] = { 1, 1, 1, 1 };
-    std::vector<float> input1({ 2 });
-
-    std::vector<float> output({ 1, 2, 3, 4, 5, 6, 7, 8});
-
-
-    return DivisionTestHelper<float>(workloadFactory,
-                                     shape0, input0, 1.0f, 0,
-                                     shape1, input1, 1.0f, 0,
-                                     shape0, output, 1.0f, 0);
-}
-
-LayerTestResult<float, 4> DivisionBroadcast1DVectorTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    unsigned int shape0[] = { 1, 3, 3, 2 };
-    std::vector<float> input0({
-                                      1,   4,       3,  8,      5, 12,
-                                      7,   16,      9, 20,     11, 24,
-                                      13,  28,     15, 32,     17, 36});
-
-    unsigned int shape1[] = { 1, 1, 1, 2 };
-    std::vector<float> input1({ 1, 2 });
-
-    std::vector<float> output({
-                                      1,   2,      3,  4,      5,  6,
-                                      7,   8,      9, 10,     11, 12,
-                                      13, 14,     15, 16,     17, 18});
-
-    return DivisionTestHelper<float>(workloadFactory,
-                                     shape0, input0, 1.0f, 0,
-                                     shape1, input1, 1.0f, 0,
-                                     shape0, output, 1.0f, 0);
-}
-
-
-LayerTestResult<uint8_t,4> DivisionUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    const unsigned int width = 2;
-    const unsigned int height = 2;
-    const unsigned int channelCount = 2;
-    const unsigned int batchSize = 2;
-
-    unsigned int shape[] = { batchSize, channelCount, height, width };
-
-    std::vector<uint8_t> input0({2,  2,  2,  2,    3,  3,  3,  3,
-                                 4,  4,  4,  4,    5,  5,  5,  5 });
-
-    std::vector<uint8_t> input1({1,  1,  1,  1,    2,  2,  2,  2,
-                                 4,  4,  4,  4,    4,  4,  4,  4 });
-
-    std::vector<uint8_t> output({8,  8,  8,  8,    6,  6,  6,  6,
-                                 4,  4,  4,  4,    5,  5,  5,  5});
-
-
-    return DivisionTestHelper<uint8_t>(workloadFactory,
-                                     shape, input0, 1.0f,  0,
-                                     shape, input1, 1.0f,  0,
-                                     shape, output, 0.25f, 0);
-}
-
-LayerTestResult<uint8_t, 4> DivisionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    unsigned int shape0[] = { 1, 2, 2, 2 };
-    std::vector<uint8_t> input0({ 2, 4, 6, 8, 10, 12, 14, 16});
-
-    unsigned int shape1[] = { 1, 1, 1, 1 };
-    std::vector<uint8_t> input1({ 2 });
-
-    std::vector<uint8_t> output({ 1, 2, 3, 4, 5, 6, 7, 8});
-
-    return DivisionTestHelper<uint8_t>(workloadFactory,
-                                     shape0, input0, 1.0f, 0,
-                                     shape1, input1, 1.0f, 0,
-                                     shape0, output, 1.0f, 0);
-}
-
-LayerTestResult<uint8_t, 4> DivisionBroadcast1DVectorUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    unsigned int shape0[] = { 1, 3, 3, 2 };
-    std::vector<uint8_t> input0({1,   4,     3,  8,      5,  12,
-                                 7,   16,    9,  20,     11, 24,
-                                 13,  28,    15, 32,     17, 36});
-
-    unsigned int shape1[] = { 1, 1, 1, 2 };
-    std::vector<uint8_t> input1({ 1, 2 });
-
-    std::vector<uint8_t> output({1,   2,      3,  4,      5,  6,
-                                 7,   8,      9, 10,     11, 12,
-                                 13, 14,     15, 16,     17, 18});
-
-    return DivisionTestHelper<uint8_t>(workloadFactory,
-                                     shape0, input0, 1.0f, 0,
-                                     shape1, input1, 1.0f, 0,
-                                     shape0, output, 1.0f, 0);
-}
-
-namespace {
-LayerTestResult<float,4> MultiplicationTestHelper(armnn::IWorkloadFactory& workloadFactory,
-                                                  const unsigned int shape0[4],
-                                                  const std::vector<float> & values0,
-                                                  const unsigned int shape1[4],
-                                                  const std::vector<float> & values1,
-                                                  const unsigned int outShape[4],
-                                                  const std::vector<float> & outValues)
-{
-    const size_t dimensionCount = 4;
-    armnn::TensorInfo inputTensorInfo0{dimensionCount, shape0, armnn::DataType::Float32};
-    armnn::TensorInfo inputTensorInfo1{dimensionCount, shape1, armnn::DataType::Float32};
-    armnn::TensorInfo outputTensorInfo{dimensionCount, outShape, armnn::DataType::Float32};
-
-    auto input0 = MakeTensor<float, 4>(inputTensorInfo0, values0);
-    auto input1 = MakeTensor<float, 4>(inputTensorInfo1, values1);
-
-    LayerTestResult<float,4> ret(outputTensorInfo);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0);
-    std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::MultiplicationQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get());
-    AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMultiplication(data, info);
-
-    inputHandle0->Allocate();
-    inputHandle1->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]);
-    CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-
-    ret.outputExpected = MakeTensor<float, 4>(outputTensorInfo, outValues);
-    return ret;
-}
-} // anonymous namespace
-
-
-LayerTestResult<float,4> MultiplicationTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    const unsigned int width = 2;
-    const unsigned int height = 2;
-    const unsigned int channelCount = 2;
-    const unsigned int batchSize = 2;
-
-    unsigned int shape[] = { batchSize, channelCount, height, width };
-
-    std::vector<float> input0({
-        1,  1,  1,  1,    2,  2,  2,  2,
-        3,  3,  3,  3,    4,  4,  4,  4 });
-
-    std::vector<float> input1({
-        2,  2,  2,  2,    3,  3,  3,  3,
-        4,  4,  4,  4,    5,  5,  5,  5 });
-
-    std::vector<float> output({
-        2,  2,  2,  2,    6,  6,  6,  6,
-        12, 12, 12, 12,  20, 20, 20, 20 });
-
-    return MultiplicationTestHelper(workloadFactory,
-                                    shape,
-                                    input0,
-                                    shape,
-                                    input1,
-                                    shape,
-                                    output);
-}
-
-LayerTestResult<float, 4> MultiplicationBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    unsigned int shape0[] = { 1, 2, 2, 2 };
-    std::vector<float> input0({ 1, 2, 3, 4, 5, 6, 7, 8});
-
-    unsigned int shape1[] = { 1, 1, 1, 1 };
-    std::vector<float> input1({ 2 });
-
-    std::vector<float> output({ 2, 4, 6, 8, 10, 12, 14, 16});
-
-    return MultiplicationTestHelper(workloadFactory,
-                                    shape0,
-                                    input0,
-                                    shape1,
-                                    input1,
-                                    shape0,
-                                    output);
-}
-
-LayerTestResult<float, 4> MultiplicationBroadcast1DVectorTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    unsigned int shape0[] = { 1, 3, 3, 2 };
-    std::vector<float> input0({
-        1,   2,      3,  4,      5,  6,
-        7,   8,      9, 10,     11, 12,
-        13, 14,     15, 16,     17, 18});
-
-    unsigned int shape1[] = { 1, 1, 1, 2 };
-    std::vector<float> input1({ 1, 2 });
-
-    std::vector<float> output({
-        1,   4,       3,  8,      5, 12,
-        7,   16,      9, 20,     11, 24,
-        13,  28,     15, 32,     17, 36});
-
-    return MultiplicationTestHelper(workloadFactory,
-                                    shape0,
-                                    input0,
-                                    shape1,
-                                    input1,
-                                    shape0,
-                                    output);
-}
-
-LayerTestResult<float,4> CompareMultiplicationTest(armnn::IWorkloadFactory& workloadFactory,
-                                          armnn::IWorkloadFactory& refWorkloadFactory)
-{
-    const unsigned int width = 16;
-    const unsigned int height = 32;
-    const unsigned int channelCount = 2;
-    const unsigned int batchSize = 5;
-
-    armnn::TensorInfo inputTensorInfo0;
-    armnn::TensorInfo inputTensorInfo1;
-    armnn::TensorInfo outputTensorInfo;
-
-    constexpr unsigned int shape[] = { batchSize, channelCount, height, width };
-
-    inputTensorInfo0 = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
-    inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
-    outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
-
-    LayerTestResult<float,4> comparisonResult(outputTensorInfo);
-
-    auto input0 = MakeRandomTensor<float, 4>(inputTensorInfo0, 803506992);
-    auto input1 = MakeRandomTensor<float, 4>(inputTensorInfo1, 54902257);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0);
-    std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle0Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo0);
-    std::unique_ptr<armnn::ITensorHandle> inputHandle1Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo1);
-    std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::MultiplicationQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get());
-    AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    armnn::MultiplicationQueueDescriptor refData = data;
-    armnn::WorkloadInfo refInfo = info;
-    SetWorkloadInput(refData, refInfo, 0, inputTensorInfo0, inputHandle0Ref.get());
-    SetWorkloadInput(refData, refInfo, 1, inputTensorInfo1, inputHandle1Ref.get());
-    SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMultiplication(data, info);
-    std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateMultiplication(refData, refInfo);
-
-    inputHandle0->Allocate();
-    inputHandle1->Allocate();
-    outputHandle->Allocate();
-    inputHandle0Ref->Allocate();
-    inputHandle1Ref->Allocate();
-    outputHandleRef->Allocate();
-
-    CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]);
-    CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
-    CopyDataToITensorHandle(inputHandle0Ref.get(), &input0[0][0][0][0]);
-    CopyDataToITensorHandle(inputHandle1Ref.get(), &input1[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-    refWorkloadFactory.Finalize();
-    workloadRef->Execute();
-
-    CopyDataFromITensorHandle(&comparisonResult.output[0][0][0][0], outputHandle.get());
-    CopyDataFromITensorHandle(&comparisonResult.outputExpected[0][0][0][0], outputHandleRef.get());
-
-    return comparisonResult;
-}
-
-LayerTestResult<float,4> CompareBatchNormTest(armnn::IWorkloadFactory& workloadFactory,
-                                     armnn::IWorkloadFactory& refWorkloadFactory)
-{
-    const unsigned int width     = 2;
-    const unsigned int height    = 3;
-    const unsigned int channels  = 5;
-    const unsigned int batchSize = 3;
-
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-    armnn::TensorInfo tensorInfo;
-
-    constexpr unsigned int shape[]       = {batchSize, channels, height, width};
-    constexpr unsigned int tensorShape[] = {channels};
-
-    inputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
-    outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
-    tensorInfo = armnn::TensorInfo(1, tensorShape, armnn::DataType::Float32);
-
-    auto input = MakeRandomTensor<float, 4>(inputTensorInfo, 21312);
-
-    auto mean     = MakeRandomTensor<float, 1>(tensorInfo, 123);
-    auto variance = MakeRandomTensor<float, 1>(tensorInfo, 234, 0.0f);
-    auto beta     = MakeRandomTensor<float, 1>(tensorInfo, 123);
-    auto gamma    = MakeRandomTensor<float, 1>(tensorInfo, 345);
-
-    LayerTestResult<float,4> ret(outputTensorInfo);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle  = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandleRef  = refWorkloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::BatchNormalizationQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    armnn::ScopedCpuTensorHandle meanTensor(tensorInfo);
-    armnn::ScopedCpuTensorHandle varianceTensor(tensorInfo);
-    armnn::ScopedCpuTensorHandle betaTensor(tensorInfo);
-    armnn::ScopedCpuTensorHandle gammaTensor(tensorInfo);
-
-    AllocateAndCopyDataToITensorHandle(&meanTensor, &mean[0]);
-    AllocateAndCopyDataToITensorHandle(&varianceTensor, &variance[0]);
-    AllocateAndCopyDataToITensorHandle(&betaTensor, &beta[0]);
-    AllocateAndCopyDataToITensorHandle(&gammaTensor, &gamma[0]);
-
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-    data.m_Mean             = &meanTensor;
-    data.m_Variance         = &varianceTensor;
-    data.m_Beta             = &betaTensor;
-    data.m_Gamma            = &gammaTensor;
-    data.m_Parameters.m_Eps = 0.01f;
-
-    armnn::BatchNormalizationQueueDescriptor refData = data;
-    armnn::WorkloadInfo refInfo = info;
-    SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get());
-    SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateBatchNormalization(data, info);
-    std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateBatchNormalization(refData, refInfo);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-    inputHandleRef->Allocate();
-    outputHandleRef->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-    CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-    refWorkloadFactory.Finalize();
-    workloadRef->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-    CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get());
-
-    return ret;
-}
-
-template<typename T>
-void PermuteTensorData(
-        armnn::IWorkloadFactory& workloadFactory,
-        const armnn::PermutationVector& mappings,
-        armnn::TensorInfo & inputTensorInfo,
-        const T * inputData,
-        std::vector<T>& outputData)
-{
-    BOOST_ASSERT_MSG(inputData != nullptr, "inputData must not be null");
-    if (inputData == nullptr)
-    {
-        // Nullptr is an error in the test. By returning without doing the concatenation
-        // I expect the caller to fail the test. It still makes sense to report this as
-        // an assert for Debug builds.
-        return;
-    }
-
-    armnn::TensorInfo outputTensorInfo = armnnUtils::Permuted(inputTensorInfo, mappings);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::PermuteQueueDescriptor queueDescriptor;
-    queueDescriptor.m_Parameters = armnn::PermuteDescriptor{mappings};
-    armnn::WorkloadInfo workloadInfo;
-    AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePermute(queueDescriptor, workloadInfo);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), inputData);
-
-    workload->Execute();
-
-    outputData.resize(outputTensorInfo.GetNumElements());
-    CopyDataFromITensorHandle(&outputData[0], outputHandle.get());
-    inputTensorInfo = outputTensorInfo;
-}
-
-armnn::OriginsDescriptor CreateMergerDescriptorForConcatenation(
-        const std::vector<armnn::TensorInfo> & inputTensorInfos,
-        unsigned int concatDim)
-{
-    std::vector<armnn::TensorShape> shapes;
-    shapes.reserve(inputTensorInfos.size());
-    for (const armnn::TensorInfo& it: inputTensorInfos)
-    {
-        shapes.push_back(it.GetShape());
-    }
-
-    return armnn::CreateMergerDescriptorForConcatenation(shapes.begin(),
-                                                         shapes.end(),
-                                                         concatDim);
-}
-
-//
-// Concatenation is only supported for N and C dimensions for NCHW. In case of
-// <4 dimensions we need to make sure that the concat dimensions are at least
-// the 3rd slowest iterating one.
-//
-
-bool NeedPermuteForConcat(
-        const std::vector<armnn::TensorInfo> & inputTensorInfos,
-        unsigned int concatDim)
-{
-    // See note above. Additionally we expect the input shapes to have the
-    // same number of dimensions.
-    unsigned int nDimensions = 0;
-
-    // Determine the number of dimensions as well as sanity check them
-    // agains test implementation issues.
-    for (auto && tensorInfo : inputTensorInfos)
-    {
-        if (!nDimensions)
-        {
-            nDimensions = tensorInfo.GetShape().GetNumDimensions();
-        }
-        else
-        {
-            BOOST_ASSERT_MSG(nDimensions == tensorInfo.GetShape().GetNumDimensions(),
-                "Input shapes must have the same number of dimensions");
-        }
-    }
-
-    return (nDimensions-concatDim) < 3;
-}
-
-armnn::TensorShape ExpandTensorShapeTo3dForPermute(const armnn::TensorShape & inputShape)
-{
-    unsigned int numDims = inputShape.GetNumDimensions();
-    if (numDims >= 3)
-    {
-        // Nothing to do if the inputShape has at least 3 dimensions.
-        return inputShape;
-    }
-
-    std::vector<unsigned int> newDims(size_t(3), 1u);
-    unsigned int expandedBy = 3 - numDims;
-    for (unsigned int i=0; i<numDims; ++i)
-    {
-        newDims[expandedBy+i] = inputShape[i];
-    }
-    return armnn::TensorShape(3u, &newDims[0]);
-}
-
-void Generate3dPermuteVectorForConcat(
-        unsigned int numDimensions,
-        unsigned int & concatDim,
-        std::pair<armnn::PermutationVector, armnn::PermutationVector> & permutations)
-{
-    BOOST_ASSERT_MSG(numDimensions <= 3,
-       "Only dimensions 1,2 and 3 are supported by this helper");
-
-    unsigned int expandedBy = 3 - numDimensions;
-    unsigned int expandedConcatAxis = concatDim + expandedBy;
-
-    if (expandedConcatAxis == 2)
-    {
-        concatDim = 0;
-        armnn::PermutationVector forwardPermutation({1, 2, 0});
-        armnn::PermutationVector reversePermutation({2, 0, 1});
-        permutations = std::make_pair(forwardPermutation, reversePermutation);
-    }
-    else if (expandedConcatAxis == 1)
-    {
-        concatDim = 0;
-        armnn::PermutationVector forwardPermutation({2, 0, 1});
-        armnn::PermutationVector reversePermutation({1, 2, 0});
-        permutations = std::make_pair(forwardPermutation, reversePermutation);
-    }
-    else
-    {
-        BOOST_ASSERT(expandedConcatAxis == 0);
-        concatDim = 0;
-    }
-}
-
-//
-// Permute the input tensors so we can do a supported concatenation.
-// Also treat lower than 3d tensors as 3d by adding dummy 1 dimensions
-// at the front. Finally this function tells what the output shape
-// of the permuted concatenated tensor is going to be.
-//
-template <typename T>
-void PermuteInputsForConcat(
-        armnn::IWorkloadFactory& workloadFactory,
-        std::vector<armnn::TensorInfo> & inputTensorInfos,
-        std::vector<T *> & inputData,
-        std::vector<std::vector<T>> & inputDataStorage,
-        armnn::PermutationVector & permuteVector,
-        unsigned int & concatDim,
-        armnn::TensorInfo & outputTensorInfo)
-{
-    BOOST_ASSERT_MSG(inputTensorInfos.size() > 1,
-        "Expecting more than one tensor to be concatenated here");
-
-    unsigned int numDims = 0;
-    unsigned int nthInput = 0;
-    const armnn::PermutationVector identity({0, 1, 2});
-
-    std::pair<armnn::PermutationVector, armnn::PermutationVector> permutations =
-        std::make_pair(identity, identity);
-
-    inputDataStorage.resize(inputData.size());
-
-    for (auto && tensorInfo : inputTensorInfos)
-    {
-        if (numDims == 0)
-        {
-            numDims = tensorInfo.GetShape().GetNumDimensions();
-            Generate3dPermuteVectorForConcat(numDims, concatDim, permutations);
-            // Store the reverese permutation.
-            permuteVector = permutations.second;
-            BOOST_ASSERT_MSG(!permuteVector.IsEqual(identity),
-                "Test logic error, we don't need permutation, so we shouldn't arrive here");
-        }
-        else
-        {
-            BOOST_ASSERT_MSG(numDims == tensorInfo.GetShape().GetNumDimensions(),
-                "All inputs must have the same number of dimensions");
-        }
-
-        armnn::TensorInfo newTensorInfo = tensorInfo;
-        newTensorInfo.SetShape(ExpandTensorShapeTo3dForPermute(tensorInfo.GetShape()));
-
-        PermuteTensorData<T>(workloadFactory,
-                             permutations.first,
-                             newTensorInfo,
-                             inputData[nthInput],
-                             inputDataStorage[nthInput]);
-
-        inputData[nthInput] = inputDataStorage[nthInput].data();
-        inputTensorInfos[nthInput] = newTensorInfo;
-
-        ++nthInput;
-    }
-
-    outputTensorInfo.SetShape(
-        armnnUtils::Permuted(
-            ExpandTensorShapeTo3dForPermute(outputTensorInfo.GetShape()),
-            permutations.first));
-}
-
-
-//
-// This is the pair of PermuteInputsForConcat(...) which permutes back
-// the output of the concatenation so we can check it against an expected
-// output.
-//
-template <typename T>
-void PermuteOutputForConcat(
-        armnn::IWorkloadFactory& workloadFactory,
-        const armnn::TensorInfo & tensorInfo,
-        const armnn::PermutationVector & permuteVector,
-        std::unique_ptr<armnn::ITensorHandle> && inputDataHandle,
-        T * data)
-{
-    BOOST_ASSERT_MSG(data != nullptr, "data must not be null");
-    if (data == nullptr)
-    {
-        // Nullptr is an error in the test. By returning without doing the permutation
-        // I expect the caller to fail the test. It still makes sense to report this as
-        // an assert for Debug builds.
-        return;
-    }
-
-    armnn::TensorInfo resultTensorInfo = tensorInfo;
-    std::vector<T> inputData(tensorInfo.GetNumElements());
-    std::vector<T> outputData;
-
-    CopyDataFromITensorHandle(&inputData[0], inputDataHandle.get());
-
-    PermuteTensorData<T>(workloadFactory,
-                         permuteVector,
-                         resultTensorInfo,
-                         &inputData[0],
-                         outputData);
-
-    ::memcpy(data, &outputData[0], sizeof(T)*outputData.size());
-}
-
-template <typename T>
-void Concatenate(armnn::IWorkloadFactory& workloadFactory,
-                 std::initializer_list<const armnn::TensorInfo> inputTensorInfosOrig,
-                 std::initializer_list<T *> inputsOrig,
-                 const armnn::TensorInfo& outputTensorInfoOrig,
-                 T * output,
-                 unsigned int concatDim)
-{
-    BOOST_ASSERT_MSG(output != nullptr, "output must not be null");
-    if (output == nullptr)
-    {
-        // Nullptr is an error in the test. By returning without doing the permutation
-        // I expect the caller to fail the test. It still makes sense to report this as
-        // an assert for Debug builds.
-        return;
-    }
-
-    armnn::MergerQueueDescriptor queueDescriptor;
-
-    // Saves a copy of the parameters which we might need to change.
-    std::vector<armnn::TensorInfo> inputTensorInfos(inputTensorInfosOrig.begin(), inputTensorInfosOrig.end());
-    std::vector<T *> inputs            = inputsOrig;
-    armnn::TensorInfo outputTensorInfo = outputTensorInfoOrig;
-
-    armnn::PermutationVector permuteVector{0, 1, 2};
-
-    // Holds and automatically releases memory for the reshaped input data.
-    std::vector<std::vector<T>> tmpInputDataStorage;
-
-    const size_t inputCount = inputTensorInfos.size();
-
-    bool needPermuteForConcat = NeedPermuteForConcat(inputTensorInfos, concatDim);
-
-    if (needPermuteForConcat)
-    {
-        //
-        // We need to permute the inputs, because concatenation along
-        // the requested axis is not supported.
-        //
-        PermuteInputsForConcat<T>(workloadFactory,
-                                  inputTensorInfos,
-                                  inputs,
-                                  tmpInputDataStorage,
-                                  permuteVector,
-                                  concatDim,
-                                  outputTensorInfo);
-    }
-
-    armnn::OriginsDescriptor viewsDescriptor = CreateMergerDescriptorForConcatenation(inputTensorInfos, concatDim);
-
-    queueDescriptor.m_ViewOrigins.reserve(viewsDescriptor.GetNumViews());
-    for (unsigned int i = 0; i < viewsDescriptor.GetNumViews(); ++i)
-    {
-        queueDescriptor.m_ViewOrigins.emplace_back(std::vector<unsigned int>(viewsDescriptor.GetViewOrigin(i),
-            viewsDescriptor.GetViewOrigin(i) + viewsDescriptor.GetNumDimensions()));
-    }
-
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    std::vector<std::unique_ptr<armnn::ITensorHandle>> inputHandles;
-    inputHandles.reserve(inputCount);
-
-    const bool subTensorsSupported = workloadFactory.SupportsSubTensors();
-    for (unsigned int i = 0; i < inputCount; ++i)
-    {
-        const armnn::TensorInfo& inputTensorInfo = inputTensorInfos[i];
-
-        std::unique_ptr<armnn::ITensorHandle> inputHandle = subTensorsSupported ?
-            workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo.GetShape(),
-                queueDescriptor.m_ViewOrigins[i].m_Origin.data())
-            : workloadFactory.CreateTensorHandle(inputTensorInfo);
-
-        inputHandles.emplace_back(std::move(inputHandle));
-    }
-
-    armnn::WorkloadInfo workloadInfo;
-
-    for (unsigned int i = 0; i < inputCount; ++i)
-    {
-        AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfos[i], inputHandles[i].get());
-    }
-
-    AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMerger(queueDescriptor, workloadInfo);
-
-    for (auto& inputHandle : inputHandles)
-    {
-        inputHandle->Allocate();
-    }
-
-    outputHandle->Allocate();
-
-    unsigned int nextInputId = 0;
-    for (auto& inputHandle : inputHandles)
-    {
-        CopyDataToITensorHandle(inputHandle.get(), inputs[nextInputId]);
-        ++nextInputId;
-    }
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    if (needPermuteForConcat)
-    {
-        PermuteOutputForConcat<T>(workloadFactory,
-                                  outputTensorInfo,
-                                  permuteVector,
-                                  std::move(outputHandle),
-                                  output);
-    }
-    else
-    {
-        CopyDataFromITensorHandle(output, outputHandle.get());
-    }
-}
-
-template <typename T>
-LayerTestResult<T, 1> Concatenation1dTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset)
-{
-    armnn::TensorInfo inputTensorInfo({ 3 }, armnn::GetDataType<T>());
-
-    auto input0 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { 1.0f, 2.0f, 3.0f }));
-    auto input1 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { 4.0f, 5.0f, 6.0f }));
-    auto input2 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { 7.0f, 8.0f, 9.0f }));
-
-    armnn::TensorInfo outputTensorInfo({ 9 }, armnn::GetDataType<T>());
-
-    LayerTestResult<T, 1> result(outputTensorInfo);
-
-    std::vector<T> output;
-    output.resize(outputTensorInfo.GetNumElements());
-    Concatenate<T>(workloadFactory,
-        { inputTensorInfo, inputTensorInfo, inputTensorInfo },
-        { input0.data(), input1.data(), input2.data() },
-        outputTensorInfo,
-        output.data(),
-        0);
-
-    result.output = MakeTensor<T, 1>(outputTensorInfo, output);
-    result.outputExpected = MakeTensor<T, 1>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f
-    }));
-
-    return result;
-}
-
-LayerTestResult<float, 1> Concatenation1dTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    return Concatenation1dTestImpl<float>(workloadFactory, 0.0f, 0);
-}
-
-template <typename T>
-LayerTestResult<T, 2> Concatenation2dTestImpl(armnn::IWorkloadFactory& workloadFactory,
-    const armnn::TensorInfo& outputTensorInfo,
-    unsigned int dimension,
-    const float qScale,
-    const int32_t qOffset)
-{
-    armnn::TensorInfo inputTensorInfo({ 2, 3 }, armnn::GetDataType<T>());
-
-    auto input0 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        // Batch 0
-        1.0f, 2.0f, 3.0f,
-
-        // Batch 1
-        10.0f, 11.0f, 12.0f,
-    }));
-
-    auto input1 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        // Batch 0
-        4.0f, 5.0f, 6.0f,
-
-        // Batch 1
-        13.0f, 14.0f, 15.0f,
-    }));
-
-    auto input2 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        // Batch 0
-        7.0f, 8.0f, 9.0f,
-
-        // Batch 1
-        16.0f, 17.0f, 18.0f,
-    }));
-
-    LayerTestResult<T, 2> result(outputTensorInfo);
-
-    std::vector<T> output;
-    output.resize(outputTensorInfo.GetNumElements());
-    Concatenate<T>(workloadFactory,
-        { inputTensorInfo, inputTensorInfo, inputTensorInfo },
-        { input0.data(), input1.data(), input2.data() },
-        outputTensorInfo,
-        output.data(),
-        dimension);
-
-    result.output = MakeTensor<T, 2>(outputTensorInfo, output);
-    return result;
-}
-
-template <typename T>
-LayerTestResult<T, 2> Concatenation2dDim0TestImpl(armnn::IWorkloadFactory& workloadFactory,
-    float qScale, int32_t qOffset)
-{
-    armnn::TensorInfo outputTensorInfo({ 6, 3 }, armnn::GetDataType<T>());
-
-    LayerTestResult<T, 2> result = Concatenation2dTestImpl<T>(workloadFactory, outputTensorInfo, 0, qScale, qOffset);
-    result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        // Batch 0
-        1.0f, 2.0f, 3.0f,
-
-        // Batch 1
-        10.0f, 11.0f, 12.0f,
-
-        // Batch 2
-        4.0f, 5.0f, 6.0f,
-
-        // Batch 3
-        13.0f, 14.0f, 15.0f,
-
-        // Batch 4
-        7.0f, 8.0f, 9.0f,
-
-        // Batch 5
-        16.0f, 17.0f, 18.0f,
-    }));
-
-    return result;
-}
-
-LayerTestResult<float, 2> Concatenation2dDim0Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return Concatenation2dDim0TestImpl<float>(workloadFactory, 0.0f, 0);
-}
-
-template <typename T>
-LayerTestResult<T, 2> Concatenation2dDim1TestImpl(armnn::IWorkloadFactory& workloadFactory,
-    float qScale, int32_t qOffset)
-{
-    armnn::TensorInfo outputTensorInfo({ 2, 9 }, armnn::GetDataType<T>());
-
-    LayerTestResult<T, 2> result = Concatenation2dTestImpl<T>(workloadFactory, outputTensorInfo, 1, qScale, qOffset);
-    result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        // Batch 0
-        1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f,
-
-        // Batch 1
-        10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f
-    }));
-
-    return result;
-}
-
-LayerTestResult<float, 2> Concatenation2dDim1Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return Concatenation2dDim1TestImpl<float>(workloadFactory, 0.0f, 0);
-}
-
-template <typename T>
-LayerTestResult<T, 2> Concatenation2dDim0DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale,
-    int32_t qOffset)
-{
-    armnn::TensorInfo input0TensorInfo({ 2, 3 }, armnn::GetDataType<T>());
-    auto input0 = MakeTensor<T, 2>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        // Batch 0
-        1.0f, 2.0f, 3.0f,
-
-        // Batch 1
-        10.0f, 11.0f, 12.0f,
-    }));
-
-    armnn::TensorInfo input1TensorInfo({ 3, 3 }, armnn::GetDataType<T>());
-    auto input1 = MakeTensor<T, 2>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        // Batch 0
-        4.0f, 5.0f, 6.0f,
-
-        // Batch 1
-        13.0f, 14.0f, 15.0f,
-
-        // Batch 0
-        7.0f, 8.0f, 9.0f,
-    }));
-
-    armnn::TensorInfo input2TensorInfo({ 1, 3 }, armnn::GetDataType<T>());
-    auto input2 = MakeTensor<T, 2>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        // Batch 1
-        16.0f, 17.0f, 18.0f,
-    }));
-
-    armnn::TensorInfo outputTensorInfo({ 6, 3 }, armnn::GetDataType<T>());
-    LayerTestResult<T, 2> result(outputTensorInfo);
-
-    std::vector<T> output;
-    output.resize(outputTensorInfo.GetNumElements());
-    Concatenate<T>(workloadFactory,
-        { input0TensorInfo, input1TensorInfo, input2TensorInfo },
-        { input0.data(), input1.data(), input2.data() },
-        outputTensorInfo,
-        output.data(),
-        0);
-
-    result.output = MakeTensor<T, 2>(outputTensorInfo, output);
-    result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        // Batch 0
-        1.0f, 2.0f, 3.0f,
-
-        // Batch 1
-        10.0f, 11.0f, 12.0f,
-
-        // Batch 2
-        4.0f, 5.0f, 6.0f,
-
-        // Batch 3
-        13.0f, 14.0f, 15.0f,
-
-        // Batch 4
-        7.0f, 8.0f, 9.0f,
-
-        // Batch 5
-        16.0f, 17.0f, 18.0f,
-    }));
-
-    return result;
-}
-
-LayerTestResult<float, 2> Concatenation2dDim0DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    return Concatenation2dDim0DiffInputDimsTestImpl<float>(workloadFactory, 0.0f, 0);
-}
-
-template <typename T>
-LayerTestResult<T, 2> Concatenation2dDim1DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale,
-    int32_t qOffset)
-{
-    armnn::TensorInfo input0TensorInfo({ 2, 3 }, armnn::GetDataType<T>());
-    auto input0 = MakeTensor<T, 2>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        // Batch 0
-        1.0f, 2.0f, 3.0f,
-
-        // Batch 1
-        10.0f, 11.0f, 12.0f,
-    }));
-
-    armnn::TensorInfo input1TensorInfo({ 2, 5 }, armnn::GetDataType<T>());
-    auto input1 = MakeTensor<T, 2>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        // Batch 0
-        4.0f, 5.0f, 6.0f, 7.0f, 8.0f,
-
-        // Batch 1
-        13.0f, 14.0f, 15.0f, 16.0f, 17.0f,
-    }));
-
-    armnn::TensorInfo input2TensorInfo({ 2, 1 }, armnn::GetDataType<T>());
-    auto input2 = MakeTensor<T, 2>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        // Batch 0
-        9.0f,
-
-        // Batch 1
-        18.0f
-    }));
-
-    armnn::TensorInfo outputTensorInfo({ 2, 9 }, armnn::GetDataType<T>());
-    LayerTestResult<T, 2> result(outputTensorInfo);
-
-    std::vector<T> output;
-    output.resize(outputTensorInfo.GetNumElements());
-    Concatenate<T>(workloadFactory,
-        { input0TensorInfo, input1TensorInfo, input2TensorInfo },
-        { input0.data(), input1.data(), input2.data() },
-        outputTensorInfo,
-        output.data(),
-        1);
-
-    result.output = MakeTensor<T, 2>(outputTensorInfo, output);
-    result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        // Batch 0
-        1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f,
-
-        // Batch 1
-        10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f,
-    }));
-
-    return result;
-}
-
-LayerTestResult<float, 2> Concatenation2dDim1DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    return Concatenation2dDim1DiffInputDimsTestImpl<float>(workloadFactory, 0.0f, 0);
-}
-
-template <typename T>
-LayerTestResult<T, 3> Concatenation3dTestImpl(armnn::IWorkloadFactory& workloadFactory,
-    const armnn::TensorInfo& outputTensorInfo,
-    unsigned int dimension,
-    float qScale,
-    int32_t qOffset)
-{
-    armnn::TensorInfo inputTensorInfo({ 2, 3, 2 }, armnn::GetDataType<T>());
-
-    auto input0 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        // Batch 0, Channel 0
-        1.0f, 2.0f,
-
-        // Batch 0, Channel 1
-        3.0f, 4.0f,
-
-        // Batch 0, Channel 2
-        5.0f, 6.0f,
-
-        // Batch 1, Channel 0
-        19.0f, 20.0f,
-
-        // Batch 1, Channel 1
-        21.0f, 22.0f,
-
-        // Batch 1, Channel 2
-        23.0f, 24.0f
-    }));
-
-    auto input1 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        // Batch 0, Channel 0
-        7.0f, 8.0f,
-
-        // Batch 0, Channel 1
-        9.0f, 10.0f,
-
-        // Batch 0, Channel 2
-        11.0f, 12.0f,
-
-        // Batch 1, Channel 0
-        25.0f, 26.0f,
-
-        // Batch 1, Channel 1
-        27.0f, 28.0f,
-
-        // Batch 1, Channel 2
-        29.0f, 30.0f
-    }));
-
-    auto input2 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        // Batch 0, Channel 0
-        13.0f, 14.0f,
-
-        // Batch 0, Channel 1
-        15.0f, 16.0f,
-
-        // Batch 0, Channel 2
-        17.0f, 18.0f,
-
-        // Batch 1, Channel 0
-        31.0f, 32.0f,
-
-        // Batch 1, Channel 1
-        33.0f, 34.0f,
-
-        // Batch 1, Channel 2
-        35.0f, 36.0f
-    }));
-
-    LayerTestResult<T, 3> result(outputTensorInfo);
-
-    std::vector<T> output;
-    output.resize(outputTensorInfo.GetNumElements());
-    Concatenate<T>(workloadFactory,
-        { inputTensorInfo, inputTensorInfo, inputTensorInfo },
-        { input0.data(), input1.data(), input2.data() },
-        outputTensorInfo,
-        output.data(),
-        dimension);
-
-    result.output = MakeTensor<T, 3>(outputTensorInfo, output);
-    return result;
-}
-
-template <typename T>
-LayerTestResult<T, 3> Concatenation3dDim0TestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale,
-    int32_t qOffset)
-{
-    armnn::TensorInfo outputTensorInfo({ 6, 3, 2 }, armnn::GetDataType<T>());
-
-    LayerTestResult<T, 3> result = Concatenation3dTestImpl<T>(workloadFactory, outputTensorInfo, 0,
-        qScale, qOffset);
-    result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        // Batch 0, Channel 0
-        1.0f, 2.0f,
-
-        // Batch 0, Channel 1
-        3.0f, 4.0f,
-
-        // Batch 0, Channel 2
-        5.0f, 6.0f,
-
-        // Batch 1, Channel 0
-        19.0f, 20.0f,
-
-        // Batch 1, Channel 1
-        21.0f, 22.0f,
-
-        // Batch 1, Channel 2
-        23.0f, 24.0f,
-
-        // Batch 2, Channel 0
-        7.0f, 8.0f,
-
-        // Batch 2, Channel 1
-        9.0f, 10.0f,
-
-        // Batch 2, Channel 2
-        11.0f, 12.0f,
-
-        // Batch 3, Channel 0
-        25.0f, 26.0f,
-
-        // Batch 3, Channel 1
-        27.0f, 28.0f,
-
-        // Batch 3, Channel 2
-        29.0f, 30.0f,
-
-        // Batch 4, Channel 0
-        13.0f, 14.0f,
-
-        // Batch 4, Channel 1
-        15.0f, 16.0f,
-
-        // Batch 4, Channel 2
-        17.0f, 18.0f,
-
-        // Batch 5, Channel 0
-        31.0f, 32.0f,
-
-        // Batch 5, Channel 1
-        33.0f, 34.0f,
-
-        // Batch 5, Channel 2
-        35.0f, 36.0f
-    }));
-    return result;
-}
-
-LayerTestResult<float, 3> Concatenation3dDim0Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return Concatenation3dDim0TestImpl<float>(workloadFactory, 0.0f, 0);
-}
-
-template <typename T>
-LayerTestResult<T, 3> Concatenation3dDim1TestImpl(armnn::IWorkloadFactory& workloadFactory,
-    float qScale, int32_t qOffset)
-{
-    armnn::TensorInfo outputTensorInfo({ 2, 9, 2 }, armnn::GetDataType<T>());
-
-    LayerTestResult<T, 3> result = Concatenation3dTestImpl<T>(workloadFactory, outputTensorInfo, 1, qScale, qOffset);
-    result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        // Batch 0, Channel 0
-        1.0f, 2.0f,
-
-        // Batch 0, Channel 1
-        3.0f, 4.0f,
-
-        // Batch 0, Channel 2
-        5.0f, 6.0f,
-
-        // Batch 0, Channel 3
-        7.0f, 8.0f,
-
-        // Batch 0, Channel 4
-        9.0f, 10.0f,
-
-        // Batch 0, Channel 5
-        11.0f, 12.0f,
-
-        // Batch 0, Channel 6
-        13.0f, 14.0f,
-
-        // Batch 0, Channel 7
-        15.0f, 16.0f,
-
-        // Batch 0, Channel 8
-        17.0f, 18.0f,
-
-        // Batch 1, Channel 0
-        19.0f, 20.0f,
-
-        // Batch 1, Channel 1
-        21.0f, 22.0f,
-
-        // Batch 1, Channel 2
-        23.0f, 24.0f,
-
-        // Batch 1, Channel 3
-        25.0f, 26.0f,
-
-        // Batch 1, Channel 4
-        27.0f, 28.0f,
-
-        // Batch 1, Channel 5
-        29.0f, 30.0f,
-
-        // Batch 1, Channel 6
-        31.0f, 32.0f,
-
-        // Batch 1, Channel 7
-        33.0f, 34.0f,
-
-        // Batch 1, Channel 8
-        35.0f, 36.0f
-    }));
-
-    return result;
-}
-
-LayerTestResult<float, 3> Concatenation3dDim1Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return Concatenation3dDim1TestImpl<float>(workloadFactory, 0.0f, 0);
-}
-
-template <typename T>
-LayerTestResult<T, 3> Concatenation3dDim2TestImpl(armnn::IWorkloadFactory& workloadFactory,
-    float qScale, int32_t qOffset)
-{
-    armnn::TensorInfo outputTensorInfo({ 2, 3, 6 }, armnn::GetDataType<T>());
-
-    LayerTestResult<T, 3> result = Concatenation3dTestImpl<T>(workloadFactory, outputTensorInfo, 2, qScale, qOffset);
-    result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        // Batch 0, Channel 0
-        1.0f, 2.0f, 7.0f, 8.0f, 13.0f, 14.0f,
-
-        // Batch 0, Channel 1
-        3.0f, 4.0f, 9.0f, 10.0f, 15.0f, 16.0f,
-
-        // Batch 0, Channel 2
-        5.0f, 6.0f, 11.0f, 12.0f, 17.0f, 18.0f,
-
-        // Batch 1, Channel 0
-        19.0f, 20.0f, 25.0f, 26.0f, 31.0f, 32.0f,
-
-        // Batch 1, Channel 1
-        21.0f, 22.0f, 27.0f, 28.0f, 33.0f, 34.0f,
-
-        // Batch 1, Channel 2
-        23.0f, 24.0f, 29.0f, 30.0f, 35.0f, 36.0f,
-    }));
-
-    return result;
-}
-
-LayerTestResult<float, 3> Concatenation3dDim2Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return Concatenation3dDim2TestImpl<float>(workloadFactory, 0.0f, 0);
-}
-
-template <typename T>
-LayerTestResult<T, 3> Concatenation3dDim0DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale,
-    int32_t qOffset)
-{
-    armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, armnn::GetDataType<T>());
-    auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, {
-            // Batch 0, Channel 0
-            1.0f, 2.0f,
-
-            // Batch 0, Channel 1
-            3.0f, 4.0f,
-
-            // Batch 0, Channel 2
-            5.0f, 6.0f,
-
-            // Batch 1, Channel 0
-            19.0f, 20.0f,
-
-            // Batch 1, Channel 1
-            21.0f, 22.0f,
-
-            // Batch 1, Channel 2
-            23.0f, 24.0f
-    }));
-
-    armnn::TensorInfo input1TensorInfo({ 1, 3, 2 }, armnn::GetDataType<T>());
-    auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, {
-            // Batch 0, Channel 0
-            7.0f, 8.0f,
-
-            // Batch 0, Channel 1
-            9.0f, 10.0f,
-
-            // Batch 0, Channel 2
-            11.0f, 12.0f,
-    }));
-
-    armnn::TensorInfo input2TensorInfo({ 3, 3, 2 }, armnn::GetDataType<T>());
-    auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, {
-            // Batch 0, Channel 0
-            25.0f, 26.0f,
-
-            // Batch 0, Channel 1
-            27.0f, 28.0f,
-
-            // Batch 0, Channel 2
-            29.0f, 30.0f,
-
-            // Batch 1, Channel 0
-            13.0f, 14.0f,
-
-            // Batch 1, Channel 1
-            15.0f, 16.0f,
-
-            // Batch 1, Channel 2
-            17.0f, 18.0f,
-
-            // Batch 2, Channel 0
-            31.0f, 32.0f,
-
-            // Batch 2, Channel 1
-            33.0f, 34.0f,
-
-            // Batch 2, Channel 2
-            35.0f, 36.0f
-    }));
-
-    armnn::TensorInfo outputTensorInfo({ 6, 3, 2 }, armnn::GetDataType<T>());
-    LayerTestResult<T, 3> result(outputTensorInfo);
-
-    std::vector<T> output;
-    output.resize(outputTensorInfo.GetNumElements());
-    Concatenate<T>(workloadFactory,
-        { input0TensorInfo, input1TensorInfo, input2TensorInfo },
-        { input0.data(), input1.data(), input2.data() },
-        outputTensorInfo,
-        output.data(),
-        0);
-
-    result.output = MakeTensor<T, 3>(outputTensorInfo, output);
-    result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        // Batch 0, Channel 0
-        1.0f, 2.0f,
-
-        // Batch 0, Channel 1
-        3.0f, 4.0f,
-
-        // Batch 0, Channel 2
-        5.0f, 6.0f,
-
-        // Batch 1, Channel 0
-        19.0f, 20.0f,
-
-        // Batch 1, Channel 1
-        21.0f, 22.0f,
-
-        // Batch 1, Channel 2
-        23.0f, 24.0f,
-
-        // Batch 2, Channel 0
-        7.0f, 8.0f,
-
-        // Batch 2, Channel 1
-        9.0f, 10.0f,
-
-        // Batch 2, Channel 2
-        11.0f, 12.0f,
-
-        // Batch 3, Channel 0
-        25.0f, 26.0f,
-
-        // Batch 3, Channel 1
-        27.0f, 28.0f,
-
-        // Batch 3, Channel 2
-        29.0f, 30.0f,
-
-        // Batch 4, Channel 0
-        13.0f, 14.0f,
-
-        // Batch 4, Channel 1
-        15.0f, 16.0f,
-
-        // Batch 4, Channel 2
-        17.0f, 18.0f,
-
-        // Batch 5, Channel 0
-        31.0f, 32.0f,
-
-        // Batch 5, Channel 1
-        33.0f, 34.0f,
-
-        // Batch 5, Channel 2
-        35.0f, 36.0f
-    }));
-
-    return result;
-}
-
-LayerTestResult<float, 3> Concatenation3dDim0DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    return Concatenation3dDim0DiffInputDimsTestImpl<float>(workloadFactory, 0.0f, 0);
-}
-
-template <typename T>
-LayerTestResult<T, 3> Concatenation3dDim1DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale,
-    int32_t qOffset)
-{
-    armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, armnn::GetDataType<T>());
-    auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        // Batch 0, Channel 0
-        1.0f, 2.0f,
-
-        // Batch 0, Channel 1
-        3.0f, 4.0f,
-
-        // Batch 0, Channel 2
-        5.0f, 6.0f,
-
-        // Batch 1, Channel 0
-        19.0f, 20.0f,
-
-        // Batch 1, Channel 1
-        21.0f, 22.0f,
-
-        // Batch 1, Channel 2
-        23.0f, 24.0f
-    }));
-
-    armnn::TensorInfo input1TensorInfo({ 2, 4, 2 }, armnn::GetDataType<T>());
-    auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        // Batch 0, Channel 0
-        7.0f, 8.0f,
-
-        // Batch 0, Channel 1
-        9.0f, 10.0f,
-
-        // Batch 0, Channel 2
-        11.0f, 12.0f,
-
-        // Batch 0, Channel 3
-        25.0f, 26.0f,
-
-        // Batch 1, Channel 0
-        27.0f, 28.0f,
-
-        // Batch 1, Channel 1
-        29.0f, 30.0f,
-
-        // Batch 1, Channel 2
-        13.0f, 14.0f,
-
-        // Batch 1, Channel 3
-        15.0f, 16.0f,
-    }));
-
-    armnn::TensorInfo input2TensorInfo({ 2, 1, 2 }, armnn::GetDataType<T>());
-    auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        // Batch 0, Channel 0
-        17.0f, 18.0f,
-
-        // Batch 1, Channel 0
-        31.0f, 32.0f,
-    }));
-
-    armnn::TensorInfo outputTensorInfo({ 2, 8, 2 }, armnn::GetDataType<T>());
-    LayerTestResult<T, 3> result(outputTensorInfo);
-
-    std::vector<T> output;
-    output.resize(outputTensorInfo.GetNumElements());
-    Concatenate<T>(workloadFactory,
-        { input0TensorInfo, input1TensorInfo, input2TensorInfo },
-        { input0.data(), input1.data(), input2.data() },
-        outputTensorInfo,
-        output.data(),
-        1);
-
-    result.output = MakeTensor<T, 3>(outputTensorInfo, output);
-    result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        // Batch 0, Channel 0
-        1.0f, 2.0f,
-
-        // Batch 0, Channel 1
-        3.0f, 4.0f,
-
-        // Batch 0, Channel 2
-        5.0f, 6.0f,
-
-        // Batch 0, Channel 3
-        7.0f, 8.0f,
-
-        // Batch 0, Channel 4
-        9.0f, 10.0f,
-
-        // Batch 0, Channel 5
-        11.0f, 12.0f,
-
-        // Batch 0, Channel 6
-        25.0f, 26.0f,
-
-        // Batch 0, Channel 7
-        17.0f, 18.0f,
-
-        // Batch 1, Channel 0
-        19.0f, 20.0f,
-
-        // Batch 1, Channel 1
-        21.0f, 22.0f,
-
-        // Batch 1, Channel 2
-        23.0f, 24.0f,
-
-        // Batch 1, Channel 3
-        27.0f, 28.0f,
-
-        // Batch 1, Channel 4
-        29.0f, 30.0f,
-
-        // Batch 1, Channel 5
-        13.0f, 14.0f,
-
-        // Batch 1, Channel 6
-        15.0f, 16.0f,
-
-        // Batch 1, Channel 7
-        31.0f, 32.0f,
-    }));
-
-    return result;
-}
-
-LayerTestResult<float, 3> Concatenation3dDim1DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    return Concatenation3dDim1DiffInputDimsTestImpl<float>(workloadFactory, 0.0f, 0);
-}
-
-template <typename T>
-LayerTestResult<T, 3> Concatenation3dDim2DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale,
-    int32_t qOffset)
-{
-    armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, armnn::GetDataType<T>());
-    auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        // Batch 0, Channel 0
-        1.0f, 2.0f,
-
-        // Batch 0, Channel 1
-        3.0f, 4.0f,
-
-        // Batch 0, Channel 2
-        5.0f, 6.0f,
-
-        // Batch 1, Channel 0
-        19.0f, 20.0f,
-
-        // Batch 1, Channel 1
-        21.0f, 22.0f,
-
-        // Batch 1, Channel 2
-        23.0f, 24.0f
-    }));
-
-    armnn::TensorInfo input1TensorInfo({ 2, 3, 1 }, armnn::GetDataType<T>());
-    auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        // Batch 0, Channel 0
-        7.0f,
-
-        // Batch 0, Channel 1
-        9.0f,
-
-        // Batch 0, Channel 2
-        11.0f,
-
-        // Batch 1, Channel 0
-        25.0f,
-
-        // Batch 1, Channel 1
-        27.0f,
-
-        // Batch 1, Channel 2
-        29.0f
-    }));
-
-    armnn::TensorInfo input2TensorInfo({ 2, 3, 3 }, armnn::GetDataType<T>());
-    auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        // Batch 0, Channel 0
-        13.0f, 14.0f, 50.0f,
-
-        // Batch 0, Channel 1
-        15.0f, 16.0f, 51.0f,
-
-        // Batch 0, Channel 2
-        17.0f, 18.0f, 52.0f,
-
-        // Batch 1, Channel 0
-        31.0f, 32.0f, 53.0f,
-
-        // Batch 1, Channel 1
-        33.0f, 34.0f, 54.0f,
-
-        // Batch 1, Channel 2
-        35.0f, 36.0f, 55.0f,
-    }));
-
-    armnn::TensorInfo outputTensorInfo({ 2, 3, 6 }, armnn::GetDataType<T>());
-    LayerTestResult<T, 3> result(outputTensorInfo);
-
-    std::vector<T> output;
-    output.resize(outputTensorInfo.GetNumElements());
-    Concatenate<T>(workloadFactory,
-        { input0TensorInfo, input1TensorInfo, input2TensorInfo },
-        { input0.data(), input1.data(), input2.data() },
-        outputTensorInfo,
-        output.data(),
-        2);
-
-    result.output = MakeTensor<T, 3>(outputTensorInfo, output);
-    result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, {
-        // Batch 0, Channel 0
-        1.0f, 2.0f, 7.0f, 13.0f, 14.0f, 50.0f,
-
-        // Batch 0, Channel 1
-        3.0f, 4.0f, 9.0f, 15.0f, 16.0f, 51.0f,
-
-        // Batch 0, Channel 2
-        5.0f, 6.0f, 11.0f, 17.0f, 18.0f, 52.0f,
-
-        // Batch 1, Channel 0
-        19.0f, 20.0f, 25.0f, 31.0f, 32.0f, 53.0f,
-
-        // Batch 1, Channel 1
-        21.0f, 22.0f, 27.0f, 33.0f, 34.0f, 54.0f,
-
-        // Batch 1, Channel 2
-        23.0f, 24.0f, 29.0f, 35.0f, 36.0f, 55.0f,
-    }));
-
-    return result;
-}
-
-LayerTestResult<float, 3> Concatenation3dDim2DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    return Concatenation3dDim2DiffInputDimsTestImpl<float>(workloadFactory, 0.0f, 0);
-}
-
-LayerTestResult<float, 4> ResizeBilinearNopTest(armnn::IWorkloadFactory& workloadFactory,
-                                                const armnn::DataLayoutIndexed& dataLayout)
-{
-    const armnn::TensorInfo inputTensorInfo = GetTensorInfo<float>(1, 2, 4, 4, dataLayout);
-    const armnn::TensorInfo outputTensorInfo = GetTensorInfo<float>(1, 2, 4, 4, dataLayout);
-
-    std::vector<float> inputData({
-        1.0f, 2.0f, 3.0f, 4.0f,
-        2.0f, 3.0f, 4.0f, 5.0f,
-        3.0f, 4.0f, 5.0f, 6.0f,
-        4.0f, 5.0f, 6.0f, 7.0f,
-
-        1.0f, 2.0f, 3.0f, 4.0f,
-        2.0f, 3.0f, 4.0f, 5.0f,
-        3.0f, 4.0f, 5.0f, 6.0f,
-        4.0f, 5.0f, 6.0f, 7.0f
-    });
-
-    const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
-    if (dataLayout.GetDataLayout() == armnn::DataLayout::NHWC)
-    {
-        std::vector<float> tmp(inputData.size());
-        armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data());
-        inputData = tmp;
-    }
-
-    auto input = MakeTensor<float, 4>(inputTensorInfo, inputData);
-
-    LayerTestResult<float, 4> result(outputTensorInfo);
-    result.outputExpected = input;
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ResizeBilinearQueueDescriptor descriptor;
-    descriptor.m_Parameters.m_DataLayout = dataLayout;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-    return result;
-}
-
-LayerTestResult<float, 4> SimpleResizeBilinearTest(armnn::IWorkloadFactory& workloadFactory,
-                                                   const armnn::DataLayoutIndexed& dataLayout)
-{
-    const armnn::TensorInfo inputTensorInfo = GetTensorInfo<float>(1, 2, 2, 2, dataLayout);
-    const armnn::TensorInfo outputTensorInfo = GetTensorInfo<float>(1, 2, 1, 1, dataLayout);
-
-    std::vector<float> inputData({
-          1.0f, 255.0f,
-        200.0f, 250.0f,
-
-        250.0f, 200.0f,
-        250.0f,   1.0f
-    });
-
-    // The 'resize bilinear' operation projects the top-left corner of output texels into the input image,
-    // then figures out the interpolants and weights. Note this is different to projecting the centre of the
-    // output texel. Thus, for a input matrix of 2x2, we'll expect the output 1x1 matrix to contain, as
-    // its single element, the value that was at position (0,0) of the input matrix (rather than an average,
-    // which we would expect if projecting the centre).
-
-    std::vector<float> outputData({
-          1.0f,
-
-        250.0f
-    });
-
-    const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
-    if (dataLayout.GetDataLayout() == armnn::DataLayout::NHWC)
-    {
-        std::vector<float> tmp(inputData.size());
-        armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data());
-        inputData = tmp;
-
-        std::vector<float> tmp1(outputData.size());
-        armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data());
-        outputData = tmp1;
-    }
-
-    auto input = MakeTensor<float, 4>(inputTensorInfo, inputData);
-
-    LayerTestResult<float, 4> result(outputTensorInfo);
-    result.outputExpected = MakeTensor<float, 4>(outputTensorInfo, outputData);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ResizeBilinearQueueDescriptor descriptor;
-    descriptor.m_Parameters.m_DataLayout = dataLayout;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-    return result;
-}
-
-LayerTestResult<float, 4> ResizeBilinearSqMinTest(armnn::IWorkloadFactory& workloadFactory,
-                                                  const armnn::DataLayoutIndexed& dataLayout)
-{
-    const armnn::TensorInfo inputTensorInfo = GetTensorInfo<float>(1, 2, 4, 4, dataLayout);
-    const armnn::TensorInfo outputTensorInfo = GetTensorInfo<float>(1, 2, 2, 2, dataLayout);
-
-    std::vector<float> inputData({
-        1.0f, 2.0f, 3.0f, 4.0f,
-        2.0f, 3.0f, 4.0f, 5.0f,
-        3.0f, 4.0f, 5.0f, 6.0f,
-        4.0f, 5.0f, 6.0f, 7.0f,
-
-        7.0f, 6.0f, 5.0f, 4.0f,
-        6.0f, 5.0f, 4.0f, 3.0f,
-        5.0f, 4.0f, 3.0f, 2.0f,
-        4.0f, 3.0f, 2.0f, 1.0f
-    });
-
-    std::vector<float> outputData({
-        1.0f, 3.0f,
-        3.0f, 5.0f,
-
-        7.0f, 5.0f,
-        5.0f, 3.0f
-    });
-
-    const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
-    if (dataLayout.GetDataLayout() == armnn::DataLayout::NHWC)
-    {
-        std::vector<float> tmp(inputData.size());
-        armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data());
-        inputData = tmp;
-
-        std::vector<float> tmp1(outputData.size());
-        armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data());
-        outputData = tmp1;
-    }
-
-    auto input = MakeTensor<float, 4>(inputTensorInfo, inputData);
-
-    LayerTestResult<float, 4> result(outputTensorInfo);
-    result.outputExpected = MakeTensor<float, 4>(outputTensorInfo, outputData);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ResizeBilinearQueueDescriptor descriptor;
-    descriptor.m_Parameters.m_DataLayout = dataLayout;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-    return result;
-}
-
-LayerTestResult<float, 4> ResizeBilinearMinTest(armnn::IWorkloadFactory& workloadFactory,
-                                                const armnn::DataLayoutIndexed& dataLayout)
-{
-    const armnn::TensorInfo inputTensorInfo = GetTensorInfo<float>(1, 2, 3, 5, dataLayout);
-    const armnn::TensorInfo outputTensorInfo = GetTensorInfo<float>(1, 2, 2, 3, dataLayout);
-
-    std::vector<float> inputData({
-          1.0f,   2.0f,   3.0f,   5.0f,   8.0f,
-         13.0f,  21.0f,  34.0f,  55.0f,  89.0f,
-        144.0f, 233.0f, 377.0f, 610.0f, 987.0f,
-
-        987.0f, 610.0f, 377.0f, 233.0f, 144.0f,
-         89.0f,  55.0f,  34.0f,  21.0f,  13.0f,
-          8.0f,   5.0f,   3.0f,   2.0f,   1.0f
-    });
-
-    std::vector<float> outputData({
-          1.0f,   2.6666f,   6.00f,
-         78.5f, 179.3333f, 401.00f,
-
-        987.0f, 454.6670f, 203.33f,
-         48.5f,  22.3333f,  10.00f
-    });
-
-    const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
-    if (dataLayout.GetDataLayout() == armnn::DataLayout::NHWC)
-    {
-        std::vector<float> tmp(inputData.size());
-        armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data());
-        inputData = tmp;
-
-        std::vector<float> tmp1(outputData.size());
-        armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data());
-        outputData = tmp1;
-    }
-
-    auto input = MakeTensor<float, 4>(inputTensorInfo, inputData);
-
-    LayerTestResult<float, 4> result(outputTensorInfo);
-    result.outputExpected = MakeTensor<float, 4>(outputTensorInfo, outputData);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ResizeBilinearQueueDescriptor descriptor;
-    descriptor.m_Parameters.m_DataLayout = dataLayout;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-    return result;
-}
-
-LayerTestResult<float, 4> ResizeBilinearMagTest(armnn::IWorkloadFactory& workloadFactory,
-                                                const armnn::DataLayoutIndexed& dataLayout)
-{
-    const armnn::TensorInfo inputTensorInfo = GetTensorInfo<float>(1, 2, 3, 2, dataLayout);
-    const armnn::TensorInfo outputTensorInfo = GetTensorInfo<float>(1, 2, 3, 5, dataLayout);
-
-    std::vector<float> inputData({
-          1.0f,   2.0f,
-         13.0f,  21.0f,
-        144.0f, 233.0f,
-
-        233.0f, 144.0f,
-         21.0f,  13.0f,
-          2.0f,   1.0f
-    });
-
-    std::vector<float> outputData({
-          1.0f,   1.4f,   1.8f,   2.0f,   2.0f,
-         13.0f,  16.2f,  19.4f,  21.0f,  21.0f,
-        144.0f, 179.6f, 215.2f, 233.0f, 233.0f,
-
-        233.0f, 197.4f, 161.8f, 144.0f, 144.0f,
-         21.0f,  17.8f,  14.6f,  13.0f,  13.0f,
-          2.0f,   1.6f,   1.2f,   1.0f,   1.0f
-    });
-
-    const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
-    if (dataLayout.GetDataLayout() == armnn::DataLayout::NHWC)
-    {
-        std::vector<float> tmp(inputData.size());
-        armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data());
-        inputData = tmp;
-
-        std::vector<float> tmp1(outputData.size());
-        armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data());
-        outputData = tmp1;
-    }
-
-    auto input = MakeTensor<float, 4>(inputTensorInfo, inputData);
-
-    LayerTestResult<float, 4> result(outputTensorInfo);
-    result.outputExpected = MakeTensor<float, 4>(outputTensorInfo, outputData);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ResizeBilinearQueueDescriptor descriptor;
-    descriptor.m_Parameters.m_DataLayout = dataLayout;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-    return result;
-}
-
-LayerTestResult<float, 2> FakeQuantizationTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    constexpr unsigned int width = 2;
-    constexpr unsigned int height = 3;
-
-    const armnn::TensorInfo tensorInfo({height, width },
-        armnn::DataType::Float32);
-    auto input = MakeTensor<float, 2>(tensorInfo, std::vector<float>({
-       -10.0f,  -5.0f,
-         0.0f,   5.0f,
-        10.0f,  10.0f
-    }));
-
-    LayerTestResult<float, 2> ret(tensorInfo);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle  = workloadFactory.CreateTensorHandle(tensorInfo);
-
-    std::unique_ptr<armnn::ITensorHandle> outputHandle  = workloadFactory.CreateTensorHandle(tensorInfo);
-
-    armnn::FakeQuantizationQueueDescriptor data;
-    armnn::WorkloadInfo info;
-
-    AddInputToWorkload(data, info, tensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, tensorInfo, outputHandle.get());
-    float min = -10.f;
-    float max = 10.f;
-
-    data.m_Parameters.m_Min = min;
-    data.m_Parameters.m_Max = max;
-
-    armnn::PassthroughCpuTensorHandle refHandle(tensorInfo, &ret.outputExpected[0][0]);
-    armnn::FakeQuantizationQueueDescriptor refData = data;
-    armnn::WorkloadInfo refInfo = info;
-    SetWorkloadOutput(refData, refInfo, 0, tensorInfo, &refHandle);
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateFakeQuantization(data, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get());
-
-    ret.outputExpected = MakeTensor<float, 2>(tensorInfo, std::vector<float>({
-        0.0f,     63.0f,
-        128.0f,   191.0f,
-        255.0f,   255.0f
-    }));
-    return ret;
-}
-
-namespace
-{
-
-LayerTestResult<float, 4> L2NormalizationTestImpl(armnn::IWorkloadFactory& workloadFactory,
-                                                  const armnn::TensorShape& inputOutputTensorShape,
-                                                  const std::vector<float>& inputValues,
-                                                  const std::vector<float>& expectedOutputValues,
-                                                  armnn::DataLayout dataLayout)
-{
-    const armnn::TensorInfo inputTensorInfo(inputOutputTensorShape, armnn::DataType::Float32);
-    const armnn::TensorInfo outputTensorInfo(inputOutputTensorShape, armnn::DataType::Float32);
-
-    auto inputTensor = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>(inputValues));
-
-    LayerTestResult<float, 4> result(outputTensorInfo);
-    result.outputExpected = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>(expectedOutputValues));
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::L2NormalizationQueueDescriptor descriptor;
-    descriptor.m_Parameters.m_DataLayout = dataLayout;
-    armnn::WorkloadInfo info;
-
-    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateL2Normalization(descriptor, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-
-    return result;
-}
-
-float CalcInvL2Norm(std::initializer_list<float> elements)
-{
-    const float reduction = std::accumulate(elements.begin(), elements.end(), 0.0f,
-        [](float acc, float element) { return acc + element * element; });
-    return 1.0f / sqrtf(reduction);
-}
-
-} // anonymous namespace
-
-template<typename T>
-LayerTestResult<T, 2> Pad2dTestCommon(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset)
-{
-  const armnn::TensorShape inputShape{ 3, 3 };
-  const armnn::TensorShape outputShape{ 7, 7 };
-
-  const armnn::TensorInfo inputTensorInfo(inputShape, armnn::GetDataType<T>());
-  const armnn::TensorInfo outputTensorInfo(outputShape, armnn::GetDataType<T>());
-
-  std::vector<T> inputValues(
-    QuantizedVector<T>(qScale, qOffset,
-    {
-      // Height (3) x Width (3)
-      4, 8, 6,
-      7, 4, 4,
-      3, 2, 4
-    }));
-
- std::vector<T> expectedOutputValues(
-  QuantizedVector<T>(qScale, qOffset,
-    {
-      0, 0, 0, 0, 0, 0, 0,
-      0, 0, 0, 0, 0, 0, 0,
-      0, 0, 4, 8, 6, 0, 0,
-      0, 0, 7, 4, 4, 0, 0,
-      0, 0, 3, 2, 4, 0, 0,
-      0, 0, 0, 0, 0, 0, 0,
-      0, 0, 0, 0, 0, 0, 0
-    }));
-
-  auto inputTensor = MakeTensor<T, 2>(inputTensorInfo, std::vector<T>(inputValues));
-
-  LayerTestResult<T, 2> result(outputTensorInfo);
-  result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, std::vector<T>(expectedOutputValues));
-
-  std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-  armnn::PadQueueDescriptor descriptor;
-
-  std::vector<std::pair<unsigned int, unsigned int>> PadList;
-  PadList.push_back(std::pair<unsigned int, unsigned int>(2,2));
-  PadList.push_back(std::pair<unsigned int, unsigned int>(2,2));
-
-  descriptor.m_Parameters.m_PadList = PadList;
-  armnn::WorkloadInfo info;
-
-  AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
-  AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
-
-  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePad(descriptor, info);
-
-  inputHandle->Allocate();
-  outputHandle->Allocate();
-
-  CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0]);
-
-  workloadFactory.Finalize();
-  workload->Execute();
-
-  CopyDataFromITensorHandle(&result.output[0][0], outputHandle.get());
-
-  return result;
-}
-
-template <typename T>
-LayerTestResult<T, 3> Pad3dTestCommon(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset)
-{
-    const armnn::TensorShape inputShape{ 2, 2, 2 };
-    const armnn::TensorShape outputShape{ 3, 5, 6 };
-
-    const armnn::TensorInfo inputTensorInfo(inputShape, armnn::GetDataType<T>());
-    const armnn::TensorInfo outputTensorInfo(outputShape, armnn::GetDataType<T>());
-
-    std::vector<T> inputValues(
-      QuantizedVector<T>(qScale,qOffset,
-    {
-        // Channel 0, Height (2) x Width (2)
-        0, 4,
-        2, 5,
-
-        // Channel 1, Height (2) x Width (2)
-        6, 1,
-        5, 2
-    }));
-
-    std::vector<T> expectedOutputValues(
-      QuantizedVector<T>(qScale,qOffset,
-    {
-
-        0, 0, 0, 0, 0, 0,
-        0, 0, 0, 0, 0, 0,
-        0, 0, 0, 4, 0, 0,
-        0, 0, 2, 5, 0, 0,
-        0, 0, 0, 0, 0, 0,
-
-        0, 0, 0, 0, 0, 0,
-        0, 0, 0, 0, 0, 0,
-        0, 0, 6, 1, 0, 0,
-        0, 0, 5, 2, 0, 0,
-        0, 0, 0, 0, 0, 0,
-
-        0, 0, 0, 0, 0, 0,
-        0, 0, 0, 0, 0, 0,
-        0, 0, 0, 0, 0, 0,
-        0, 0, 0, 0, 0, 0,
-        0, 0, 0, 0, 0, 0
-
-    }));
-
-    auto inputTensor = MakeTensor<T, 3>(inputTensorInfo, std::vector<T>(inputValues));
-
-    LayerTestResult<T, 3> result(outputTensorInfo);
-    result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, std::vector<T>(expectedOutputValues));
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::PadQueueDescriptor descriptor;
-
-    std::vector<std::pair<unsigned int, unsigned int>> PadList;
-    PadList.push_back(std::pair<unsigned int, unsigned int>(0,1));
-    PadList.push_back(std::pair<unsigned int, unsigned int>(2,1));
-    PadList.push_back(std::pair<unsigned int, unsigned int>(2,2));
-
-    descriptor.m_Parameters.m_PadList = PadList;
-    armnn::WorkloadInfo info;
-
-    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePad(descriptor, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0], outputHandle.get());
-
-    return result;
-}
-
-template <typename T>
-LayerTestResult<T, 4> Pad4dTestCommon(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset)
-{
-    const armnn::TensorShape inputShape{ 2, 2, 3, 2 };
-    const armnn::TensorShape outputShape{ 4, 5, 7, 4 };
-
-    const armnn::TensorInfo inputTensorInfo(inputShape, armnn::GetDataType<T>());
-    const armnn::TensorInfo outputTensorInfo(outputShape, armnn::GetDataType<T>());
-
-    std::vector<T> inputValues(
-      QuantizedVector<T>(qScale,qOffset,
-    {
-        // Batch 0, Channel 0, Height (3) x Width (2)
-        0, 1,
-        2, 3,
-        4, 5,
-
-        // Batch 0, Channel 1, Height (3) x Width (2)
-        6, 7,
-        8, 9,
-        10, 11,
-
-        // Batch 1, Channel 0, Height (3) x Width (2)
-        12, 13,
-        14, 15,
-        16, 17,
-
-        // Batch 1, Channel 1, Height (3) x Width (2)
-        18, 19,
-        20, 21,
-        22, 23
-    }));
-
-    std::vector<T> expectedOutputValues(
-      QuantizedVector<T>(qScale,qOffset,
-    {
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 1, 0,
-        0, 2, 3, 0,
-        0, 4, 5, 0,
-        0, 0, 0, 0,
-
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 6, 7, 0,
-        0, 8, 9, 0,
-        0, 10, 11, 0,
-        0, 0, 0, 0,
-
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 12, 13, 0,
-        0, 14, 15, 0,
-        0, 16, 17, 0,
-        0, 0, 0, 0,
-
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 18, 19, 0,
-        0, 20, 21, 0,
-        0, 22, 23, 0,
-        0, 0, 0, 0,
-
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0,
-        0, 0, 0, 0
-    }));
-
-    auto inputTensor = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>(inputValues));
-
-    LayerTestResult<T, 4> result(outputTensorInfo);
-    result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, std::vector<T>(expectedOutputValues));
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::PadQueueDescriptor descriptor;
-
-    std::vector<std::pair<unsigned int, unsigned int>> PadList;
-    PadList.push_back(std::pair<unsigned int, unsigned int>(1,1));
-    PadList.push_back(std::pair<unsigned int, unsigned int>(2,1));
-    PadList.push_back(std::pair<unsigned int, unsigned int>(3,1));
-    PadList.push_back(std::pair<unsigned int, unsigned int>(1,1));
-
-    descriptor.m_Parameters.m_PadList = PadList;
-    armnn::WorkloadInfo info;
-
-    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePad(descriptor, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0][0][0]);
-
-    workloadFactory.Finalize();
-
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-
-    return result;
-}
-
-LayerTestResult<uint8_t, 2> PadUint82dTest(armnn::IWorkloadFactory& workloadFactory)
-{
-  return Pad2dTestCommon<uint8_t>(workloadFactory, 1.0f, 0);
-}
-
-LayerTestResult<uint8_t, 3> PadUint83dTest(armnn::IWorkloadFactory& workloadFactory)
-{
-  return Pad3dTestCommon<uint8_t>(workloadFactory, 1.0f, 0);
-}
-
-LayerTestResult<uint8_t, 4> PadUint84dTest(armnn::IWorkloadFactory& workloadFactory)
-{
-  return Pad4dTestCommon<uint8_t>(workloadFactory, 1.0f, 0);
-}
-
-LayerTestResult<float, 2> PadFloat322dTest(armnn::IWorkloadFactory& workloadFactory)
-{
-  return Pad2dTestCommon<float>(workloadFactory, 0.0f, 0);
-}
-
-LayerTestResult<float, 3> PadFloat323dTest(armnn::IWorkloadFactory& workloadFactory)
-{
-  return Pad3dTestCommon<float>(workloadFactory, 0.0f, 0);
-}
-
-LayerTestResult<float, 4> PadFloat324dTest(armnn::IWorkloadFactory& workloadFactory)
-{
-  return Pad4dTestCommon<float>(workloadFactory, 0.0f, 0);
-}
-
-LayerTestResult<float, 4> L2Normalization1dTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    // Width: 1
-    // Height: 1
-    // Channels: 10
-    // BatchSize: 1
-
-    const armnn::TensorShape inputOutputShape{ 1, 10, 1, 1 };
-    std::vector<float> inputValues
-    {
-        // Batch 0, Channel 0, Height (1) x Width (1)
-         1.0f,
-
-        // Batch 0, Channel 1, Height (1) x Width (1)
-         2.0f,
-
-        // Batch 0, Channel 2, Height (1) x Width (1)
-         3.0f,
-
-        // Batch 0, Channel 3, Height (1) x Width (1)
-         4.0f,
-
-        // Batch 0, Channel 4, Height (1) x Width (1)
-         5.0f,
-
-        // Batch 0, Channel 5, Height (1) x Width (1)
-         6.0f,
-
-        // Batch 0, Channel 6, Height (1) x Width (1)
-         7.0f,
-
-        // Batch 0, Channel 7, Height (1) x Width (1)
-         8.0f,
-
-        // Batch 0, Channel 8, Height (1) x Width (1)
-         9.0f,
-
-        // Batch 0, Channel 9, Height (1) x Width (1)
-        10.0f
-    };
-    const float approxInvL2Norm = 0.050964719f;
-    std::vector<float> expectedOutputValues
-    {
-        // Batch 0, Channel 0, Height (1) x Width (1)
-         1.0f * approxInvL2Norm,
-         2.0f * approxInvL2Norm,
-         3.0f * approxInvL2Norm,
-         4.0f * approxInvL2Norm,
-         5.0f * approxInvL2Norm,
-         6.0f * approxInvL2Norm,
-         7.0f * approxInvL2Norm,
-         8.0f * approxInvL2Norm,
-         9.0f * approxInvL2Norm,
-        10.0f * approxInvL2Norm
-    };
-
-    return L2NormalizationTestImpl(workloadFactory, inputOutputShape,
-                                   inputValues, expectedOutputValues, armnn::DataLayout::NCHW);
-}
-
-LayerTestResult<float, 4> L2Normalization1dNhwcTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    // Width: 1
-    // Height: 1
-    // Channels: 10
-    // BatchSize: 1
-
-    const armnn::TensorShape inputOutputShape{ 1, 1, 1, 10 };
-    std::vector<float> inputValues
-    {
-        // Batch 0, Height 0, Width (1) x Channel (10)
-        1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f
-    };
-    const float approxInvL2Norm = 0.050964719f;
-    std::vector<float> expectedOutputValues
-    {
-        // Batch 0, Height 0, Width (1) x Channel (10)
-         1.0f * approxInvL2Norm,
-         2.0f * approxInvL2Norm,
-         3.0f * approxInvL2Norm,
-         4.0f * approxInvL2Norm,
-         5.0f * approxInvL2Norm,
-         6.0f * approxInvL2Norm,
-         7.0f * approxInvL2Norm,
-         8.0f * approxInvL2Norm,
-         9.0f * approxInvL2Norm,
-        10.0f * approxInvL2Norm
-    };
-
-    return L2NormalizationTestImpl(workloadFactory, inputOutputShape,
-                                   inputValues, expectedOutputValues, armnn::DataLayout::NHWC);
-}
-
-LayerTestResult<float, 4> L2Normalization2dTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    // Width: 5
-    // Height: 1
-    // Channels: 2
-    // BatchSize: 1
-
-    const armnn::TensorShape inputOutputShape{ 1, 2, 1, 5 };
-    std::vector<float> inputValues
-    {
-        // Batch 0, Channel 0, Height (1) x Width (5)
-        1.0f, 3.0f, 5.0f, 7.0f,  9.0f,
-
-        // Batch 0, Channel 1, Height (1) x Width (5)
-        2.0f, 4.0f, 6.0f, 8.0f, 10.0f
-    };
-    std::vector<float> expectedOutputValues
-    {
-        // Batch 0, Channel 0, Height (1) x Width (5)
-         1.0f * CalcInvL2Norm({ 1.0f,  2.0f }),
-         3.0f * CalcInvL2Norm({ 3.0f,  4.0f }),
-         5.0f * CalcInvL2Norm({ 5.0f,  6.0f }),
-         7.0f * CalcInvL2Norm({ 7.0f,  8.0f }),
-         9.0f * CalcInvL2Norm({ 9.0f, 10.0f }),
-
-        // Batch 0, Channel 1, Height (1) x Width (5)
-         2.0f * CalcInvL2Norm({ 1.0f,  2.0f }),
-         4.0f * CalcInvL2Norm({ 3.0f,  4.0f }),
-         6.0f * CalcInvL2Norm({ 5.0f,  6.0f }),
-         8.0f * CalcInvL2Norm({ 7.0f,  8.0f }),
-        10.0f * CalcInvL2Norm({ 9.0f, 10.0f })
-    };
-
-    return L2NormalizationTestImpl(workloadFactory, inputOutputShape,
-                                   inputValues, expectedOutputValues, armnn::DataLayout::NCHW);
-}
-
-LayerTestResult<float, 4> L2Normalization2dNhwcTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    // Width: 5
-    // Height: 1
-    // Channels: 2
-    // BatchSize: 1
-
-    const armnn::TensorShape inputOutputShape{ 1, 1, 5, 2 };
-    std::vector<float> inputValues
-    {
-        // Batch 0, Height 0, Width (5) x Channel (2)
-        1.0f,  2.0f,
-        3.0f,  4.0f,
-        5.0f,  6.0f,
-        7.0f,  8.0f,
-        9.0f, 10.0f
-    };
-    std::vector<float> expectedOutputValues
-    {
-        // Batch 0, Height 0, Width (5) x Channel (2)
-        1.0f * CalcInvL2Norm({ 1.0f,  2.0f }),
-        2.0f * CalcInvL2Norm({ 1.0f,  2.0f }),
-        3.0f * CalcInvL2Norm({ 3.0f,  4.0f }),
-        4.0f * CalcInvL2Norm({ 3.0f,  4.0f }),
-        5.0f * CalcInvL2Norm({ 5.0f,  6.0f }),
-        6.0f * CalcInvL2Norm({ 5.0f,  6.0f }),
-        7.0f * CalcInvL2Norm({ 7.0f,  8.0f }),
-        8.0f * CalcInvL2Norm({ 7.0f,  8.0f }),
-        9.0f * CalcInvL2Norm({ 9.0f, 10.0f }),
-       10.0f * CalcInvL2Norm({ 9.0f, 10.0f })
-    };
-
-    return L2NormalizationTestImpl(workloadFactory, inputOutputShape,
-                                   inputValues, expectedOutputValues, armnn::DataLayout::NHWC);
-}
-
-LayerTestResult<float, 4> L2Normalization3dTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    // Width: 3
-    // Height: 4
-    // Channels: 2
-    // BatchSize: 1
-
-    const armnn::TensorShape inputOutputShape{ 1, 2, 4, 3 };
-    std::vector<float> inputValues
-    {
-        // Batch 0, Channel 0, Height (4) x Width (3)
-        119.0f,  21.0f, 150.0f,
-        149.0f,  32.0f, 179.0f,
-         15.0f, 227.0f, 141.0f,
-        147.0f, 199.0f, 220.0f,
-
-        // Batch 0, Channel 1, Height (4) x Width (3)
-        110.0f, 140.0f,  73.0f,
-        211.0f, 212.0f,  89.0f,
-         24.0f, 138.0f, 188.0f,
-        162.0f,  12.0f, 161.0f
-    };
-    std::vector<float> expectedOutputValues
-    {
-        // Batch 0, Channel 0, Height (4) x Width (3)
-        119.0f * CalcInvL2Norm({ 119.0f, 110.0f }),
-         21.0f * CalcInvL2Norm({  21.0f, 140.0f }),
-        150.0f * CalcInvL2Norm({ 150.0f,  73.0f }),
-        149.0f * CalcInvL2Norm({ 149.0f, 211.0f }),
-         32.0f * CalcInvL2Norm({  32.0f, 212.0f }),
-        179.0f * CalcInvL2Norm({ 179.0f,  89.0f }),
-         15.0f * CalcInvL2Norm({  15.0f,  24.0f }),
-        227.0f * CalcInvL2Norm({ 227.0f, 138.0f }),
-        141.0f * CalcInvL2Norm({ 141.0f, 188.0f }),
-        147.0f * CalcInvL2Norm({ 147.0f, 162.0f }),
-        199.0f * CalcInvL2Norm({ 199.0f,  12.0f }),
-        220.0f * CalcInvL2Norm({ 220.0f, 161.0f }),
-
-        // Batch 0, Channel 1, Height (4) x Width (3)
-        110.0f * CalcInvL2Norm({ 119.0f, 110.0f }),
-        140.0f * CalcInvL2Norm({  21.0f, 140.0f }),
-         73.0f * CalcInvL2Norm({ 150.0f,  73.0f }),
-        211.0f * CalcInvL2Norm({ 149.0f, 211.0f }),
-        212.0f * CalcInvL2Norm({  32.0f, 212.0f }),
-         89.0f * CalcInvL2Norm({ 179.0f,  89.0f }),
-         24.0f * CalcInvL2Norm({  15.0f,  24.0f }),
-        138.0f * CalcInvL2Norm({ 227.0f, 138.0f }),
-        188.0f * CalcInvL2Norm({ 141.0f, 188.0f }),
-        162.0f * CalcInvL2Norm({ 147.0f, 162.0f }),
-         12.0f * CalcInvL2Norm({ 199.0f,  12.0f }),
-        161.0f * CalcInvL2Norm({ 220.0f, 161.0f })
-    };
-
-    return L2NormalizationTestImpl(workloadFactory, inputOutputShape,
-                                   inputValues, expectedOutputValues, armnn::DataLayout::NCHW);
-}
-
-LayerTestResult<float, 4> L2Normalization3dNhwcTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    // Width: 3
-    // Height: 4
-    // Channels: 2
-    // BatchSize: 1
-
-    const armnn::TensorShape inputOutputShape{ 1, 4, 3, 2 };
-    std::vector<float> inputValues
-    {
-        // Batch 0, Height 0, Width (3) x Channel (2)
-        119.0f, 110.0f,
-         21.0f, 140.0f,
-        150.0f,  73.0f,
-
-        // Batch 0, Height 1, Width (3) x Channel (2)
-        149.0f, 211.0f,
-         32.0f, 212.0f,
-        179.0f,  89.0f,
-
-        // Batch 0, Height 2, Width (3) x Channel (2)
-         15.0f,  24.0f,
-        227.0f, 138.0f,
-        141.0f, 188.0f,
-
-        // Batch 0, Height 3, Width (3) x Channel (2)
-        147.0f, 162.0f,
-        199.0f,  12.0f,
-        220.0f, 161.0f
-    };
-    std::vector<float> expectedOutputValues
-    {
-        // Batch 0, Height 0, Width (3) x Channel (2)
-        119.0f * CalcInvL2Norm({ 119.0f, 110.0f }),
-        110.0f * CalcInvL2Norm({ 119.0f, 110.0f }),
-         21.0f * CalcInvL2Norm({  21.0f, 140.0f }),
-        140.0f * CalcInvL2Norm({  21.0f, 140.0f }),
-        150.0f * CalcInvL2Norm({ 150.0f,  73.0f }),
-         73.0f * CalcInvL2Norm({ 150.0f,  73.0f }),
-
-        // Batch 0, Height 1, Width (3) x Channel (2)
-        149.0f * CalcInvL2Norm({ 149.0f, 211.0f }),
-        211.0f * CalcInvL2Norm({ 149.0f, 211.0f }),
-         32.0f * CalcInvL2Norm({  32.0f, 212.0f }),
-        212.0f * CalcInvL2Norm({  32.0f, 212.0f }),
-        179.0f * CalcInvL2Norm({ 179.0f,  89.0f }),
-         89.0f * CalcInvL2Norm({ 179.0f,  89.0f }),
-
-        // Batch 0, Height 2, Width (3) x Channel (2)
-         15.0f * CalcInvL2Norm({  15.0f,  24.0f }),
-         24.0f * CalcInvL2Norm({  15.0f,  24.0f }),
-        227.0f * CalcInvL2Norm({ 227.0f, 138.0f }),
-        138.0f * CalcInvL2Norm({ 227.0f, 138.0f }),
-        141.0f * CalcInvL2Norm({ 141.0f, 188.0f }),
-        188.0f * CalcInvL2Norm({ 141.0f, 188.0f }),
-
-        // Batch 0, Height 3, Width (3) x Channel (2)
-        147.0f * CalcInvL2Norm({ 147.0f, 162.0f }),
-        162.0f * CalcInvL2Norm({ 147.0f, 162.0f }),
-        199.0f * CalcInvL2Norm({ 199.0f,  12.0f }),
-         12.0f * CalcInvL2Norm({ 199.0f,  12.0f }),
-        220.0f * CalcInvL2Norm({ 220.0f, 161.0f }),
-        161.0f * CalcInvL2Norm({ 220.0f, 161.0f })
-    };
-
-    return L2NormalizationTestImpl(workloadFactory, inputOutputShape,
-                                   inputValues, expectedOutputValues, armnn::DataLayout::NHWC);
-}
-
-LayerTestResult<float, 4> L2Normalization4dTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    // Width: 3
-    // Height: 4
-    // Channels: 3
-    // BatchSize: 2
-
-    const armnn::TensorShape inputOutputShape{ 2, 3, 4, 3 };
-    std::vector<float> inputValues
-    {
-        // Batch 0, Channel 0, Height (4) x Width (3)
-        235.0f,  46.0f, 178.0f,
-        100.0f, 123.0f,  19.0f,
-        172.0f,  74.0f, 250.0f,
-          6.0f, 195.0f,  80.0f,
-
-        // Batch 0, Channel 1, Height (4) x Width (3)
-        113.0f,  95.0f, 202.0f,
-         77.0f, 114.0f,  71.0f,
-        122.0f, 246.0f, 166.0f,
-         82.0f,  28.0f,  37.0f,
-
-        // Batch 0, Channel 2, Height (4) x Width (3)
-         56.0f, 170.0f, 162.0f,
-        194.0f,  89.0f, 254.0f,
-         12.0f, 209.0f, 200.0f,
-          1.0f,  64.0f,  54.0f,
-
-        // Batch 1, Channel 0, Height (4) x Width (3)
-         67.0f,  90.0f,  49.0f,
-          7.0f, 163.0f,  18.0f,
-         25.0f, 117.0f, 103.0f,
-        247.0f,  59.0f, 189.0f,
-
-        // Batch 1, Channel 1, Height (4) x Width (3)
-        239.0f, 104.0f, 199.0f,
-         17.0f, 124.0f, 153.0f,
-        222.0f, 217.0f, 75.0f,
-         32.0f, 126.0f, 21.0f,
-
-        // Batch 1, Channel 2, Height (4) x Width (3)
-         97.0f, 145.0f, 215.0f,
-        115.0f, 116.0f, 238.0f,
-        226.0f,  16.0f, 132.0f,
-         92.0f, 125.0f,  88.0f
-    };
-    std::vector<float> expectedOutputValues
-    {
-        // Batch 0, Channel 0, Height (4) x Width (3)
-        235.0f * CalcInvL2Norm({ 235.0f, 113.0f,  56.0f }),
-         46.0f * CalcInvL2Norm({  46.0f,  95.0f, 170.0f }),
-        178.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }),
-        100.0f * CalcInvL2Norm({ 100.0f,  77.0f, 194.0f }),
-        123.0f * CalcInvL2Norm({ 123.0f, 114.0f,  89.0f }),
-         19.0f * CalcInvL2Norm({  19.0f,  71.0f, 254.0f }),
-        172.0f * CalcInvL2Norm({ 172.0f, 122.0f,  12.0f }),
-         74.0f * CalcInvL2Norm({  74.0f, 246.0f, 209.0f }),
-        250.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }),
-          6.0f * CalcInvL2Norm({   6.0f,  82.0f,   1.0f }),
-        195.0f * CalcInvL2Norm({ 195.0f,  28.0f,  64.0f }),
-         80.0f * CalcInvL2Norm({  80.0f,  37.0f,  54.0f }),
-
-        // Batch 0, Channel 1, Height (4) x Width (3)
-        113.0f * CalcInvL2Norm({ 235.0f, 113.0f,  56.0f }),
-         95.0f * CalcInvL2Norm({  46.0f,  95.0f, 170.0f }),
-        202.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }),
-         77.0f * CalcInvL2Norm({ 100.0f,  77.0f, 194.0f }),
-        114.0f * CalcInvL2Norm({ 123.0f, 114.0f,  89.0f }),
-         71.0f * CalcInvL2Norm({  19.0f,  71.0f, 254.0f }),
-        122.0f * CalcInvL2Norm({ 172.0f, 122.0f,  12.0f }),
-        246.0f * CalcInvL2Norm({  74.0f, 246.0f, 209.0f }),
-        166.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }),
-         82.0f * CalcInvL2Norm({   6.0f,  82.0f,   1.0f }),
-         28.0f * CalcInvL2Norm({ 195.0f,  28.0f,  64.0f }),
-         37.0f * CalcInvL2Norm({  80.0f,  37.0f,  54.0f }),
-
-        // Batch 0, Channel 2, Height (4) x Width (3)
-         56.0f * CalcInvL2Norm({ 235.0f, 113.0f,  56.0f }),
-        170.0f * CalcInvL2Norm({  46.0f,  95.0f, 170.0f }),
-        162.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }),
-        194.0f * CalcInvL2Norm({ 100.0f,  77.0f, 194.0f }),
-         89.0f * CalcInvL2Norm({ 123.0f, 114.0f,  89.0f }),
-        254.0f * CalcInvL2Norm({  19.0f,  71.0f, 254.0f }),
-         12.0f * CalcInvL2Norm({ 172.0f, 122.0f,  12.0f }),
-        209.0f * CalcInvL2Norm({  74.0f, 246.0f, 209.0f }),
-        200.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }),
-          1.0f * CalcInvL2Norm({   6.0f,  82.0f,   1.0f }),
-         64.0f * CalcInvL2Norm({ 195.0f,  28.0f,  64.0f }),
-         54.0f * CalcInvL2Norm({  80.0f,  37.0f,  54.0f }),
-
-        // Batch 1, Channel 0, Height (4) x Width (3)
-         67.0f * CalcInvL2Norm({  67.0f, 239.0f,  97.0f }),
-         90.0f * CalcInvL2Norm({  90.0f, 104.0f, 145.0f }),
-         49.0f * CalcInvL2Norm({  49.0f, 199.0f, 215.0f }),
-          7.0f * CalcInvL2Norm({   7.0f,  17.0f, 115.0f }),
-        163.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }),
-         18.0f * CalcInvL2Norm({  18.0f, 153.0f, 238.0f }),
-         25.0f * CalcInvL2Norm({  25.0f, 222.0f, 226.0f }),
-        117.0f * CalcInvL2Norm({ 117.0f, 217.0f,  16.0f }),
-        103.0f * CalcInvL2Norm({ 103.0f,  75.0f, 132.0f }),
-        247.0f * CalcInvL2Norm({ 247.0f,  32.0f,  92.0f }),
-         59.0f * CalcInvL2Norm({  59.0f, 126.0f, 125.0f }),
-        189.0f * CalcInvL2Norm({ 189.0f,  21.0f,  88.0f }),
-
-        // Batch 1, Channel 1, Height (4) x Width (3)
-        239.0f * CalcInvL2Norm({  67.0f, 239.0f,  97.0f }),
-        104.0f * CalcInvL2Norm({  90.0f, 104.0f, 145.0f }),
-        199.0f * CalcInvL2Norm({  49.0f, 199.0f, 215.0f }),
-         17.0f * CalcInvL2Norm({   7.0f,  17.0f, 115.0f }),
-        124.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }),
-        153.0f * CalcInvL2Norm({  18.0f, 153.0f, 238.0f }),
-        222.0f * CalcInvL2Norm({  25.0f, 222.0f, 226.0f }),
-        217.0f * CalcInvL2Norm({ 117.0f, 217.0f,  16.0f }),
-         75.0f * CalcInvL2Norm({ 103.0f,  75.0f, 132.0f }),
-         32.0f * CalcInvL2Norm({ 247.0f,  32.0f,  92.0f }),
-        126.0f * CalcInvL2Norm({  59.0f, 126.0f, 125.0f }),
-         21.0f * CalcInvL2Norm({ 189.0f,  21.0f,  88.0f }),
-
-        // Batch 1, Channel 2, Height (4) x Width (3)
-         97.0f * CalcInvL2Norm({  67.0f, 239.0f,  97.0f }),
-        145.0f * CalcInvL2Norm({  90.0f, 104.0f, 145.0f }),
-        215.0f * CalcInvL2Norm({  49.0f, 199.0f, 215.0f }),
-        115.0f * CalcInvL2Norm({   7.0f,  17.0f, 115.0f }),
-        116.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }),
-        238.0f * CalcInvL2Norm({  18.0f, 153.0f, 238.0f }),
-        226.0f * CalcInvL2Norm({  25.0f, 222.0f, 226.0f }),
-         16.0f * CalcInvL2Norm({ 117.0f, 217.0f,  16.0f }),
-        132.0f * CalcInvL2Norm({ 103.0f,  75.0f, 132.0f }),
-         92.0f * CalcInvL2Norm({ 247.0f,  32.0f,  92.0f }),
-        125.0f * CalcInvL2Norm({  59.0f, 126.0f, 125.0f }),
-         88.0f * CalcInvL2Norm({ 189.0f,  21.0f,  88.0f })
-    };
-
-    return L2NormalizationTestImpl(workloadFactory, inputOutputShape,
-                                   inputValues, expectedOutputValues, armnn::DataLayout::NCHW);
-}
-
-LayerTestResult<float, 4> L2Normalization4dNhwcTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    // Width: 3
-    // Height: 4
-    // Channels: 3
-    // BatchSize: 2
-
-    const armnn::TensorShape inputOutputShape{ 2, 4, 3, 3 };
-    std::vector<float> inputValues
-    {
-        // Batch 0, Height 0, Width (3) x Channel (3)
-        235.0f, 113.0f,  56.0f,
-         46.0f,  95.0f, 170.0f,
-        178.0f, 202.0f, 162.0f,
-
-        // Batch 0, Height 1, Width (3) x Channel (3)
-        100.0f,  77.0f, 194.0f,
-        123.0f, 114.0f,  89.0f,
-         19.0f,  71.0f, 254.0f,
-
-        // Batch 0, Height 2, Width (3) x Channel (3)
-        172.0f, 122.0f,  12.0f,
-         74.0f, 246.0f, 209.0f,
-        250.0f, 166.0f, 200.0f,
-
-        // Batch 0, Height 3, Width (3) x Channel (3)
-          6.0f,  82.0f,   1.0f,
-        195.0f,  28.0f,  64.0f,
-         80.0f,  37.0f,  54.0f,
-
-        // Batch 1, Height 0, Width (3) x Channel (3)
-         67.0f, 239.0f,  97.0f,
-         90.0f, 104.0f, 145.0f,
-         49.0f, 199.0f, 215.0f,
-
-        // Batch 1, Height 1, Width (3) x Channel (3)
-          7.0f,  17.0f, 115.0f,
-        163.0f, 124.0f, 116.0f,
-         18.0f, 153.0f, 238.0f,
-
-        // Batch 1, Height 2, Width (3) x Channel (3)
-         25.0f, 222.0f, 226.0f,
-        117.0f, 217.0f,  16.0f,
-        103.0f,  75.0f, 132.0f,
-
-        // Batch 1, Height 3, Width (3) x Channel (3)
-        247.0f,  32.0f,  92.0f,
-         59.0f, 126.0f, 125.0f,
-        189.0f,  21.0f,  88.0f
-    };
-    std::vector<float> expectedOutputValues
-    {
-        // Batch 0, Height 0, Width (3) x Channel (3)
-        235.0f * CalcInvL2Norm({ 235.0f, 113.0f,  56.0f }),
-        113.0f * CalcInvL2Norm({ 235.0f, 113.0f,  56.0f }),
-         56.0f * CalcInvL2Norm({ 235.0f, 113.0f,  56.0f }),
-         46.0f * CalcInvL2Norm({  46.0f,  95.0f, 170.0f }),
-         95.0f * CalcInvL2Norm({  46.0f,  95.0f, 170.0f }),
-        170.0f * CalcInvL2Norm({  46.0f,  95.0f, 170.0f }),
-        178.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }),
-        202.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }),
-        162.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }),
-
-        // Batch 0, Height 1, Width (3) x Channel (3)
-        100.0f * CalcInvL2Norm({ 100.0f,  77.0f, 194.0f }),
-         77.0f * CalcInvL2Norm({ 100.0f,  77.0f, 194.0f }),
-        194.0f * CalcInvL2Norm({ 100.0f,  77.0f, 194.0f }),
-        123.0f * CalcInvL2Norm({ 123.0f, 114.0f,  89.0f }),
-        114.0f * CalcInvL2Norm({ 123.0f, 114.0f,  89.0f }),
-         89.0f * CalcInvL2Norm({ 123.0f, 114.0f,  89.0f }),
-         19.0f * CalcInvL2Norm({  19.0f,  71.0f, 254.0f }),
-         71.0f * CalcInvL2Norm({  19.0f,  71.0f, 254.0f }),
-        254.0f * CalcInvL2Norm({  19.0f,  71.0f, 254.0f }),
-
-        // Batch 0, Height 2, Width (3) x Channel (3)
-        172.0f * CalcInvL2Norm({ 172.0f, 122.0f,  12.0f }),
-        122.0f * CalcInvL2Norm({ 172.0f, 122.0f,  12.0f }),
-         12.0f * CalcInvL2Norm({ 172.0f, 122.0f,  12.0f }),
-         74.0f * CalcInvL2Norm({  74.0f, 246.0f, 209.0f }),
-        246.0f * CalcInvL2Norm({  74.0f, 246.0f, 209.0f }),
-        209.0f * CalcInvL2Norm({  74.0f, 246.0f, 209.0f }),
-        250.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }),
-        166.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }),
-        200.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }),
-
-        // Batch 0, Height 3, Width (3) x Channel (3)
-          6.0f * CalcInvL2Norm({   6.0f,  82.0f,   1.0f }),
-         82.0f * CalcInvL2Norm({   6.0f,  82.0f,   1.0f }),
-          1.0f * CalcInvL2Norm({   6.0f,  82.0f,   1.0f }),
-        195.0f * CalcInvL2Norm({ 195.0f,  28.0f,  64.0f }),
-         28.0f * CalcInvL2Norm({ 195.0f,  28.0f,  64.0f }),
-         64.0f * CalcInvL2Norm({ 195.0f,  28.0f,  64.0f }),
-         80.0f * CalcInvL2Norm({  80.0f,  37.0f,  54.0f }),
-         37.0f * CalcInvL2Norm({  80.0f,  37.0f,  54.0f }),
-         54.0f * CalcInvL2Norm({  80.0f,  37.0f,  54.0f }),
-
-        // Batch 1, Height 0, Width (3) x Channel (3)
-         67.0f * CalcInvL2Norm({  67.0f, 239.0f,  97.0f }),
-        239.0f * CalcInvL2Norm({  67.0f, 239.0f,  97.0f }),
-         97.0f * CalcInvL2Norm({  67.0f, 239.0f,  97.0f }),
-         90.0f * CalcInvL2Norm({  90.0f, 104.0f, 145.0f }),
-        104.0f * CalcInvL2Norm({  90.0f, 104.0f, 145.0f }),
-        145.0f * CalcInvL2Norm({  90.0f, 104.0f, 145.0f }),
-         49.0f * CalcInvL2Norm({  49.0f, 199.0f, 215.0f }),
-        199.0f * CalcInvL2Norm({  49.0f, 199.0f, 215.0f }),
-        215.0f * CalcInvL2Norm({  49.0f, 199.0f, 215.0f }),
-
-        // Batch 1, Height 1, Width (3) x Channel (3)
-          7.0f * CalcInvL2Norm({   7.0f,  17.0f, 115.0f }),
-         17.0f * CalcInvL2Norm({   7.0f,  17.0f, 115.0f }),
-        115.0f * CalcInvL2Norm({   7.0f,  17.0f, 115.0f }),
-        163.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }),
-        124.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }),
-        116.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }),
-         18.0f * CalcInvL2Norm({  18.0f, 153.0f, 238.0f }),
-        153.0f * CalcInvL2Norm({  18.0f, 153.0f, 238.0f }),
-        238.0f * CalcInvL2Norm({  18.0f, 153.0f, 238.0f }),
-
-        // Batch 1, Height 2, Width (3) x Channel (3)
-         25.0f * CalcInvL2Norm({  25.0f, 222.0f, 226.0f }),
-        222.0f * CalcInvL2Norm({  25.0f, 222.0f, 226.0f }),
-        226.0f * CalcInvL2Norm({  25.0f, 222.0f, 226.0f }),
-        117.0f * CalcInvL2Norm({ 117.0f, 217.0f,  16.0f }),
-        217.0f * CalcInvL2Norm({ 117.0f, 217.0f,  16.0f }),
-         16.0f * CalcInvL2Norm({ 117.0f, 217.0f,  16.0f }),
-        103.0f * CalcInvL2Norm({ 103.0f,  75.0f, 132.0f }),
-         75.0f * CalcInvL2Norm({ 103.0f,  75.0f, 132.0f }),
-        132.0f * CalcInvL2Norm({ 103.0f,  75.0f, 132.0f }),
-
-        // Batch 1, Height 3, Width (3) x Channel (3)
-        247.0f * CalcInvL2Norm({ 247.0f,  32.0f,  92.0f }),
-         32.0f * CalcInvL2Norm({ 247.0f,  32.0f,  92.0f }),
-         92.0f * CalcInvL2Norm({ 247.0f,  32.0f,  92.0f }),
-         59.0f * CalcInvL2Norm({  59.0f, 126.0f, 125.0f }),
-        126.0f * CalcInvL2Norm({  59.0f, 126.0f, 125.0f }),
-        125.0f * CalcInvL2Norm({  59.0f, 126.0f, 125.0f }),
-        189.0f * CalcInvL2Norm({ 189.0f,  21.0f,  88.0f }),
-         21.0f * CalcInvL2Norm({ 189.0f,  21.0f,  88.0f }),
-         88.0f * CalcInvL2Norm({ 189.0f,  21.0f,  88.0f })
-    };
-
-    return L2NormalizationTestImpl(workloadFactory, inputOutputShape,
-                                   inputValues, expectedOutputValues, armnn::DataLayout::NHWC);
-}
-
-template <typename T>
-LayerTestResult<T, 4> ConstantTestImpl(armnn::IWorkloadFactory& workloadFactory,
-    float qScale,
-    int32_t qOffset)
-{
-    constexpr unsigned int inputWidth = 3;
-    constexpr unsigned int inputHeight = 4;
-    constexpr unsigned int inputChannels = 3;
-    constexpr unsigned int inputBatchSize = 2;
-
-    constexpr unsigned int outputWidth = inputWidth;
-    constexpr unsigned int outputHeight = inputHeight;
-    constexpr unsigned int outputChannels = inputChannels;
-    constexpr unsigned int outputBatchSize = inputBatchSize;
-
-    armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
-        armnn::GetDataType<T>());
-
-    armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
-        armnn::GetDataType<T>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>(
-        QuantizedVector<T>(qScale, qOffset, {
-        // Batch 0, Channel 0
-        235.0f,  46.0f, 178.0f,
-        100.0f, 123.0f,  19.0f,
-        172.0f,  74.0f, 250.0f,
-          6.0f, 195.0f,  80.0f,
-
-        // Batch 0, Channel 1
-        113.0f,  95.0f, 202.0f,
-         77.0f, 114.0f,  71.0f,
-        122.0f, 246.0f, 166.0f,
-         82.0f,  28.0f,  37.0f,
-
-        // Batch 0, Channel 2
-         56.0f, 170.0f, 162.0f,
-        194.0f,  89.0f, 254.0f,
-         12.0f, 209.0f, 200.0f,
-          1.0f,  64.0f,  54.0f,
-
-        // Batch 1, Channel 0
-         67.0f,  90.0f,  49.0f,
-          7.0f, 163.0f,  18.0f,
-         25.0f, 117.0f, 103.0f,
-        247.0f,  59.0f, 189.0f,
-
-        // Batch 1, Channel 1
-        239.0f, 104.0f, 199.0f,
-         17.0f, 124.0f, 153.0f,
-        222.0f, 217.0f, 75.0f,
-         32.0f, 126.0f, 21.0f,
-
-        // Batch 1, Channel 2
-         97.0f, 145.0f, 215.0f,
-        115.0f, 116.0f, 238.0f,
-        226.0f,  16.0f, 132.0f,
-         92.0f, 125.0f,  88.0f,
-    })));
-
-    LayerTestResult<T, 4> result(outputTensorInfo);
-    result.outputExpected = input;
-
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ScopedCpuTensorHandle constantTensor(inputTensorInfo);
-    AllocateAndCopyDataToITensorHandle(&constantTensor, &input[0][0][0][0]);
-
-    armnn::ConstantQueueDescriptor descriptor;
-    descriptor.m_LayerOutput = &constantTensor;
-
-    armnn::WorkloadInfo info;
-    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConstant(descriptor, info);
-
-    outputHandle->Allocate();
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-    return result;
-}
-
-LayerTestResult<float, 4> ConstantTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    return ConstantTestImpl<float>(workloadFactory, 0.0f, 0);
-}
-
-LayerTestResult<uint8_t, 4> ConstantTestUint8(armnn::IWorkloadFactory& workloadFactory)
-{
-    return ConstantTestImpl<uint8_t>(workloadFactory, 1.0f, 0);
-}
-
-LayerTestResult<uint8_t, 3> MergerUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    unsigned int outputWidth = 3;
-    unsigned int outputHeight = 6;
-    unsigned int outputChannels = 3;
-
-    unsigned int inputWidth1 = 3;
-    unsigned int inputHeight1 = 6;
-    unsigned int inputChannels1 = 2;
-
-    unsigned int inputWidth2 = 3;
-    unsigned int inputHeight2 = 6;
-    unsigned int inputChannels2 = 1;
-
-    // Defines the tensor descriptors.
-    armnn::TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, armnn::DataType::QuantisedAsymm8);
-    armnn::TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, armnn::DataType::QuantisedAsymm8);
-    armnn::TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, armnn::DataType::QuantisedAsymm8);
-
-    // Arbitrary scale and offsets. They don't really matter as the merger operator doesn't dequantize/quantize them.
-    const float scale = 0.13497836f;
-    const int32_t offset = -7;
-
-    outputTensorInfo.SetQuantizationScale(scale);
-    outputTensorInfo.SetQuantizationOffset(offset);
-    inputTensorInfo1.SetQuantizationScale(scale);
-    inputTensorInfo1.SetQuantizationOffset(offset);
-    inputTensorInfo2.SetQuantizationScale(scale);
-    inputTensorInfo2.SetQuantizationOffset(offset);
-
-    LayerTestResult<uint8_t, 3> ret(outputTensorInfo);
-
-    ret.outputExpected = MakeTensor<uint8_t, 3>(outputTensorInfo, std::vector<uint8_t>(
-        {
-            1, 2, 3,
-            4, 5, 6,
-            7, 8, 9,
-            10, 11, 12,
-            13, 14, 15,
-            16, 17, 18,
-
-            19, 20, 21,
-            22, 23, 24,
-            25, 26, 27,
-            28, 29, 30,
-            31, 32, 33,
-            34, 35, 36,
-
-            37, 38, 39,
-            40, 41, 42,
-            43, 44, 45,
-            46, 47, 48,
-            49, 50, 51,
-            52, 53, 54,
-        })
-    );
-
-    auto input1 = MakeTensor<uint8_t, 3>(inputTensorInfo1, std::vector<uint8_t>(
-    {
-        1, 2, 3,
-        4, 5, 6,
-        7, 8, 9,
-        10, 11, 12,
-        13, 14, 15,
-        16, 17, 18,
-
-        19, 20, 21,
-        22, 23, 24,
-        25, 26, 27,
-        28, 29, 30,
-        31, 32, 33,
-        34, 35, 36,
-    })
-    );
-
-    auto input2 = MakeTensor<uint8_t, 3>(inputTensorInfo2, std::vector<uint8_t>(
-    {
-        37, 38, 39,
-        40, 41, 42,
-        43, 44, 45,
-        46, 47, 48,
-        49, 50, 51,
-        52, 53, 54,
-    })
-    );
-
-    std::vector<unsigned int> wOrigin1 = { 0, 0, 0 }; //Extent of the window is defined by size of input[0].
-    armnn::MergerQueueDescriptor::ViewOrigin window1(wOrigin1);
-
-    std::vector<unsigned int> wOrigin2 = { 2, 0, 0 }; //Extent of the window is defined by size of input[1].
-    armnn::MergerQueueDescriptor::ViewOrigin window2(wOrigin2);
-
-
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    bool subTensorsSupported = workloadFactory.SupportsSubTensors();
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle1 =
-        subTensorsSupported ?
-            workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) :
-            workloadFactory.CreateTensorHandle(inputTensorInfo1);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle2 =
-        subTensorsSupported ?
-            workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) :
-            workloadFactory.CreateTensorHandle(inputTensorInfo2);
-
-
-    armnn::MergerQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
-    AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    data.m_ViewOrigins.push_back(window1);
-    data.m_ViewOrigins.push_back(window2);
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMerger(data, info);
-
-    inputHandle1->Allocate();
-    inputHandle2->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]);
-    CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get());
-
-    return ret;
-}
-
-LayerTestResult<uint8_t, 4> AdditionUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    unsigned int batchSize = 1;
-    unsigned int channels = 2;
-    unsigned int height = 2;
-    unsigned int width = 3;
-
-    const float scale = 7.0f;
-    const int32_t offset = 3;
-
-    armnn::TensorInfo inputTensorInfo1, inputTensorInfo2;
-    armnn::TensorInfo outputTensorInfo;
-
-    const unsigned int shape[] = { batchSize, channels, height, width };
-    inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::QuantisedAsymm8);
-    inputTensorInfo1.SetQuantizationScale(scale);
-    inputTensorInfo1.SetQuantizationOffset(offset);
-
-    inputTensorInfo2 = armnn::TensorInfo(4, shape, armnn::DataType::QuantisedAsymm8);
-    inputTensorInfo2.SetQuantizationScale(scale);
-    inputTensorInfo2.SetQuantizationOffset(offset);
-
-    outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::QuantisedAsymm8);
-    outputTensorInfo.SetQuantizationScale(scale);
-    outputTensorInfo.SetQuantizationOffset(offset);
-
-    // See dequantized values to the right.
-    auto input1 = MakeTensor<uint8_t, 4>(inputTensorInfo1, std::vector<uint8_t>(
-    {
-         63,  35,  77,  70,  56, 112, //  420, 224,  518,  469,  371, 763
-        203,  28, 252, 168, 245,  91  // 1400, 175, 1743, 1155, 1694, 616
-    }));
-
-    // See dequantized values to the right.
-    auto input2 = MakeTensor<uint8_t, 4>(inputTensorInfo1, std::vector<uint8_t>(
-    {
-         21,   7, 175, 231, 175, 210, // 126,   28, 1204, 1596, 1204, 1449
-        126, 161,  63,  21, 105, 126  // 861, 1106,  420,  126,  714,  861
-    }));
-
-    // See dequantized values to the right.
-    LayerTestResult<uint8_t, 4> result(outputTensorInfo);
-    result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, std::vector<uint8_t>(
-    {
-         81,  39, 249, 255, 228, 255, //  546,  252, 1722, 2065(clamped), 1575, 2212(clamped)
-        255, 186, 255, 186, 255, 214, // 2261(clamped), 1281, 2163(clamped), 1281, 2408(clamped), 1477
-    }));
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
-    std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::AdditionQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());
-    AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info);
-
-    inputHandle1->Allocate();
-    inputHandle2->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
-    CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-
-    return result;
-}
-
-namespace
-{
-LayerTestResult<uint8_t, 4> MultiplicationUint8TestHelper(armnn::IWorkloadFactory& workloadFactory,
-                                                          const unsigned int shape0[4],
-                                                          const std::vector<uint8_t> & values0,
-                                                          float scale0,
-                                                          int32_t offset0,
-                                                          const unsigned int shape1[4],
-                                                          const std::vector<uint8_t> & values1,
-                                                          float scale1,
-                                                          int32_t offset1,
-                                                          const unsigned int outShape[4],
-                                                          const std::vector<uint8_t> & outValues,
-                                                          float outScale,
-                                                          int32_t outOffset)
-{
-    armnn::TensorInfo inputTensorInfo0(4, shape0, armnn::DataType::QuantisedAsymm8);
-    armnn::TensorInfo inputTensorInfo1(4, shape1, armnn::DataType::QuantisedAsymm8);
-    armnn::TensorInfo outputTensorInfo(4, outShape, armnn::DataType::QuantisedAsymm8);
-
-    inputTensorInfo0.SetQuantizationScale(scale0);
-    inputTensorInfo0.SetQuantizationOffset(offset0);
-
-    inputTensorInfo1.SetQuantizationScale(scale1);
-    inputTensorInfo1.SetQuantizationOffset(offset1);
-
-    outputTensorInfo.SetQuantizationScale(outScale);
-    outputTensorInfo.SetQuantizationOffset(outOffset);
-
-    auto input0 = MakeTensor<uint8_t, 4>(inputTensorInfo0, values0);
-    auto input1 = MakeTensor<uint8_t, 4>(inputTensorInfo1, values1);
-
-    LayerTestResult<uint8_t, 4> result(outputTensorInfo);
-    result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, outValues);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0);
-    std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::MultiplicationQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data,  info, inputTensorInfo0, inputHandle0.get());
-    AddInputToWorkload(data,  info, inputTensorInfo1, inputHandle1.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMultiplication(data, info);
-
-    inputHandle0->Allocate();
-    inputHandle1->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]);
-    CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-
-    return result;
-}
-} // anonymous namespace
-
-LayerTestResult<uint8_t, 4> MultiplicationUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    unsigned int batchSize = 1;
-    unsigned int channels = 2;
-    unsigned int height = 2;
-    unsigned int width = 3;
-    const unsigned int shape[] = { batchSize, channels, height, width };
-
-    // See dequantized values to the right.
-    std::vector<uint8_t> input0({
-         62,  37,   3, 172,  13, 111, // 244, 144,   8, 684,  48, 440,
-        188,  20,  73,  31,  23,  31  // 748,  76, 288, 120,  88, 120
-    });
-
-    // See dequantized values to the right.
-    std::vector<uint8_t> input1({
-        126, 240, 252, 183, 121, 247, // 384, 726, 762, 555, 369, 747,
-         48, 115, 151,  79,  78,  97  // 150, 351, 459, 243, 240, 297
-    });
-
-    // See dequantized values to the right.
-    std::vector<uint8_t> output(
-    {
-         64,  72,   0, 255,   8, 236, //  93696, 104544, 6096(clamped), 379620(clamped), 17712, 328680,
-         77,  15,  92,  16,  10,  21, // 112200,  26676,        132192,           29160, 21120,  35640
-    });
-
-    return MultiplicationUint8TestHelper(workloadFactory,
-                                         shape,
-                                         input0,
-                                         4.0f,
-                                         1,
-                                         shape,
-                                         input1,
-                                         3.0f,
-                                         -2,
-                                         shape,
-                                         output,
-                                         1366.255f, // Scale/offset chosen to have output values out of range.
-                                         -5);
-}
-
-LayerTestResult<uint8_t, 4> MultiplicationBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    const unsigned int shape0[] = { 1, 2, 2, 3 };
-    const unsigned int shape1[] = { 1, 1, 1, 1 };
-
-    std::vector<uint8_t> input0({
-        1, 2, 3,    4,  5,  6,
-        7, 8, 9,   10, 11, 12
-    });
-
-    std::vector<uint8_t> input1({2});
-
-    std::vector<uint8_t> output({
-        2,  4,   6,     8, 10, 12,
-        14, 16, 18,    20, 22, 24
-    });
-
-    return MultiplicationUint8TestHelper(workloadFactory,
-                                         shape0,
-                                         input0,
-                                         1.0f,
-                                         0,
-                                         shape1,
-                                         input1,
-                                         1.0f,
-                                         0,
-                                         shape0,
-                                         output,
-                                         1.0f,
-                                         0);
-}
-
-LayerTestResult<uint8_t, 4> MultiplicationBroadcast1DVectorUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    const unsigned int shape0[] = { 1, 2, 2, 3 };
-    const unsigned int shape1[] = { 1, 1, 1, 3 };
-
-    std::vector<uint8_t> input0({
-        1, 2, 3,    4,  5,  6,
-        7, 8, 9,   10, 11, 12
-    });
-
-    std::vector<uint8_t> input1({1, 2, 3});
-
-    std::vector<uint8_t> output({
-        1,  4,   9,     4, 10, 18,
-        7, 16,  27,    10, 22, 36
-    });
-
-    return MultiplicationUint8TestHelper(workloadFactory,
-                                         shape0,
-                                         input0,
-                                         1.0f,
-                                         0,
-                                         shape1,
-                                         input1,
-                                         1.0f,
-                                         0,
-                                         shape0,
-                                         output,
-                                         1.0f,
-                                         0);
-}
-
-namespace
-{
-template <typename T>
-LayerTestResult<T, 4> SubtractionTestHelper(armnn::IWorkloadFactory& workloadFactory,
-                                            const unsigned int shape0[4],
-                                            const std::vector<T>& values0,
-                                            float scale0,
-                                            int32_t offset0,
-                                            const unsigned int shape1[4],
-                                            const std::vector<T> & values1,
-                                            float scale1,
-                                            int32_t offset1,
-                                            const unsigned int outShape[4],
-                                            const std::vector<T> & outValues,
-                                            float outScale,
-                                            int32_t outOffset)
-{
-    auto dataType = (std::is_same<T, uint8_t>::value ?
-                     armnn::DataType::QuantisedAsymm8 :
-                     armnn::DataType::Float32);
-
-    armnn::TensorInfo inputTensorInfo0(4, shape0, dataType);
-    armnn::TensorInfo inputTensorInfo1(4, shape1, dataType);
-    armnn::TensorInfo outputTensorInfo(4, outShape, dataType);
-
-    inputTensorInfo0.SetQuantizationScale(scale0);
-    inputTensorInfo0.SetQuantizationOffset(offset0);
-
-    inputTensorInfo1.SetQuantizationScale(scale1);
-    inputTensorInfo1.SetQuantizationOffset(offset1);
-
-    outputTensorInfo.SetQuantizationScale(outScale);
-    outputTensorInfo.SetQuantizationOffset(outOffset);
-
-    auto input0 = MakeTensor<T, 4>(inputTensorInfo0, values0);
-    auto input1 = MakeTensor<T, 4>(inputTensorInfo1, values1);
-
-    LayerTestResult<T, 4> result(outputTensorInfo);
-    result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outValues);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0);
-    std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::SubtractionQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data,  info, inputTensorInfo0, inputHandle0.get());
-    AddInputToWorkload(data,  info, inputTensorInfo1, inputHandle1.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateSubtraction(data, info);
-
-    inputHandle0->Allocate();
-    inputHandle1->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]);
-    CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-
-    return result;
-}
-} // anonymous namespace
-
-LayerTestResult<uint8_t, 4> SubtractionUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    const unsigned int shape0[] = { 1, 1, 2, 2 };
-    const unsigned int shape1[] = { 1, 1, 2, 2 };
-
-    std::vector<uint8_t> input0({ 10, 12, 14, 16 });
-    std::vector<uint8_t> input1({ 1, 2, 1, 2 });
-    std::vector<uint8_t> output({ 3, 3, 5, 5 });
-
-    return SubtractionTestHelper(workloadFactory,
-                                 shape0, input0, 0.5f, 2,
-                                 shape1, input1, 1.0f, 0,
-                                 shape0, output, 1.0f, 0);
-}
-
-LayerTestResult<uint8_t, 4> SubtractionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    const unsigned int shape0[] = { 1, 1, 2, 2 };
-    const unsigned int shape1[] = { 1, 1, 1, 1 };
-
-    std::vector<uint8_t> input0({ 10, 12, 14, 16 });
-    std::vector<uint8_t> input1({ 2 });
-    std::vector<uint8_t> output({ 5, 6, 7, 8 });
-
-    return SubtractionTestHelper(workloadFactory,
-                                 shape0, input0, 0.5f, 2,
-                                 shape1, input1, 1.0f, 0,
-                                 shape0, output, 1.0f, 3);
-}
-
-LayerTestResult<uint8_t, 4> SubtractionBroadcastUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    const unsigned int shape0[] = { 1, 1, 2, 2 };
-    const unsigned int shape1[] = { 1, 1, 2, 1 };
-
-    std::vector<uint8_t> input0({ 10, 12, 14, 16 });
-    std::vector<uint8_t> input1({ 2, 1 });
-    std::vector<uint8_t> output({ 8, 11, 12, 15 });
-
-    return SubtractionTestHelper(workloadFactory,
-                                 shape0, input0, 1.0f, 0,
-                                 shape1, input1, 1.0f, 0,
-                                 shape0, output, 1.0f, 0);
-}
-
-LayerTestResult<float, 4> SubtractionTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    const unsigned int shape0[] = { 1, 1, 2, 2 };
-    const unsigned int shape1[] = { 1, 1, 2, 2 };
-
-    std::vector<float> input0({ 1,  2, 3, 4 });
-    std::vector<float> input1({ 1, -1, 0, 2 });
-    std::vector<float> output({ 0,  3, 3, 2 });
-
-    return SubtractionTestHelper(workloadFactory,
-                                 shape0, input0, 1.0f, 0,
-                                 shape1, input1, 1.0f, 0,
-                                 shape0, output, 1.0f, 0);
-}
-
-LayerTestResult<float, 4> SubtractionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    const unsigned int shape0[] = { 1, 1, 2, 2 };
-    const unsigned int shape1[] = { 1, 1, 1, 1 };
-
-    std::vector<float> input0({ 1,  2, 3, 4 });
-    std::vector<float> input1({ 10 });
-    std::vector<float> output({ -9,  -8, -7, -6 });
-
-    return SubtractionTestHelper(workloadFactory,
-                                 shape0, input0, 1.0f, 0,
-                                 shape1, input1, 1.0f, 0,
-                                 shape0, output, 1.0f, 0);
-}
-
-LayerTestResult<float, 4> SubtractionBroadcastTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    const unsigned int shape0[] = { 1, 1, 2, 2 };
-    const unsigned int shape1[] = { 1, 1, 1, 2 };
-
-    std::vector<float> input0({ 1,  2, 3, 4 });
-    std::vector<float> input1({ 10, -5 });
-    std::vector<float> output({ -9,  7, -7, 9 });
-
-    return SubtractionTestHelper(workloadFactory,
-                                 shape0, input0, 1.0f, 0,
-                                 shape1, input1, 1.0f, 0,
-                                 shape0, output, 1.0f, 0);
-}
-
-LayerTestResult<uint8_t, 4> ResizeBilinearNopUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    constexpr unsigned int inputWidth = 4;
-    constexpr unsigned int inputHeight = 4;
-    constexpr unsigned int inputChannels = 1;
-    constexpr unsigned int inputBatchSize = 1;
-
-    constexpr unsigned int outputWidth = inputWidth;
-    constexpr unsigned int outputHeight = inputHeight;
-    constexpr unsigned int outputChannels = inputChannels;
-    constexpr unsigned int outputBatchSize = inputBatchSize;
-
-    armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
-        armnn::DataType::QuantisedAsymm8);
-    inputTensorInfo.SetQuantizationScale(1.5f);
-    inputTensorInfo.SetQuantizationOffset(-3);
-
-    armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
-        armnn::DataType::QuantisedAsymm8);
-    outputTensorInfo.SetQuantizationScale(1.5f);
-    outputTensorInfo.SetQuantizationOffset(-3);
-
-    auto input = MakeTensor<uint8_t, 4>(inputTensorInfo, std::vector<uint8_t>({
-        1, 2, 3, 4,
-        2, 3, 4, 5,
-        3, 4, 5, 6,
-        4, 5, 6, 7
-    }));
-
-    LayerTestResult<uint8_t, 4> result(outputTensorInfo);
-    result.outputExpected = input;
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ResizeBilinearQueueDescriptor descriptor;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-    return result;
-}
-
-LayerTestResult<uint8_t, 4> SimpleResizeBilinearUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    constexpr unsigned int inputWidth = 2;
-    constexpr unsigned int inputHeight = 2;
-    constexpr unsigned int inputChannels = 1;
-    constexpr unsigned int inputBatchSize = 1;
-
-    constexpr unsigned int outputWidth = inputWidth / 2;
-    constexpr unsigned int outputHeight = inputHeight / 2;
-    constexpr unsigned int outputChannels = inputChannels;
-    constexpr unsigned int outputBatchSize = inputBatchSize;
-
-    armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
-        armnn::DataType::QuantisedAsymm8);
-    inputTensorInfo.SetQuantizationScale(0.1567f);
-    inputTensorInfo.SetQuantizationOffset(1);
-
-    armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
-        armnn::DataType::QuantisedAsymm8);
-    outputTensorInfo.SetQuantizationScale(0.1567f);
-    outputTensorInfo.SetQuantizationOffset(1);
-
-    auto input = MakeTensor<uint8_t, 4>(inputTensorInfo, std::vector<uint8_t>({
-        1, 255,
-        200, 250
-    }));
-
-    // The 'resize bilinear' operation projects the top-left corner of output texels into the input image,
-    // then figures out the interpolants and weights. Note this is different to projecting the centre of the
-    // output texel - and thus we'll expect the output 1x1 matrix to contain, as its single element, the value
-    // that was at position (0,0) of the input matrix (rather than an average, which we would expect if projecting
-    // the centre).
-    LayerTestResult<uint8_t, 4> result(outputTensorInfo);
-    result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, std::vector<uint8_t>({
-        1
-    }));
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ResizeBilinearQueueDescriptor descriptor;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-    return result;
-}
-
-LayerTestResult<uint8_t, 4> ResizeBilinearSqMinUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    constexpr unsigned int inputWidth = 4;
-    constexpr unsigned int inputHeight = 4;
-    constexpr unsigned int inputChannels = 1;
-    constexpr unsigned int inputBatchSize = 1;
-
-    constexpr unsigned int outputWidth = inputWidth / 2;
-    constexpr unsigned int outputHeight = inputHeight / 2;
-    constexpr unsigned int outputChannels = inputChannels;
-    constexpr unsigned int outputBatchSize = inputBatchSize;
-
-    armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
-        armnn::DataType::QuantisedAsymm8);
-    inputTensorInfo.SetQuantizationScale(3.141592f);
-    inputTensorInfo.SetQuantizationOffset(3);
-
-    armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
-        armnn::DataType::QuantisedAsymm8);
-    outputTensorInfo.SetQuantizationScale(3.141592f);
-    outputTensorInfo.SetQuantizationOffset(3);
-
-    auto input = MakeTensor<uint8_t, 4>(inputTensorInfo, std::vector<uint8_t>({
-        1, 2, 3, 4,
-        2, 3, 4, 5,
-        3, 4, 5, 6,
-        4, 5, 6, 7
-    }));
-
-    LayerTestResult<uint8_t, 4> result(outputTensorInfo);
-    result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, std::vector<uint8_t>({
-        1, 3,
-        3, 5
-    }));
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ResizeBilinearQueueDescriptor descriptor;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-    return result;
-}
-
-LayerTestResult<uint8_t, 4> ResizeBilinearMinUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    constexpr unsigned int inputWidth = 3;
-    constexpr unsigned int inputHeight = 2;
-    constexpr unsigned int inputChannels = 1;
-    constexpr unsigned int inputBatchSize = 1;
-
-    constexpr unsigned int outputWidth = 2;
-    constexpr unsigned int outputHeight = 1;
-    constexpr unsigned int outputChannels = inputChannels;
-    constexpr unsigned int outputBatchSize = inputBatchSize;
-
-    armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
-        armnn::DataType::QuantisedAsymm8);
-    inputTensorInfo.SetQuantizationScale(1.5f);
-    inputTensorInfo.SetQuantizationOffset(-1);
-
-    armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
-        armnn::DataType::QuantisedAsymm8);
-    outputTensorInfo.SetQuantizationScale(1.5f);
-    outputTensorInfo.SetQuantizationOffset(-1);
-
-    auto input = MakeTensor<uint8_t, 4>(inputTensorInfo, std::vector<uint8_t>({
-        1,  2,  3, // 3.0, 4.5, 6.0
-        5,  8, 13  // 9.0, 13.5, 21.0
-    }));
-
-    LayerTestResult<uint8_t, 4> result(outputTensorInfo);
-    result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, std::vector<uint8_t>({
-        1, 3 // 3.0, 5.25
-    }));
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ResizeBilinearQueueDescriptor descriptor;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-    return result;
-}
-
-LayerTestResult<uint8_t, 4> ResizeBilinearMagUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    constexpr unsigned int inputWidth = 2;
-    constexpr unsigned int inputHeight = 3;
-    constexpr unsigned int inputChannels = 1;
-    constexpr unsigned int inputBatchSize = 1;
-
-    constexpr unsigned int outputWidth = 5;
-    constexpr unsigned int outputHeight = 3;
-    constexpr unsigned int outputChannels = inputChannels;
-    constexpr unsigned int outputBatchSize = inputBatchSize;
-
-    armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
-        armnn::DataType::QuantisedAsymm8);
-    inputTensorInfo.SetQuantizationScale(0.010765f);
-    inputTensorInfo.SetQuantizationOffset(7);
-
-    armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
-        armnn::DataType::QuantisedAsymm8);
-    outputTensorInfo.SetQuantizationScale(0.010132f);
-    outputTensorInfo.SetQuantizationOffset(-18);
-
-    auto input = MakeTensor<uint8_t, 4>(inputTensorInfo, std::vector<uint8_t>({
-         24, 228, // 0.183005, 2.379065,
-        105, 128, // 1.05497, 1.302565
-        230,  71  // 2.400595, 0.68896
-    }));
-
-    LayerTestResult<uint8_t, 4> result(outputTensorInfo);
-    result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, std::vector<uint8_t>({
-          0,  87, 173, 217, 217, // 0.18300501, 1.06142902, 1.93985295, 2.37906504, 2.37906504
-         86,  96, 106, 111, 111, // 1.05497003, 1.15400803, 1.25304604, 1.30256498, 1.30256498
-        219, 151,  84,  50,  50  // 2.40059495, 1.71594095, 1.03128707, 0.68896002, 0.68896002
-    }));
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ResizeBilinearQueueDescriptor descriptor;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-    return result;
-}
-
-LayerTestResult<float, 4> BatchNormTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    // BatchSize: 1
-    // Channels: 2
-    // Height: 3
-    // Width: 2
-
-    const armnn::TensorShape inputOutputShape{ 1, 2, 3, 2 };
-    std::vector<float> inputValues
-    {
-        // Batch 0, Channel 0, Height (3) x Width (2)
-         1.f, 4.f,
-         4.f, 2.f,
-         1.f, 6.f,
-
-        // Batch 0, Channel 1, Height (3) x Width (2)
-         1.f, 1.f,
-         4.f, 1.f,
-        -2.f, 4.f
-    };
-    std::vector<float> expectedOutputValues
-    {
-        // Batch 0, Channel 0, Height (3) x Width (2)
-        1.f, 4.f,
-        4.f, 2.f,
-        1.f, 6.f,
-
-        // Batch 0, Channel 1, Height (3) x Width (2)
-        3.f, 3.f,
-        4.f, 3.f,
-        2.f, 4.f
-    };
-
-    return BatchNormTestImpl<float>(workloadFactory, inputOutputShape, inputValues, expectedOutputValues,
-                                    0.f, 0, armnn::DataLayout::NCHW);
-}
-
-LayerTestResult<float, 4> BatchNormNhwcTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    // BatchSize: 1
-    // Height: 3
-    // Width: 2
-    // Channels: 2
-
-    const armnn::TensorShape inputOutputShape{ 1, 3, 2, 2 };
-    std::vector<float> inputValues
-    {
-        // Batch 0, Height 0, Width (2) x Channel (2)
-        1.f,  1.f,
-        4.f,  1.f,
-
-        // Batch 0, Height 1, Width (2) x Channel (2)
-        4.f,  4.f,
-        2.f,  1.f,
-
-        // Batch 0, Height 2, Width (2) x Channel (2)
-        1.f, -2.f,
-        6.f,  4.f
-    };
-    std::vector<float> expectedOutputValues
-    {
-        // Batch 0, Height 0, Width (2) x Channel (2)
-        1.f, 3.f,
-        4.f, 3.f,
-
-        // Batch 0, Height 1, Width (2) x Channel (2)
-        4.f, 4.f,
-        2.f, 3.f,
-
-        // Batch 0, Height 2, Width (2) x Channel (2)
-        1.f, 2.f,
-        6.f, 4.f
-    };
-
-    return BatchNormTestImpl<float>(workloadFactory, inputOutputShape, inputValues, expectedOutputValues,
-                                    0.f, 0, armnn::DataLayout::NHWC);
-}
-
-LayerTestResult<uint8_t, 4> BatchNormUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    // BatchSize: 1
-    // Channels: 2
-    // Height: 3
-    // Width: 2
-
-    const armnn::TensorShape inputOutputShape{ 1, 2, 3, 2 };
-    std::vector<float> inputValues
-    {
-        // Batch 0, Channel 0, Height (3) x Width (2)
-         1.f, 4.f,
-         4.f, 2.f,
-         1.f, 6.f,
-
-        // Batch 0, Channel 1, Height (3) x Width (2)
-         1.f, 1.f,
-         4.f, 1.f,
-        -2.f, 4.f
-    };
-    std::vector<float> expectedOutputValues
-    {
-        // Batch 0, Channel 0, Height (3) x Width (2)
-        1.f, 4.f,
-        4.f, 2.f,
-        1.f, 6.f,
-
-        // Batch 0, Channel 1, Height (3) x Width (2)
-        3.f, 3.f,
-        4.f, 3.f,
-        2.f, 4.f
-    };
-
-    return BatchNormTestImpl<uint8_t>(workloadFactory, inputOutputShape, inputValues, expectedOutputValues,
-                                      1.f/20.f, 50, armnn::DataLayout::NCHW);
-}
-
-LayerTestResult<uint8_t, 4> BatchNormUint8NhwcTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    // BatchSize: 1
-    // Height: 3
-    // Width: 2
-    // Channels: 2
-
-    const armnn::TensorShape inputOutputShape{ 1, 3, 2, 2 };
-    std::vector<float> inputValues
-    {
-        // Batch 0, Height 0, Width (2) x Channel (2)
-        1.f,  1.f,
-        4.f,  1.f,
-
-        // Batch 0, Height 1, Width (2) x Channel (2)
-        4.f,  4.f,
-        2.f,  1.f,
-
-        // Batch 0, Height 2, Width (2) x Channel (2)
-        1.f, -2.f,
-        6.f,  4.f
-    };
-    std::vector<float> expectedOutputValues
-    {
-        // Batch 0, Height 0, Width (2) x Channel (2)
-        1.f, 3.f,
-        4.f, 3.f,
-
-        // Batch 0, Height 1, Width (2) x Channel (2)
-        4.f, 4.f,
-        2.f, 3.f,
-
-        // Batch 0, Height 2, Width (2) x Channel (2)
-        1.f, 2.f,
-        6.f, 4.f
-    };
-
-    return BatchNormTestImpl<uint8_t>(workloadFactory, inputOutputShape, inputValues, expectedOutputValues,
-                                      1.f/20.f, 50, armnn::DataLayout::NHWC);
-}
-
-LayerTestResult<uint8_t, 4> ConstantUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return ConstantTestImpl<uint8_t>(workloadFactory, 2e-6f, 1);
-}
-
-LayerTestResult<uint8_t, 1> Concatenation1dUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return Concatenation1dTestImpl<uint8_t>(workloadFactory, 0.5f, -1);
-}
-
-LayerTestResult<uint8_t, 2> Concatenation2dDim0Uint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return Concatenation2dDim0TestImpl<uint8_t>(workloadFactory, 0.5f, -1);
-}
-
-LayerTestResult<uint8_t, 2> Concatenation2dDim1Uint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return Concatenation2dDim1TestImpl<uint8_t>(workloadFactory, 0.5f, -1);
-}
-
-LayerTestResult<uint8_t, 2> Concatenation2dDim0DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return Concatenation2dDim0DiffInputDimsTestImpl<uint8_t>(workloadFactory, 0.5f, -1);
-}
-
-LayerTestResult<uint8_t, 2> Concatenation2dDim1DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return Concatenation2dDim1DiffInputDimsTestImpl<uint8_t>(workloadFactory, 0.5f, -1);
-}
-
-LayerTestResult<uint8_t, 3> Concatenation3dDim0Uint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return Concatenation3dDim0TestImpl<uint8_t>(workloadFactory, 0.5f, -1);
-}
-
-LayerTestResult<uint8_t, 3> Concatenation3dDim1Uint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return Concatenation3dDim1TestImpl<uint8_t>(workloadFactory, 0.5f, -1);
-}
-
-LayerTestResult<uint8_t, 3> Concatenation3dDim2Uint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return Concatenation3dDim2TestImpl<uint8_t>(workloadFactory, 0.5f, -1);
-}
-
-LayerTestResult<uint8_t, 3> Concatenation3dDim0DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return Concatenation3dDim0TestImpl<uint8_t>(workloadFactory, 0.5f, -1);
-}
-
-LayerTestResult<uint8_t, 3> Concatenation3dDim1DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return Concatenation3dDim1DiffInputDimsTestImpl<uint8_t>(workloadFactory, 0.5f, -1);
-}
-
-LayerTestResult<uint8_t, 3> Concatenation3dDim2DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return Concatenation3dDim2DiffInputDimsTestImpl<uint8_t>(workloadFactory, 0.5f, -1);
-}
-
-LayerTestResult<float, 4> SimpleMaxPooling2dSize2x2Stride2x2Test(armnn::IWorkloadFactory& workloadFactory,
-                                                                 bool forceNoPadding)
-{
-    return SimpleMaxPooling2dSize2x2Stride2x2TestCommon<float>(workloadFactory, forceNoPadding);
-}
-
-LayerTestResult<uint8_t, 4> SimpleMaxPooling2dSize2x2Stride2x2Uint8Test(armnn::IWorkloadFactory& workloadFactory,
-                                                                        bool forceNoPadding)
-{
-    return SimpleMaxPooling2dSize2x2Stride2x2TestCommon<uint8_t>(workloadFactory, forceNoPadding, 3.0f, -5);
-}
-
-LayerTestResult<float, 4> SimpleMaxPooling2dSize3x3Stride2x4Test(armnn::IWorkloadFactory& workloadFactory,
-                                                                 bool forceNoPadding)
-{
-    return SimpleMaxPooling2dSize3x3Stride2x4TestCommon<float>(workloadFactory, forceNoPadding);
-}
-
-LayerTestResult<uint8_t, 4> SimpleMaxPooling2dSize3x3Stride2x4Uint8Test(armnn::IWorkloadFactory& workloadFactory,
-                                                                        bool forceNoPadding)
-{
-    return SimpleMaxPooling2dSize3x3Stride2x4TestCommon<uint8_t>(workloadFactory, forceNoPadding, 0.1f, 128);
-}
-
-LayerTestResult<float, 4> SimpleMaxPooling2dTest(armnn::IWorkloadFactory& workloadFactory,
-                                                 const armnn::DataLayoutIndexed& dataLayout)
-{
-    return SimpleMaxPooling2dTestCommon<float>(workloadFactory, dataLayout);
-}
-
-LayerTestResult<uint8_t, 4> SimpleMaxPooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory,
-                                                        const armnn::DataLayoutIndexed& dataLayout)
-{
-    return SimpleMaxPooling2dTestCommon<uint8_t>(workloadFactory, dataLayout);
-}
-
-LayerTestResult<float, 4> SimpleAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory,
-                                                     const armnn::DataLayoutIndexed& dataLayout)
-{
-    return SimpleAveragePooling2dTestCommon<float>(workloadFactory, dataLayout);
-}
-
-LayerTestResult<uint8_t, 4> SimpleAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory,
-                                                            const armnn::DataLayoutIndexed& dataLayout)
-{
-    return SimpleAveragePooling2dTestCommon<uint8_t>(workloadFactory, dataLayout, 0.5, -1);
-}
-
-LayerTestResult<float, 4> IgnorePaddingAveragePooling2dSize3x2Stride2x2Test(armnn::IWorkloadFactory& workloadFactory,
-                                                                            bool forceNoPadding)
-{
-    return IgnorePaddingAveragePooling2dSize3x2Stride2x2TestCommon<float>(workloadFactory, forceNoPadding);
-}
-
-LayerTestResult<float, 4> LargeTensorsAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    return LargeTensorsAveragePooling2dTestCommon<float>(workloadFactory);
-}
-
-LayerTestResult<uint8_t, 4> LargeTensorsAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return LargeTensorsAveragePooling2dTestCommon<uint8_t>(workloadFactory, 0.5, -1);
-}
-
-LayerTestResult<float, 4> SimpleL2Pooling2dTest(armnn::IWorkloadFactory& workloadFactory,
-                                                const armnn::DataLayoutIndexed& dataLayout)
-{
-    return SimpleL2Pooling2dTestCommon<float>(workloadFactory, dataLayout);
-}
-
-LayerTestResult<uint8_t, 4> SimpleL2Pooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory,
-                                                       const armnn::DataLayoutIndexed& dataLayout)
-{
-    return SimpleL2Pooling2dTestCommon<uint8_t>(workloadFactory, dataLayout);
-}
-
-LayerTestResult<float, 4> L2Pooling2dSize3Stride1Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return L2Pooling2dSize3Stride1TestCommon<float>(workloadFactory);
-}
-
-LayerTestResult<uint8_t, 4> L2Pooling2dSize3Stride1Uint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return L2Pooling2dSize3Stride1TestCommon<uint8_t>(workloadFactory);
-}
-
-LayerTestResult<float, 4> L2Pooling2dSize3Stride3Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return L2Pooling2dSize3Stride3TestCommon<float>(workloadFactory);
-}
-
-LayerTestResult<uint8_t, 4> L2Pooling2dSize3Stride3Uint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return L2Pooling2dSize3Stride3TestCommon<uint8_t>(workloadFactory);
-}
-
-LayerTestResult<float, 4> L2Pooling2dSize3Stride4Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return L2Pooling2dSize3Stride4TestCommon<float>(workloadFactory);
-}
-
-LayerTestResult<uint8_t, 4> L2Pooling2dSize3Stride4Uint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return L2Pooling2dSize3Stride4TestCommon<uint8_t>(workloadFactory);
-}
-
-LayerTestResult<float, 4> L2Pooling2dSize7Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return L2Pooling2dSize7TestCommon<float>(workloadFactory);
-}
-
-LayerTestResult<uint8_t, 4> L2Pooling2dSize7Uint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return L2Pooling2dSize7TestCommon<uint8_t>(workloadFactory);
-}
-
-LayerTestResult<float, 4> L2Pooling2dSize9Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return L2Pooling2dSize9TestCommon<float>(workloadFactory);
-}
-
-LayerTestResult<uint8_t, 4> L2Pooling2dSize9Uint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return L2Pooling2dSize9TestCommon<uint8_t>(workloadFactory);
-}
-
-LayerTestResult<float, 4> AsymmetricNonSquarePooling2dTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    return AsymmetricNonSquarePooling2dTestCommon<float>(workloadFactory);
-}
-
-LayerTestResult<uint8_t, 4> AsymmetricNonSquarePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return AsymmetricNonSquarePooling2dTestCommon<uint8_t>(workloadFactory);
-}
-
-LayerTestResult<float, 4> ComparePooling2dTest(armnn::IWorkloadFactory& workloadFactory,
-                                               armnn::IWorkloadFactory& refWorkloadFactory,
-                                               armnn::PoolingAlgorithm  poolingType)
-{
-    return ComparePooling2dTestCommon<float>(workloadFactory, refWorkloadFactory, poolingType);
-}
-
-LayerTestResult<uint8_t, 4> ComparePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory,
-                                                      armnn::IWorkloadFactory& refWorkloadFactory,
-                                                      armnn::PoolingAlgorithm  poolingType)
-{
-    return ComparePooling2dTestCommon<uint8_t>(workloadFactory, refWorkloadFactory, poolingType, 0.1f, 128);
-}
-
-LayerTestResult<float, 2> FullyConnectedLargeTest(armnn::IWorkloadFactory& workloadFactory,
-                                                  bool transposeWeights)
-{
-    return FullyConnectedLargeTestCommon<float>(workloadFactory, transposeWeights);
-}
-
-LayerTestResult<float, 4> IgnorePaddingSimpleMaxPooling2dTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    return IgnorePaddingSimpleMaxPooling2dTestCommon<float>(workloadFactory);
-}
-
-LayerTestResult<uint8_t, 4> IgnorePaddingSimpleMaxPooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return IgnorePaddingSimpleMaxPooling2dTestCommon<uint8_t>(workloadFactory, 1.0f, -5);
-}
-
-LayerTestResult<float, 4> IgnorePaddingMaxPooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return IgnorePaddingMaxPooling2dSize3TestCommon<float>(workloadFactory);
-}
-
-LayerTestResult<uint8_t, 4> IgnorePaddingMaxPooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return IgnorePaddingMaxPooling2dSize3TestCommon<uint8_t>(workloadFactory, 1.0f, -5);
-}
-
-LayerTestResult<float, 4> IgnorePaddingSimpleAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    return IgnorePaddingSimpleAveragePooling2dTestCommon<float>(workloadFactory);
-}
-
-LayerTestResult<uint8_t, 4> IgnorePaddingSimpleAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return IgnorePaddingSimpleAveragePooling2dTestCommon<uint8_t>(workloadFactory);
-}
-
-LayerTestResult<float, 4> IgnorePaddingSimpleAveragePooling2dNoPaddingTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    return IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon<float>(workloadFactory);
-}
-
-LayerTestResult<uint8_t, 4> IgnorePaddingSimpleAveragePooling2dNoPaddingUint8Test(
-    armnn::IWorkloadFactory& workloadFactory)
-{
-    return IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon<uint8_t>(workloadFactory);
-}
-
-LayerTestResult<float, 4> IgnorePaddingAveragePooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return IgnorePaddingAveragePooling2dSize3TestCommon<float>(workloadFactory);
-}
-
-LayerTestResult<uint8_t, 4> IgnorePaddingAveragePooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return IgnorePaddingAveragePooling2dSize3TestCommon<uint8_t>(workloadFactory);
-}
-
-LayerTestResult<float, 4> IgnorePaddingSimpleL2Pooling2dTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    return IgnorePaddingSimpleL2Pooling2dTestCommon<float>(workloadFactory);
-}
-
-LayerTestResult<uint8_t, 4> IgnorePaddingSimpleL2Pooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return IgnorePaddingSimpleL2Pooling2dTestCommon<uint8_t>(workloadFactory);
-}
-
-LayerTestResult<float, 4> IgnorePaddingL2Pooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return IgnorePaddingL2Pooling2dSize3TestCommon<float>(workloadFactory);
-}
-
-LayerTestResult<uint8_t, 4> IgnorePaddingL2Pooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return IgnorePaddingL2Pooling2dSize3TestCommon<uint8_t>(workloadFactory);
-}
-
-LayerTestResult<float, 4> SimplePermuteFloat32Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return SimplePermuteFloat32TestCommon(workloadFactory);
-};
-
-LayerTestResult<uint8_t, 4> SimplePermuteUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return SimplePermuteUint8TestCommon(workloadFactory);
-};
-
-LayerTestResult<float, 4> PermuteFloat32ValueSet1Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return PermuteFloat32ValueSet1TestCommon(workloadFactory);
-};
-
-LayerTestResult<float, 4> PermuteFloat32ValueSet2Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return PermuteFloat32ValueSet2TestCommon(workloadFactory);
-};
-
-LayerTestResult<float, 4> PermuteFloat32ValueSet3Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return PermuteFloat32ValueSet3TestCommon(workloadFactory);
-};
-
-namespace
-{
-
-template <typename T, std::size_t InputDim, std::size_t OutputDim>
-LayerTestResult<T, OutputDim> MeanTestHelper(armnn::IWorkloadFactory& workloadFactory,
-                                             const unsigned int* inputShape,
-                                             const std::vector<T>& inputData,
-                                             const std::vector<unsigned int>& axis,
-                                             bool keepDims,
-                                             const unsigned int* outputShape,
-                                             const std::vector<T>& outputData,
-                                             float scale = 1.0f,
-                                             int32_t offset = 0)
-{
-    auto dataType = (std::is_same<T, uint8_t>::value ? armnn::DataType::QuantisedAsymm8 : armnn::DataType::Float32);
-
-    armnn::TensorInfo inputTensorInfo(InputDim, inputShape, dataType);
-    armnn::TensorInfo outputTensorInfo(OutputDim, outputShape, dataType);
-
-    inputTensorInfo.SetQuantizationScale(scale);
-    inputTensorInfo.SetQuantizationOffset(offset);
-
-    outputTensorInfo.SetQuantizationScale(scale);
-    outputTensorInfo.SetQuantizationOffset(offset);
-
-    auto input = MakeTensor<T, InputDim>(inputTensorInfo, inputData);
-
-    LayerTestResult<T, OutputDim> result(outputTensorInfo);
-    result.outputExpected = MakeTensor<T, OutputDim>(outputTensorInfo, outputData);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::MeanQueueDescriptor data;
-    data.m_Parameters.m_Axis = axis;
-    data.m_Parameters.m_KeepDims = keepDims;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data,  info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMean(data, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), input.origin());
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(result.output.origin(), outputHandle.get());
-
-    return result;
-}
-
-} // anonymous namespace
-
-LayerTestResult<uint8_t, 1> MeanUint8SimpleTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    const unsigned int inputShape[] = { 3, 2 };
-    const unsigned int outputShape[] = { 1 };
-
-    std::vector<uint8_t> input({ 1, 1, 2, 2, 3, 3 });
-    std::vector<uint8_t> output({ 2 });
-
-    return MeanTestHelper<uint8_t, 2, 1>(workloadFactory, inputShape, input, {}, false, outputShape, output);
-}
-
-LayerTestResult<uint8_t, 3> MeanUint8SimpleAxisTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    const unsigned int inputShape[] = { 1, 1, 3, 2 };
-    const unsigned int outputShape[] = { 1, 1, 2 };
-
-    std::vector<uint8_t> input({ 1, 1, 2, 2, 3, 3 });
-    std::vector<uint8_t> output({ 2, 2 });
-
-    return MeanTestHelper<uint8_t, 4, 3>(workloadFactory, inputShape, input, { 2 }, false, outputShape, output);
-}
-
-LayerTestResult<uint8_t, 4> MeanUint8KeepDimsTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    const unsigned int inputShape[] = { 1, 1, 3, 2 };
-    const unsigned int outputShape[] = { 1, 1, 1, 2 };
-
-    std::vector<uint8_t> input({ 1, 1, 2, 2, 3, 3 });
-    std::vector<uint8_t> output({ 2, 2 });
-
-    return MeanTestHelper<uint8_t, 4, 4>(workloadFactory, inputShape, input, { 2 }, true, outputShape, output);
-}
-
-LayerTestResult<uint8_t, 4> MeanUint8MultipleDimsTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    const unsigned int inputShape[] = { 2, 3, 1, 2 };
-    const unsigned int outputShape[] = { 1, 3, 1, 1 };
-
-    std::vector<uint8_t> input({ 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6 });
-    std::vector<uint8_t> output({ 1, 3, 5 });
-
-    return MeanTestHelper<uint8_t, 4, 4>(workloadFactory, inputShape, input, { 0, 3 }, true, outputShape, output);
-}
-
-LayerTestResult<uint8_t, 1> MeanVtsUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    const unsigned int inputShape[] = { 4, 3, 2 };
-    const unsigned int outputShape[] = { 2 };
-
-    std::vector<uint8_t> input({ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
-                                 24 });
-    std::vector<uint8_t> output({ 12, 13 });
-
-    return MeanTestHelper<uint8_t, 3, 1>(workloadFactory, inputShape, input, { 0, 1 }, false, outputShape,
-                                         output, 0.8f, 5);
-}
-
-LayerTestResult<float, 1> MeanFloatSimpleTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    const unsigned int inputShape[] = { 3, 2 };
-    const unsigned int outputShape[] = { 1 };
-
-    std::vector<float> input({ 1.0f, 1.0f, 2.0f, 2.0f, 3.0f, 3.0f });
-    std::vector<float> output({ 2.0f });
-
-    return MeanTestHelper<float, 2, 1>(workloadFactory, inputShape, input, {}, false, outputShape, output);
-}
-
-LayerTestResult<float, 3> MeanFloatSimpleAxisTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    const unsigned int inputShape[] = { 2, 3, 1, 2 };
-    const unsigned int outputShape[] = { 3, 1, 2 };
-
-    std::vector<float> input({ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f });
-    std::vector<float> output({ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f });
-
-    return MeanTestHelper<float, 4, 3>(workloadFactory, inputShape, input, { 0 }, false, outputShape, output);
-}
-
-LayerTestResult<float, 4> MeanFloatKeepDimsTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    const unsigned int inputShape[] = { 1, 1, 3, 2 };
-    const unsigned int outputShape[] = { 1, 1, 1, 2 };
-
-    std::vector<float> input({ 1.0f, 1.0f, 2.0f, 2.0f, 3.0f, 3.0f });
-    std::vector<float> output({ 2.0f, 2.0f });
-
-    return MeanTestHelper<float, 4, 4>(workloadFactory, inputShape, input, { 2 }, true, outputShape, output);
-}
-
-LayerTestResult<float, 4> MeanFloatMultipleDimsTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    const unsigned int inputShape[] = { 2, 3, 1, 2 };
-    const unsigned int outputShape[] = { 1, 3, 1, 1 };
-
-    std::vector<float> input({ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f });
-    std::vector<float> output({ 1.5f, 3.5f, 5.5f });
-
-    return MeanTestHelper<float, 4, 4>(workloadFactory, inputShape, input, { 0, 3 }, true, outputShape, output);
-}
-
-LayerTestResult<float, 1> MeanVtsFloat1Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    const unsigned int inputShape[] = { 4, 3, 2 };
-    const unsigned int outputShape[] = { 2 };
-
-    std::vector<float> input({ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f,
-                               15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f });
-    std::vector<float> output({ 12.0f, 13.0f });
-
-    return MeanTestHelper<float, 3, 1>(workloadFactory, inputShape, input, { 0, 1 }, false, outputShape, output);
-}
-
-LayerTestResult<float, 3> MeanVtsFloat2Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    const unsigned int inputShape[] = { 4, 3, 2 };
-    const unsigned int outputShape[] = { 1, 3, 1 };
-
-    std::vector<float> input({ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f,
-                               15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f });
-    std::vector<float> output({ 10.5f, 12.5f, 14.5f });
-
-    return MeanTestHelper<float, 3, 3>(workloadFactory, inputShape, input, { 0, 2 }, true, outputShape, output);
-}
-
-LayerTestResult<float, 3> MeanVtsFloat3Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    const unsigned int inputShape[] = { 1, 2, 2, 1 };
-    const unsigned int outputShape[] = { 1, 2, 1 };
-
-    std::vector<float> input({ 1.0f, 2.0f, 3.0f, 4.0f });
-    std::vector<float> output({ 1.5f, 3.5f });
-
-    return MeanTestHelper<float, 4, 3>(workloadFactory, inputShape, input, { 2 }, false, outputShape, output);
-}
-
-LayerTestResult<float, 4> AdditionAfterMaxPoolTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    // Create Initial Tensor
-    // 1, 2, 3
-    // 4, 5, 6
-    // 7, 8, 9
-
-    armnn::TensorInfo poolingInputTensorInfo({ 1, 1, 3, 3}, armnn::GetDataType<float>());
-    armnn::TensorInfo poolingOutputTensorInfo({ 1, 1, 2, 2}, armnn::GetDataType<float>());
-
-    boost::multi_array<float, 4> poolingInput = MakeTensor<float,4>(poolingInputTensorInfo,
-                                                            {1, 2, 3,
-                                                             4, 5, 6,
-                                                             7, 8, 9
-                                                            });
-
-    std::unique_ptr<armnn::ITensorHandle> poolingInputHandle =
-            workloadFactory.CreateTensorHandle(poolingInputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> poolingOutputHandle =
-            workloadFactory.CreateTensorHandle(poolingOutputTensorInfo);
-
-    // Apply MaxPool poolSize = 1x1, stride=2x2
-    // Result =
-    // 1, 3
-    // 7, 9
-    armnn::Pooling2dDescriptor descriptor;
-    descriptor.m_PoolHeight = 1;
-    descriptor.m_PoolWidth = 1;
-    descriptor.m_StrideX = 2;
-    descriptor.m_StrideY = 2;
-    descriptor.m_PoolType = armnn::PoolingAlgorithm::Max;
-
-    armnn::Pooling2dQueueDescriptor queueDescriptor;
-    queueDescriptor.m_Parameters = descriptor;
-    armnn::WorkloadInfo workloadInfo;
-    AddInputToWorkload(queueDescriptor, workloadInfo, poolingInputTensorInfo, poolingInputHandle.get());
-    AddOutputToWorkload(queueDescriptor, workloadInfo, poolingOutputTensorInfo, poolingOutputHandle.get());
-
-    // Create the MaxPool
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePooling2d(queueDescriptor, workloadInfo);
-
-    //LayerTestResult<float, 4> result(poolingOutputTensorInfo);
-    auto shape( GetTensorShapeAsArray<4>(poolingOutputTensorInfo));
-    boost::multi_array<float, 4> resultMaxPool;
-    resultMaxPool.resize(shape);
-
-
-    // Create addition with another tensor the same size
-    // This would be the result to apply a Conv2d with kernel ones(2) and stride 1x1
-    // with the initial tensor.
-    // 12, 16
-    // 24, 28
-
-    armnn::TensorInfo addInputTensorInfo({ 1,1,2,2}, armnn::GetDataType<float>());
-    armnn::TensorInfo addOutputTensorInfo({ 1,1,2,2}, armnn::GetDataType<float>());
-
-    boost::multi_array<float, 4> addInput = MakeTensor<float,4>(addInputTensorInfo,
-                                                                    {12, 16,
-                                                                     24, 28,
-                                                                    });
-
-    // Expected output tensor after MaxPool and Addition.
-    LayerTestResult<float,4> addRet(addOutputTensorInfo);
-    addRet.outputExpected = MakeTensor<float, 4>(addOutputTensorInfo, std::vector<float>(
-            {
-                    13, 19,
-                    31, 37
-            }));
-
-    std::unique_ptr<armnn::ITensorHandle> addInputHandle = workloadFactory.CreateTensorHandle(addInputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> addOutputHandle = workloadFactory.CreateTensorHandle(addOutputTensorInfo);
-
-    armnn::AdditionQueueDescriptor data;
-    armnn::WorkloadInfo info;
-
-    // Add the output of the MaxPool and the new tensor
-    AddInputToWorkload(data, info, poolingOutputTensorInfo, poolingOutputHandle.get());
-    AddInputToWorkload(data, info, addInputTensorInfo, addInputHandle.get());
-    AddOutputToWorkload(data, info, addOutputTensorInfo, addOutputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> addWorkload = workloadFactory.CreateAddition(data, info);
-
-    poolingInputHandle->Allocate();
-    poolingOutputHandle->Allocate();
-    addInputHandle->Allocate();
-    addOutputHandle->Allocate();
-
-    CopyDataToITensorHandle(poolingInputHandle.get(), &poolingInput[0][0][0][0]);
-    CopyDataFromITensorHandle(&resultMaxPool[0][0][0][0], poolingOutputHandle.get());
-
-    CopyDataToITensorHandle(poolingOutputHandle.get(), &resultMaxPool[0][0][0][0]);
-    CopyDataToITensorHandle(addInputHandle.get(), &addInput[0][0][0][0]);
-
-    workload->Execute();
-    addWorkload->Execute();
-
-    CopyDataFromITensorHandle(&addRet.output[0][0][0][0], addOutputHandle.get());
-
-    workloadFactory.Finalize();
-
-    return addRet;
-}
diff --git a/src/backends/test/LayerTests.hpp b/src/backends/test/LayerTests.hpp
deleted file mode 100644
index 26dec60e0b..0000000000
--- a/src/backends/test/LayerTests.hpp
+++ /dev/null
@@ -1,414 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#pragma once
-
-#include "armnn/ArmNN.hpp"
-#include "armnn/Tensor.hpp"
-#include "armnnUtils/Half.hpp"
-
-#include <boost/multi_array.hpp>
-#include <boost/assert.hpp>
-#include <array>
-
-// Layer callables.
-
-namespace armnn
-{
-class IWorkloadFactory;
-}
-
-template <std::size_t n>
-boost::array<unsigned int, n> GetTensorShapeAsArray(const armnn::TensorInfo& tensorInfo)
-{
-    BOOST_ASSERT_MSG(n == tensorInfo.GetNumDimensions(),
-        "Attempting to construct a shape array of mismatching size");
-
-    boost::array<unsigned int, n> shape;
-    for (unsigned int i = 0; i < n; i++)
-    {
-        shape[i] = tensorInfo.GetShape()[i];
-    }
-    return shape;
-}
-
-template <typename T, std::size_t n>
-struct LayerTestResult
-{
-    LayerTestResult(const armnn::TensorInfo& outputInfo)
-    {
-        auto shape( GetTensorShapeAsArray<n>(outputInfo) );
-        output.resize(shape);
-        outputExpected.resize(shape);
-        supported = true;
-    }
-
-    boost::multi_array<T, n> output;
-    boost::multi_array<T, n> outputExpected;
-    bool supported;
-};
-
-LayerTestResult<float, 4> SimpleConvolution2d3x5Test(armnn::IWorkloadFactory& workloadFactory,
-                                                     bool biasEnabled,
-                                                     const armnn::DataLayoutIndexed& layout);
-
-LayerTestResult<float, 4> SimpleConvolution2d3x3Test(armnn::IWorkloadFactory& workloadFactory,
-                                                     bool biasEnabled,
-                                                     const armnn::DataLayoutIndexed& layout);
-
-LayerTestResult<float, 4> SimpleConvolution2d3x3NhwcTest(armnn::IWorkloadFactory& workloadFactory,
-                                                         bool biasEnabled);
-
-LayerTestResult<float, 4>
-Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest(armnn::IWorkloadFactory& workloadFactory,
-                                                           const armnn::DataLayoutIndexed& layout);
-LayerTestResult<float, 4> Convolution2dAsymmetricPaddingTest(armnn::IWorkloadFactory& workloadFactory,
-                                                             const armnn::DataLayoutIndexed& layout);
-
-
-LayerTestResult<float,   4> Convolution1dTest(armnn::IWorkloadFactory& workloadFactory,
-                                              bool biasEnabled);
-LayerTestResult<uint8_t, 4> Convolution1dUint8Test(armnn::IWorkloadFactory& workloadFactory,
-                                                   bool biasEnabled);
-
-LayerTestResult<float, 4> DepthwiseConvolution2dTest(armnn::IWorkloadFactory& workloadFactory,
-                                                     bool biasEnabled,
-                                                     const armnn::DataLayoutIndexed& layout);
-
-LayerTestResult<float, 4> DepthwiseConvolution2dDepthNhwcTest(armnn::IWorkloadFactory& workloadFactory,
-                                                              bool biasEnabled);
-
-LayerTestResult<float, 4> DepthwiseConvolution2dDepthMul1Test(armnn::IWorkloadFactory& workloadFactory,
-                                                              bool biasEnabled,
-                                                              const armnn::DataLayoutIndexed& layout);
-
-LayerTestResult<float, 4> DepthwiseConvolution2dAsymmetricTest(armnn::IWorkloadFactory& workloadFactory,
-                                                               bool biasEnabled,
-                                                               const armnn::DataLayoutIndexed& layout);
-
-LayerTestResult<float,   4> SimpleMaxPooling2dSize2x2Stride2x2Test(armnn::IWorkloadFactory& workloadFactory,
-                                                                   bool forceNoPadding);
-LayerTestResult<uint8_t, 4> SimpleMaxPooling2dSize2x2Stride2x2Uint8Test(armnn::IWorkloadFactory& workloadFactory,
-                                                                        bool forceNoPadding);
-LayerTestResult<float,   4> SimpleMaxPooling2dSize3x3Stride2x4Test(armnn::IWorkloadFactory& workloadFactory,
-                                                                   bool forceNoPadding);
-LayerTestResult<uint8_t, 4> SimpleMaxPooling2dSize3x3Stride2x4Uint8Test(armnn::IWorkloadFactory& workloadFactory,
-                                                                        bool forceNoPadding );
-LayerTestResult<float,   4> IgnorePaddingSimpleMaxPooling2dTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> IgnorePaddingSimpleMaxPooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float,   4> IgnorePaddingMaxPooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> IgnorePaddingMaxPooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<float,   4> SimpleMaxPooling2dTest(armnn::IWorkloadFactory& workloadFactory,
-                                                   const armnn::DataLayoutIndexed& dataLayout);
-LayerTestResult<uint8_t, 4> SimpleMaxPooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory,
-                                                        const armnn::DataLayoutIndexed& dataLayout);
-
-LayerTestResult<float,   4> SimpleAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory,
-                                                       const armnn::DataLayoutIndexed& dataLayout);
-LayerTestResult<uint8_t, 4> SimpleAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory,
-                                                            const armnn::DataLayoutIndexed& dataLayout);
-
-LayerTestResult<float,   4> IgnorePaddingAveragePooling2dSize3x2Stride2x2Test(armnn::IWorkloadFactory& workloadFactory,
-                                                                              bool forceNoPadding);
-LayerTestResult<float,   4> IgnorePaddingSimpleAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> IgnorePaddingSimpleAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 4>   IgnorePaddingSimpleAveragePooling2dNoPaddingTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> IgnorePaddingSimpleAveragePooling2dNoPaddingUint8Test(
-    armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float,   4> IgnorePaddingAveragePooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> IgnorePaddingAveragePooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<float,   4> SimpleL2Pooling2dTest(armnn::IWorkloadFactory& workloadFactory,
-                                                  const armnn::DataLayoutIndexed& dataLayout);
-LayerTestResult<uint8_t, 4> SimpleL2Pooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory,
-                                                       const armnn::DataLayoutIndexed& dataLayout);
-
-LayerTestResult<float,   4> L2Pooling2dSize3Stride1Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> L2Pooling2dSize3Stride1Uint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float,   4> L2Pooling2dSize3Stride3Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> L2Pooling2dSize3Stride3Uint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float,   4> L2Pooling2dSize3Stride4Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> L2Pooling2dSize3Stride4Uint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float,   4> L2Pooling2dSize7Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> L2Pooling2dSize7Uint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float,   4> L2Pooling2dSize9Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> L2Pooling2dSize9Uint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float,   4> LargeTensorsAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> LargeTensorsAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<float,   4> IgnorePaddingSimpleL2Pooling2dTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> IgnorePaddingSimpleL2Pooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float,   4> IgnorePaddingL2Pooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> IgnorePaddingL2Pooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<float,   4> AsymmetricNonSquarePooling2dTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> AsymmetricNonSquarePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<float, 4> ComparePooling2dTest(armnn::IWorkloadFactory& workloadFactory,
-                                               armnn::IWorkloadFactory& refWorkloadFactory,
-                                               armnn::PoolingAlgorithm  poolingType);
-LayerTestResult<uint8_t, 4> ComparePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory,
-                                                      armnn::IWorkloadFactory& refWorkloadFactory,
-                                                      armnn::PoolingAlgorithm  poolingType);
-
-LayerTestResult<float, 4> ConstantLinearActivationTest(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<float, 4> SimpleNormalizationAcrossTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 4> SimpleNormalizationWithinTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float,4> SimpleNormalizationAcrossNhwcTest(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<float, 2> SimpleSoftmaxTest(armnn::IWorkloadFactory& workloadFactory, float beta);
-LayerTestResult<uint8_t, 2> SimpleSoftmaxUint8Test(armnn::IWorkloadFactory& workloadFactory, float beta);
-
-LayerTestResult<float, 4> SimpleSigmoidTest(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<float, 4> SimpleReshapeFloat32Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> SimpleReshapeUint8Test(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<float, 4> SimpleFloorTest(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<float, 1> Concatenation1dTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 2> Concatenation2dDim0Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 2> Concatenation2dDim1Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 2> Concatenation2dDim0DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 2> Concatenation2dDim1DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 3> Concatenation3dDim0Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 3> Concatenation3dDim1Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 3> Concatenation3dDim2Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 3> Concatenation3dDim0DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 3> Concatenation3dDim1DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 3> Concatenation3dDim2DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<uint8_t, 4> SimpleSigmoidUint8Test(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<float, 4> CompareConvolution2dTest(armnn::IWorkloadFactory& workloadFactory,
-    armnn::IWorkloadFactory& refWorkloadFactory);
-
-template<typename T>
-LayerTestResult<T, 4> CompareDepthwiseConvolution2dTest(armnn::IWorkloadFactory& workloadFactory,
-                                                        armnn::IWorkloadFactory& refWorkloadFactory,
-                                                        const armnn::DataLayoutIndexed& layout);
-
-LayerTestResult<float, 4> CompareNormalizationTest(armnn::IWorkloadFactory& workloadFactory,
-                                                   armnn::IWorkloadFactory& refWorkloadFactory,
-                                                   armnn::NormalizationAlgorithmChannel normChannel,
-                                                   armnn::NormalizationAlgorithmMethod normMethod);
-
-LayerTestResult<float, 2> CompareSoftmaxTest(armnn::IWorkloadFactory& workloadFactory,
-    armnn::IWorkloadFactory& refWorkloadFactory, float beta);
-
-LayerTestResult<float, 2> FullyConnectedFloat32Test(armnn::IWorkloadFactory& workloadFactory,
-                                             bool                     biasEnabled,
-                                             bool                     transposeWeights);
-
-std::vector<LayerTestResult<float, 3>> SplitterTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 3> CopyViaSplitterTest(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<float, 3> MergerTest(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<float, 4> AdditionTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 4> AdditionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 4> AdditionBroadcastTest(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<float, 4> CompareAdditionTest(armnn::IWorkloadFactory& workloadFactory,
-                                              armnn::IWorkloadFactory& refWorkloadFactory);
-
-LayerTestResult<float, 4> SubtractionTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 4> SubtractionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 4> SubtractionBroadcastTest(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<float, 4> CompareActivationTest(armnn::IWorkloadFactory&  workloadFactory,
-                                                armnn::IWorkloadFactory&  refWorkloadFactory,
-                                                armnn::ActivationFunction f,
-                                                unsigned int batchSize);
-
-LayerTestResult<float, 4> DivisionTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 4> DivisionByZeroTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 4> DivisionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 4> DivisionBroadcast1DVectorTest(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<float, 4> MultiplicationTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 4> MultiplicationBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 4> MultiplicationBroadcast1DVectorTest(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<float, 4> CompareMultiplicationTest(armnn::IWorkloadFactory& workloadFactory,
-                                             armnn::IWorkloadFactory& refWorkloadFactory);
-
-LayerTestResult<float, 4> BatchNormTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 4> BatchNormNhwcTest(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<float, 4> CompareBatchNormTest(armnn::IWorkloadFactory& workloadFactory,
-                                        armnn::IWorkloadFactory& refWorkloadFactory);
-
-LayerTestResult<float, 4> BoundedReLuUpperAndLowerBoundTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> BoundedReLuUint8UpperAndLowerBoundTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 4> BoundedReLuUpperBoundOnlyTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> BoundedReLuUint8UpperBoundOnlyTest(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<float, 4> CompareBoundedReLuTest(armnn::IWorkloadFactory& workloadFactory,
-                                                 armnn::IWorkloadFactory& refWorkloadFactory,
-                                                 float upperBound,
-                                                 float lowerBound);
-
-// Tests that the output should be identical to the input when the output dimensions match the input ones.
-LayerTestResult<float, 4> ResizeBilinearNopTest(armnn::IWorkloadFactory& workloadFactory,
-                                                const armnn::DataLayoutIndexed& dataLayout);
-
-// Tests the behaviour of the resize bilinear operation when rescaling a 2x2 image into a 1x1 image.
-LayerTestResult<float, 4> SimpleResizeBilinearTest(armnn::IWorkloadFactory& workloadFactory,
-                                                   const armnn::DataLayoutIndexed& dataLayout);
-
-// Tests the resize bilinear for minification of a square input matrix (also: input dimensions are a
-// multiple of output dimensions).
-LayerTestResult<float, 4> ResizeBilinearSqMinTest(armnn::IWorkloadFactory& workloadFactory,
-                                                  const armnn::DataLayoutIndexed& dataLayout);
-
-// Tests the resize bilinear for minification (output dimensions smaller than input dimensions).
-LayerTestResult<float, 4> ResizeBilinearMinTest(armnn::IWorkloadFactory& workloadFactory,
-                                                const armnn::DataLayoutIndexed& dataLayout);
-
-// Tests the resize bilinear for magnification (output dimensions bigger than input dimensions).
-LayerTestResult<float, 4> ResizeBilinearMagTest(armnn::IWorkloadFactory& workloadFactory,
-                                                const armnn::DataLayoutIndexed& dataLayout);
-
-LayerTestResult<float, 4> BatchNormTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 4> BatchNormNhwcTest(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<float, 2> FakeQuantizationTest(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<float, 4> L2Normalization1dTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 4> L2Normalization2dTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 4> L2Normalization3dTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 4> L2Normalization4dTest(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<float, 4> L2Normalization1dNhwcTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 4> L2Normalization2dNhwcTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 4> L2Normalization3dNhwcTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 4> L2Normalization4dNhwcTest(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<float, 4> ConstantTest(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<uint8_t, 4> ConstantTestUint8(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<uint8_t, 4> BoundedReLuUint8Test(armnn::IWorkloadFactory& workloadFactory, float upperBound);
-LayerTestResult<uint8_t, 4> BoundedReLuUint8Test(armnn::IWorkloadFactory& workloadFactory,
-    float upperBound,
-    float lowerBound);
-
-LayerTestResult<uint8_t, 2> FullyConnectedUint8Test(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled);
-
-std::vector<LayerTestResult<uint8_t, 3>> SplitterUint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 3> CopyViaSplitterUint8Test(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<uint8_t, 3> MergerUint8Test(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<uint8_t, 4> AdditionUint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> AdditionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> AdditionBroadcastUint8Test(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<uint8_t, 4> SubtractionUint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> SubtractionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> SubtractionBroadcastUint8Test(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<uint8_t, 4> CompareActivationUint8Test(armnn::IWorkloadFactory&  workloadFactory,
-                                                       armnn::IWorkloadFactory&  refWorkloadFactory,
-                                                       armnn::ActivationFunction f);
-
-LayerTestResult<uint8_t, 2> CompareSoftmaxUint8Test(armnn::IWorkloadFactory& workloadFactory,
-    armnn::IWorkloadFactory& refWorkloadFactory,
-    float beta);
-
-LayerTestResult<uint8_t, 4> MultiplicationUint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> MultiplicationBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> MultiplicationBroadcast1DVectorUint8Test(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<uint8_t, 4> DivisionUint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> DivisionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> DivisionBroadcast1DVectorUint8Test(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<uint8_t, 4> SimpleConvolution2d3x5Uint8Test(armnn::IWorkloadFactory& workloadFactory,
-                                                            bool                     biasEnabled,
-                                                            const armnn::DataLayoutIndexed& layout);
-
-LayerTestResult<uint8_t, 4> SimpleConvolution2d3x3Uint8Test(armnn::IWorkloadFactory& workloadFactory,
-                                                            bool                     biasEnabled,
-                                                            const armnn::DataLayoutIndexed& layout);
-
-LayerTestResult<uint8_t, 4> DepthwiseConvolution2dUint8Test(armnn::IWorkloadFactory& workloadFactory,
-                                                            bool                     biasEnabled,
-                                                            const armnn::DataLayoutIndexed& layout);
-
-LayerTestResult<uint8_t, 4> DepthwiseConvolution2dDepthMul1Uint8Test(armnn::IWorkloadFactory& workloadFactory,
-                                                                     bool biasEnabled,
-                                                                     const armnn::DataLayoutIndexed& layout);
-
-LayerTestResult<uint8_t, 4> ConstantLinearActivationUint8Test(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<uint8_t, 4> ResizeBilinearNopUint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> SimpleResizeBilinearUint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> ResizeBilinearSqMinUint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> ResizeBilinearMinUint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> ResizeBilinearMagUint8Test(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<uint8_t, 4> BatchNormUint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> BatchNormUint8NhwcTest(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<uint8_t, 4> ConstantUint8Test(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<uint8_t, 1> Concatenation1dUint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 2> Concatenation2dDim0Uint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 2> Concatenation2dDim1Uint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 2> Concatenation2dDim0DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 2> Concatenation2dDim1DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 3> Concatenation3dDim0Uint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 3> Concatenation3dDim1Uint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 3> Concatenation3dDim2Uint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 3> Concatenation3dDim0DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 3> Concatenation3dDim1DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 3> Concatenation3dDim2DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory);
-
-
-LayerTestResult<float, 2> FullyConnectedLargeTest(armnn::IWorkloadFactory& workloadFactory,
-                                                  bool transposeWeights);
-LayerTestResult<float, 4> SimplePermuteFloat32Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> SimplePermuteUint8Test(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<uint8_t, 2> PadUint82dTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 3> PadUint83dTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> PadUint84dTest(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<float, 2> PadFloat322dTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 3> PadFloat323dTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 4> PadFloat324dTest(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<float, 4> PermuteFloat32ValueSet1Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 4> PermuteFloat32ValueSet2Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 4> PermuteFloat32ValueSet3Test(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<float, 2> LstmLayerFloat32WithCifgWithPeepholeNoProjectionTest
-        (armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 2>
-        LstmLayerFloat32NoCifgNoPeepholeNoProjectionTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 2>
-LstmLayerFloat32NoCifgWithPeepholeWithProjectionTest(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<float, 4> SimpleConvertFp16ToFp32Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<armnn::Half, 4> SimpleConvertFp32ToFp16Test(armnn::IWorkloadFactory& workloadFactory);
-
-
-LayerTestResult<uint8_t, 1> MeanUint8SimpleTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 3> MeanUint8SimpleAxisTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> MeanUint8KeepDimsTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 4> MeanUint8MultipleDimsTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<uint8_t, 1> MeanVtsUint8Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 1> MeanFloatSimpleTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 3> MeanFloatSimpleAxisTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 4> MeanFloatKeepDimsTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 4> MeanFloatMultipleDimsTest(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 1> MeanVtsFloat1Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 3> MeanVtsFloat2Test(armnn::IWorkloadFactory& workloadFactory);
-LayerTestResult<float, 3> MeanVtsFloat3Test(armnn::IWorkloadFactory& workloadFactory);
-
-LayerTestResult<float, 4> AdditionAfterMaxPoolTest(armnn::IWorkloadFactory& workloadFactory);
diff --git a/src/backends/test/LstmTestImpl.hpp b/src/backends/test/LstmTestImpl.hpp
deleted file mode 100644
index a7e595c941..0000000000
--- a/src/backends/test/LstmTestImpl.hpp
+++ /dev/null
@@ -1,1149 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#pragma once
-
-#include <armnn/ArmNN.hpp>
-#include <armnn/Tensor.hpp>
-#include <armnn/TypesUtils.hpp>
-
-#include <test/TensorHelpers.hpp>
-#include "QuantizeHelper.hpp"
-
-#include <backends/CpuTensorHandle.hpp>
-#include <backends/WorkloadFactory.hpp>
-
-LayerTestResult<float, 2> LstmNoCifgNoPeepholeNoProjectionTestImpl(armnn::IWorkloadFactory& workloadFactory,
-                                                                   const boost::multi_array<float, 2>& input,
-                                                                   const boost::multi_array<float, 2>& outputExpected)
-{
-    unsigned int batchSize = boost::numeric_cast<unsigned int>(input.shape()[0]);
-    unsigned int inputSize = boost::numeric_cast<unsigned int>(input.shape()[1]);
-    unsigned int outputSize = boost::numeric_cast<unsigned int>(outputExpected.shape()[1]);
-    // cellSize and outputSize have the same size when there is no projection.
-    unsigned numUnits = outputSize;
-
-
-    armnn::TensorInfo inputTensorInfo({batchSize , inputSize}, armnn::GetDataType<float>());
-    armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::GetDataType<float>());
-    armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::GetDataType<float>());
-
-
-    armnn::TensorInfo scratchBufferTensorInfo({batchSize, numUnits * 3}, armnn::GetDataType<float>());
-    armnn::TensorInfo cellStateOutTensorInfo({batchSize, numUnits}, armnn::GetDataType<float>());
-    armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, armnn::GetDataType<float>());
-    armnn::TensorInfo outputTensorInfo({batchSize, outputSize}, armnn::GetDataType<float>());
-
-
-    LayerTestResult<float, 2> ret(outputTensorInfo);
-
-    std::vector<float> inputVector;
-    inputVector.assign(input.data(), input.data() + (batchSize * inputSize));
-    auto inputTensor = MakeTensor<float,2>(inputTensorInfo, inputVector);
-
-    std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
-    auto cellStateInTensor = MakeTensor<float,2>(cellStateInTensorInfo, cellStateInVector);
-
-    std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
-    auto outputStateInTensor = MakeTensor<float,2>(outputStateInTensorInfo, outputStateInVector);
-
-    std::vector<float> scratchBufferVector(batchSize * numUnits * 3, 0.f);
-    auto scratchBufferTensor = MakeTensor<float,2>(scratchBufferTensorInfo, scratchBufferVector);
-
-    std::vector<float> outputStateOutVector(batchSize * outputSize, 0.f);
-    auto outputStateOutTensor = MakeTensor<float,2>(outputStateOutTensorInfo, outputStateOutVector);
-
-    std::vector<float> cellStateOutVector(batchSize * numUnits, 0.f);
-    auto cellStateOutTensor = MakeTensor<float,2>(cellStateOutTensorInfo, cellStateOutVector);
-
-    std::vector<float> outputVector;
-    outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * outputSize));
-    ret.outputExpected = MakeTensor<float, 2>(outputTensorInfo, outputVector);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
-            workloadFactory.CreateTensorHandle(cellStateInTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
-            workloadFactory.CreateTensorHandle(outputStateInTensorInfo);
-
-    std::unique_ptr<armnn::ITensorHandle> scratchHandle = workloadFactory.CreateTensorHandle(scratchBufferTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
-            workloadFactory.CreateTensorHandle(outputStateOutTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
-            workloadFactory.CreateTensorHandle(cellStateOutTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-
-    armnn::LstmQueueDescriptor data;
-    armnn::WorkloadInfo info;
-
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
-    AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
-
-    AddOutputToWorkload(data, info, scratchBufferTensorInfo, scratchHandle.get());
-    AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
-    AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    armnn::TensorInfo tensorInfo4({numUnits}, armnn::GetDataType<float>());
-    armnn::TensorInfo tensorInfo8({numUnits, 2}, armnn::GetDataType<float>());
-    armnn::TensorInfo tensorInfo16({numUnits, 4}, armnn::GetDataType<float>());
-
-    auto inputToInputWeights = MakeTensor<float, 2>(tensorInfo8, {-0.45018822f, -0.02338299f, -0.0870589f,
-                                                                  -0.34550029f, 0.04266912f, -0.15680569f,
-                                                                  -0.34856534f, 0.43890524f});
-
-    auto inputToForgetWeights = MakeTensor<float, 2>(tensorInfo8, {0.09701663f, 0.20334584f, -0.50592935f,
-                                                                   -0.31343272f, -0.40032279f, 0.44781327f,
-                                                                   0.01387155f, -0.35593212f});
-
-    auto inputToCellWeights = MakeTensor<float, 2>(tensorInfo8, {-0.50013041f, 0.1370284f, 0.11810488f, 0.2013163f,
-                                                                 -0.20583314f, 0.44344562f, 0.22077113f,
-                                                                 -0.29909778f});
-
-    auto inputToOutputWeights = MakeTensor<float, 2>(tensorInfo8, {-0.25065863f, -0.28290087f, 0.04613829f,
-                                                                   0.40525138f, 0.44272184f, 0.03897077f,
-                                                                   -0.1556896f, 0.19487578f});
-
-    auto recurrentToInputWeights = MakeTensor<float, 2>(tensorInfo16, {-0.0063535f, -0.2042388f, 0.31454784f,
-                                                                       -0.35746509f, 0.28902304f, 0.08183324f,
-                                                                       -0.16555229f, 0.02286911f, -0.13566875f,
-                                                                       0.03034258f, 0.48091322f, -0.12528998f,
-                                                                       0.24077177f, -0.51332325f, -0.33502164f,
-                                                                       0.10629296f});
-
-    auto recurrentToForgetWeights = MakeTensor<float, 2>(tensorInfo16, {-0.48684245f, -0.06655136f, 0.42224967f,
-                                                                        0.2112639f, 0.27654213f, 0.20864892f,
-                                                                        -0.07646349f, 0.45877004f, 0.00141793f,
-                                                                        -0.14609534f, 0.36447752f, 0.09196436f,
-                                                                        0.28053468f, 0.01560611f, -0.20127171f,
-                                                                        -0.01140004f});
-
-    auto recurrentToCellWeights = MakeTensor<float, 2>(tensorInfo16, {-0.3407414f, 0.24443203f, -0.2078532f,
-                                                                      0.26320225f, 0.05695659f, -0.00123841f,
-                                                                      -0.4744786f, -0.35869038f, -0.06418842f,
-                                                                      -0.13502428f, -0.501764f, 0.22830659f,
-                                                                      -0.46367589f, 0.26016325f, -0.03894562f,
-                                                                      -0.16368064f});
-
-    auto recurrentToOutputWeights = MakeTensor<float, 2>(tensorInfo16, {0.43385774f, -0.17194885f, 0.2718237f,
-                                                                        0.09215671f, 0.24107647f, -0.39835793f,
-                                                                        0.18212086f, 0.01301402f, 0.48572797f,
-                                                                        -0.50656658f, 0.20047462f, -0.20607421f,
-                                                                        -0.51818722f, -0.15390486f, 0.0468148f,
-                                                                        0.39922136f});
-
-    auto cellToInputWeights = MakeTensor<float, 1>(tensorInfo4, {0., 0., 0., 0.});
-
-    auto inputGateBias = MakeTensor<float, 1>(tensorInfo4, {0., 0., 0., 0.});
-
-    auto forgetGateBias = MakeTensor<float, 1>(tensorInfo4, {1., 1., 1., 1.});
-
-    auto cellBias = MakeTensor<float, 1>(tensorInfo4, {0., 0., 0., 0.});
-
-    auto outputGateBias = MakeTensor<float, 1>(tensorInfo4, {0., 0., 0., 0.});
-
-    armnn::ScopedCpuTensorHandle inputToInputWeightsTensor(tensorInfo8);
-    armnn::ScopedCpuTensorHandle inputToForgetWeightsTensor(tensorInfo8);
-    armnn::ScopedCpuTensorHandle inputToCellWeightsTensor(tensorInfo8);
-    armnn::ScopedCpuTensorHandle inputToOutputWeightsTensor(tensorInfo8);
-    armnn::ScopedCpuTensorHandle recurrentToForgetWeightsTensor(tensorInfo16);
-    armnn::ScopedCpuTensorHandle recurrentToInputWeightsTensor(tensorInfo16);
-    armnn::ScopedCpuTensorHandle recurrentToCellWeightsTensor(tensorInfo16);
-    armnn::ScopedCpuTensorHandle recurrentToOutputWeightsTensor(tensorInfo16);
-    armnn::ScopedCpuTensorHandle cellToInputWeightsTensor(tensorInfo4);
-    armnn::ScopedCpuTensorHandle inputGateBiasTensor(tensorInfo4);
-    armnn::ScopedCpuTensorHandle forgetGateBiasTensor(tensorInfo4);
-    armnn::ScopedCpuTensorHandle cellBiasTensor(tensorInfo4);
-    armnn::ScopedCpuTensorHandle outputGateBiasTensor(tensorInfo4);
-
-    AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, &inputToInputWeights[0][0]);
-    AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, &inputToForgetWeights[0][0]);
-    AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, &inputToCellWeights[0][0]);
-    AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, &inputToOutputWeights[0][0]);
-    AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, &recurrentToInputWeights[0][0]);
-    AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, &recurrentToForgetWeights[0][0]);
-    AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, &recurrentToCellWeights[0][0]);
-    AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, &recurrentToOutputWeights[0][0]);
-    AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, &cellToInputWeights[0]);
-    AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, &inputGateBias[0]);
-    AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, &forgetGateBias[0]);
-    AllocateAndCopyDataToITensorHandle(&cellBiasTensor, &cellBias[0]);
-    AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, &outputGateBias[0]);
-
-    data.m_InputToInputWeights = &inputToInputWeightsTensor;
-    data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
-    data.m_InputToCellWeights = &inputToCellWeightsTensor;
-    data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
-    data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
-    data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
-    data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
-    data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
-    data.m_CellToInputWeights = &cellToInputWeightsTensor;
-    data.m_InputGateBias = &inputGateBiasTensor;
-    data.m_ForgetGateBias = &forgetGateBiasTensor;
-    data.m_CellBias = &cellBiasTensor;
-    data.m_OutputGateBias = &outputGateBiasTensor;
-
-
-    // Flags to set test configuration
-    data.m_Parameters.m_ActivationFunc = 4;
-    data.m_Parameters.m_CifgEnabled = false;
-    data.m_Parameters.m_PeepholeEnabled = false;
-    data.m_Parameters.m_ProjectionEnabled = false;
-
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateLstm(data, info);
-    inputHandle->Allocate();
-    outputStateInHandle->Allocate();
-    cellStateInHandle->Allocate();
-
-    scratchHandle->Allocate();
-    outputStateOutHandle->Allocate();
-    cellStateOutHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0]);
-    CopyDataToITensorHandle(outputStateInHandle.get(), &outputStateInTensor[0][0]);
-    CopyDataToITensorHandle(cellStateInHandle.get(), &cellStateInTensor[0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get());
-
-    return ret;
-}
-
-
-LayerTestResult<float, 2>
-LstmLayerFloat32NoCifgWithPeepholeWithProjectionTestImpl(armnn::IWorkloadFactory& workloadFactory,
-                                             const boost::multi_array<float, 2>& input,
-                                             const boost::multi_array<float, 2>& outputExpected) {
-
-    unsigned int batchSize = 2;
-    unsigned int outputSize = 16;
-    unsigned int inputSize = 5;
-    unsigned numUnits = 20;
-
-    armnn::TensorInfo inputTensorInfo({batchSize , inputSize}, armnn::GetDataType<float>());
-    armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::GetDataType<float>());
-    armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::GetDataType<float>());
-
-    // Scratch buffer size without CIFG [batchSize, numUnits * 3]
-    armnn::TensorInfo scratchBufferTensorInfo({batchSize, numUnits * 3}, armnn::GetDataType<float>());
-    armnn::TensorInfo cellStateOutTensorInfo({batchSize, numUnits}, armnn::GetDataType<float>());
-    armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, armnn::GetDataType<float>());
-    armnn::TensorInfo outputTensorInfo({batchSize, outputSize}, armnn::GetDataType<float>());
-
-    LayerTestResult<float, 2> ret(outputTensorInfo);
-
-    std::vector<float> inputVector;
-    inputVector.assign(input.data(), input.data() + (batchSize * inputSize));
-    auto inputTensor = MakeTensor<float,2>(inputTensorInfo, inputVector);
-
-    std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
-    auto cellStateInTensor = MakeTensor<float,2>(cellStateInTensorInfo, cellStateInVector);
-
-    std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
-    auto outputStateInTensor = MakeTensor<float,2>(outputStateInTensorInfo, outputStateInVector);
-
-    std::vector<float> scratchBufferVector(batchSize * numUnits * 3, 0.f);
-    auto scratchBufferTensor = MakeTensor<float,2>(scratchBufferTensorInfo, scratchBufferVector);
-
-    std::vector<float> outputStateOutVector(batchSize * outputSize, 0.f);
-    auto outputStateOutTensor = MakeTensor<float,2>(outputStateOutTensorInfo, outputStateOutVector);
-
-    std::vector<float> cellStateOutVector(batchSize * numUnits, 0.f);
-    auto cellStateOutTensor = MakeTensor<float,2>(cellStateOutTensorInfo, cellStateOutVector);
-
-    std::vector<float> outputVector;
-    outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * outputSize));
-    ret.outputExpected = MakeTensor<float, 2>(outputTensorInfo, outputVector);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
-            workloadFactory.CreateTensorHandle(cellStateInTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
-            workloadFactory.CreateTensorHandle(outputStateInTensorInfo);
-
-    std::unique_ptr<armnn::ITensorHandle> scratchHandle = workloadFactory.CreateTensorHandle(scratchBufferTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
-            workloadFactory.CreateTensorHandle(outputStateOutTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
-            workloadFactory.CreateTensorHandle(cellStateOutTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::LstmQueueDescriptor data;
-    armnn::WorkloadInfo info;
-
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
-    AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
-
-    AddOutputToWorkload(data, info, scratchBufferTensorInfo, scratchHandle.get());
-    AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
-    AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    armnn::TensorInfo tensorInfo16({outputSize}, armnn::GetDataType<float>());
-    armnn::TensorInfo tensorInfo20({numUnits}, armnn::GetDataType<float>());
-    armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::GetDataType<float>());
-    armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, armnn::GetDataType<float>());
-    armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, armnn::GetDataType<float>());
-
-    auto inputToInputWeights =
-            MakeTensor<float, 2>(tensorInfo20x5, {0.021393683f,0.06124551f,  0.046905167f,-0.014657677f,-0.03149463f,
-                                                  0.09171803f, 0.14647801f,0.10797193f,   -0.0057968358f,0.0019193048f,
-                                                  -0.2726754f, 0.10154029f, -0.018539885f, 0.080349885f, -0.10262385f,
-                                                  -0.022599787f,-0.09121155f, -0.008675967f, -0.045206103f,-0.0821282f,
-                                                  -0.008045952f,0.015478081f, 0.055217247f,  0.038719587f, 0.044153627f,
-                                                  -0.06453243f,0.05031825f, -0.046935108f, -0.008164439f, 0.014574226f,
-                                                  -0.1671009f,   -0.15519552f, -0.16819797f,-0.13971269f,-0.11953059f,
-                                                  0.25005487f, -0.22790983f, 0.009855087f,  -0.028140958f, -0.11200698f,
-                                                  0.11295408f, -0.0035217577f, 0.054485075f,  0.05184695f, 0.064711206f,
-                                                  0.10989193f,   0.11674786f,  0.03490607f, 0.07727357f, 0.11390585f,
-                                                  -0.1863375f,  -0.1034451f, -0.13945189f, -0.049401227f, -0.18767063f,
-                                                  0.042483903f, 0.14233552f, 0.13832581f, 0.18350165f,    0.14545603f,
-                                                  -0.028545704f,0.024939531f,0.050929718f,0.0076203286f,-0.0029723682f,
-                                                  -0.042484224f, -0.11827596f, -0.09171104f,  -0.10808628f,-0.16327988f,
-                                                  -0.2273378f,   -0.0993647f, -0.017155107f,0.0023917493f,0.049272764f,
-                                                  0.0038534778f, 0.054764505f,   0.089753784f, 0.06947234f, 0.08014476f,
-                                                  -0.04544234f, -0.0497073f,-0.07135631f,  -0.048929106f,-0.004042012f,
-                                                  -0.009284026f, 0.018042054f, 0.0036860977f,-0.07427302f, -0.11434604f,
-                                                  -0.018995456f, 0.031487543f, 0.012834908f,0.019977754f,0.044256654f,
-                                                  -0.39292613f,  -0.18519334f, -0.11651281f,-0.06809892f, 0.011373677f
-            });
-
-    auto inputToForgetWeights =
-            MakeTensor<float, 2>(tensorInfo20x5, {-0.0018401089f, -0.004852237f,0.03698424f, 0.014181704f,0.028273236f,
-                                                   -0.016726194f, -0.05249759f,-0.10204261f, 0.00861066f,-0.040979505f,
-                                                   -0.009899187f,0.01923892f,-0.028177269f, -0.08535103f,-0.14585495f,
-                                                   0.10662567f,-0.01909731f,-0.017883534f,-0.0047269356f,-0.045103323f,
-                                                   0.0030784295f,0.076784775f,0.07463696f, 0.094531395f,0.0814421f,
-                                                   -0.12257899f, -0.033945758f,-0.031303465f, 0.045630626f,0.06843887f,
-                                                   -0.13492945f, -0.012480007f,-0.0811829f, -0.07224499f,-0.09628791f,
-                                                   0.045100946f,0.0012300825f, 0.013964662f, 0.099372394f,0.02543059f,
-                                                   0.06958324f,    0.034257296f, 0.0482646f, 0.06267997f,0.052625068f,
-                                                   0.12784666f,    0.07077897f,  0.025725935f, 0.04165009f,0.07241905f,
-                                                   0.018668644f, -0.037377294f,-0.06277783f,-0.08833636f,-0.040120605f,
-                                                   -0.011405586f,-0.007808335f,-0.010301386f,-0.005102167f,0.027717464f,
-                                                   0.05483423f, 0.11449111f, 0.11289652f,0.10939839f, 0.13396506f,
-                                                   -0.08402166f,-0.01901462f,  -0.044678304f,-0.07720565f,0.014350063f,
-                                                   -0.11757958f, -0.0652038f, -0.08185733f,-0.076754324f,-0.092614375f,
-                                                   0.10405491f, 0.052960336f, 0.035755895f,0.035839386f,-0.012540553f,
-                                                   0.036881298f,   0.02913376f,  0.03420159f,0.05448447f,-0.054523353f,
-                                                   0.02582715f, 0.02327355f, -0.011857179f,-0.0011980024f,-0.034641717f,
-                                                   -0.026125094f,-0.17582615f,-0.15923657f,-0.27486774f,-0.0006143371f,
-                                                   0.0001771948f,  -8.470171e-05f, 0.02651807f,0.045790765f,0.06956496f
-            });
-
-    auto inputToCellWeights =
-            MakeTensor<float, 2>(tensorInfo20x5, {-0.04580283f,   -0.09549462f,   -0.032418985f,  -0.06454633f,
-                                                  -0.043528453f,  0.043018587f,   -0.049152344f,  -0.12418144f,
-                                                  -0.078985475f,  -0.07596889f,   0.019484362f,   -0.11434962f,
-                                                  -0.0074034138f, -0.06314844f,   -0.092981495f,  0.0062155537f,
-                                                  -0.025034338f,  -0.0028890965f, 0.048929527f,   0.06235075f,
-                                                  0.10665918f,    -0.032036792f,  -0.08505916f,   -0.10843358f,
-                                                  -0.13002433f,   -0.036816437f,  -0.02130134f,   -0.016518239f,
-                                                  0.0047691227f,  -0.0025825808f, 0.066017866f,   0.029991534f,
-                                                  -0.10652836f,   -0.1037554f,    -0.13056071f,   -0.03266643f,
-                                                  -0.033702414f,  -0.006473424f,  -0.04611692f,   0.014419339f,
-                                                  -0.025174323f,  0.0396852f,     0.081777506f,   0.06157468f,
-                                                  0.10210095f,    -0.009658194f,  0.046511717f,   0.03603906f,
-                                                  0.0069369148f,  0.015960095f,   -0.06507666f,   0.09551598f,
-                                                  0.053568836f,   0.06408714f,    0.12835667f,    -0.008714329f,
-                                                  -0.20211966f,   -0.12093674f,   0.029450472f,   0.2849013f,
-                                                  -0.029227901f,  0.1164364f,     -0.08560263f,   0.09941786f,
-                                                  -0.036999565f,  -0.028842626f,  -0.0033637602f, -0.017012902f,
-                                                  -0.09720865f,   -0.11193351f,   -0.029155117f,  -0.017936034f,
-                                                  -0.009768936f,  -0.04223324f,   -0.036159635f,  0.06505112f,
-                                                  -0.021742892f,  -0.023377212f,  -0.07221364f,   -0.06430552f,
-                                                  0.05453865f,    0.091149814f,   0.06387331f,    0.007518393f,
-                                                  0.055960953f,   0.069779344f,   0.046411168f,   0.10509911f,
-                                                  0.07463894f,    0.0075130584f,  0.012850982f,   0.04555431f,
-                                                  0.056955688f,   0.06555285f,    0.050801456f,   -0.009862683f,
-                                                  0.00826772f,    -0.026555609f,  -0.0073611983f, -0.0014897042f
-            });
-
-    auto inputToOutputWeights =
-            MakeTensor<float, 2>(tensorInfo20x5, {-0.0998932f,   -0.07201956f, -0.052803773f,-0.15629593f,-0.15001918f,
-                                                  -0.07650751f,0.02359855f, -0.075155355f, -0.08037709f,  -0.15093534f,
-                                                  0.029517552f, -0.04751393f, 0.010350531f,-0.02664851f, -0.016839722f,
-                                                  -0.023121163f, 0.0077019283f, 0.012851257f, -0.05040649f,-0.0129761f,
-                                                  -0.021737747f,-0.038305793f,-0.06870586f, -0.01481247f,-0.001285394f,
-                                                  0.10124236f,  0.083122835f, 0.053313006f,-0.062235646f,-0.075637154f,
-                                                  -0.027833903f, 0.029774971f,  0.1130802f, 0.09218906f, 0.09506135f,
-                                                  -0.086665764f,-0.037162706f,-0.038880914f,-0.035832845f,-0.014481564f,
-                                                  -0.09825003f,-0.12048569f,-0.097665586f,-0.05287633f, -0.0964047f,
-                                                  -0.11366429f,  0.035777505f,  0.13568819f, 0.052451383f,0.050649304f,
-                                                  0.05798951f, -0.021852335f,-0.099848844f,0.014740475f,-0.078897946f,
-                                                  0.04974699f, 0.014160473f,  0.06973932f,    0.04964942f, 0.033364646f,
-                                                  0.08190124f,   0.025535367f, 0.050893165f, 0.048514254f,0.06945813f,
-                                                  -0.078907564f,-0.06707616f,  -0.11844508f, -0.09986688f,-0.07509403f,
-                                                  0.06263226f,   0.14925587f,   0.20188436f, 0.12098451f,0.14639415f,
-                                                  0.0015017595f, -0.014267382f, -0.03417257f,0.012711468f,0.0028300495f,
-                                                  -0.024758482f, -0.05098548f,-0.0821182f, 0.014225672f,  0.021544158f,
-                                                  0.08949725f,  0.07505268f, -0.0020780868f, 0.04908258f,0.06476295f,
-                                                  -0.022907063f,0.027562456f,0.040185735f, 0.019567577f,-0.015598739f,
-                                                  -0.049097303f, -0.017121866f, -0.083368234f,-0.02332002f,-0.0840956f
-            });
-
-    auto inputGateBias =
-            MakeTensor<float, 1>(tensorInfo20, {0.02234832f,  0.14757581f,   0.18176508f,  0.10380666f,  0.053110216f,
-                                                -0.06928846f, -0.13942584f,  -0.11816189f, 0.19483899f,  0.03652339f,
-                                                -0.10250295f, 0.036714908f,  -0.18426876f, 0.036065217f, 0.21810818f,
-                                                0.02383196f,  -0.043370757f, 0.08690144f,  -0.04444982f, 0.00030581196f
-            });
-
-    auto forgetGateBias =
-            MakeTensor<float, 1>(tensorInfo20, {0.035185695f, -0.042891346f, -0.03032477f, 0.23027696f,
-                                                0.11098921f,  0.15378423f,   0.09263801f,  0.09790885f,
-                                                0.09508917f,  0.061199076f,  0.07665568f,  -0.015443159f,
-                                                -0.03499149f, 0.046190713f,  0.08895977f,  0.10899629f,
-                                                0.40694186f,  0.06030037f,   0.012413437f, -0.06108739f
-            });
-
-    auto cellBias =
-            MakeTensor<float, 1>(tensorInfo20, {-0.024379363f, 0.0055531194f, 0.23377132f,   0.033463873f,
-                                                -0.1483596f,   -0.10639995f,  -0.091433935f, 0.058573797f,
-                                                -0.06809782f,  -0.07889636f,  -0.043246906f, -0.09829136f,
-                                                -0.4279842f,   0.034901652f,  0.18797937f,   0.0075234566f,
-                                                0.016178843f,  0.1749513f,    0.13975595f,   0.92058027f
-            });
-
-    auto outputGateBias =
-            MakeTensor<float, 1>(tensorInfo20, {0.046159424f,  -0.0012809046f, 0.03563469f, 0.12648113f, 0.027195795f,
-                                                0.35373217f,   -0.018957434f,  0.008907322f, -0.0762701f, 0.12018895f,
-                                                0.04216877f,   0.0022856654f,  0.040952638f,  0.3147856f,  0.08225149f,
-                                                -0.057416286f, -0.14995944f,   -0.008040261f, 0.13208859f, 0.029760877f
-            });
-
-    auto recurrentToInputWeights =
-            MakeTensor<float, 2>(tensorInfo20x16, {-0.001374326f,   -0.078856036f,   0.10672688f,    0.029162422f,
-                                                   -0.11585556f,    0.02557986f,     -0.13446963f,   -0.035785314f,
-                                                   -0.01244275f,    0.025961924f,    -0.02337298f,   -0.044228926f,
-                                                   -0.055839065f,   -0.046598054f,   -0.010546039f,  -0.06900766f,
-                                                   0.027239809f,    0.022582639f,    -0.013296484f,  -0.05459212f,
-                                                   0.08981f,        -0.045407712f,   0.08682226f,    -0.06867011f,
-                                                   -0.14390695f,    -0.02916037f,    0.000996957f,   0.091420636f,
-                                                   0.14283475f,     -0.07390571f,    -0.06402044f,   0.062524505f,
-                                                   -0.093129106f,   0.04860203f,     -0.08364217f,   -0.08119002f,
-                                                   0.009352075f,    0.22920375f,     0.0016303885f,  0.11583097f,
-                                                   -0.13732095f,    0.012405723f,    -0.07551853f,   0.06343048f,
-                                                   0.12162708f,     -0.031923793f,   -0.014335606f,  0.01790974f,
-                                                   -0.10650317f,    -0.0724401f,     0.08554849f,    -0.05727212f,
-                                                   0.06556731f,     -0.042729504f,   -0.043227166f,  0.011683251f,
-                                                   -0.013082158f,   -0.029302018f,   -0.010899579f,  -0.062036745f,
-                                                   -0.022509435f,   -0.00964907f,    -0.01567329f,   0.04260106f,
-                                                   -0.07787477f,    -0.11576462f,    0.017356863f,   0.048673786f,
-                                                   -0.017577527f,   -0.05527947f,    -0.082487635f,  -0.040137455f,
-                                                   -0.10820036f,    -0.04666372f,    0.022746278f,   -0.07851417f,
-                                                   0.01068115f,     0.032956902f,    0.022433773f,   0.0026891115f,
-                                                   0.08944216f,     -0.0685835f,     0.010513544f,   0.07228705f,
-                                                   0.02032331f,     -0.059686817f,   -0.0005566496f, -0.086984694f,
-                                                   0.040414046f,    -0.1380399f,     0.094208956f,   -0.05722982f,
-                                                   0.012092817f,    -0.04989123f,    -0.086576f,     -0.003399834f,
-                                                   -0.04696032f,    -0.045747425f,   0.10091314f,    0.048676282f,
-                                                   -0.029037097f,   0.031399418f,    -0.0040285117f, 0.047237843f,
-                                                   0.09504992f,     0.041799378f,    -0.049185462f,  -0.031518843f,
-                                                   -0.10516937f,    0.026374253f,    0.10058866f,    -0.0033195973f,
-                                                   -0.041975245f,   0.0073591834f,   0.0033782164f,  -0.004325073f,
-                                                   -0.10167381f,    0.042500053f,    -0.01447153f,   0.06464186f,
-                                                   -0.017142897f,   0.03312627f,     0.009205989f,   0.024138335f,
-                                                   -0.011337001f,   0.035530265f,    -0.010912711f,  0.0706555f,
-                                                   -0.005894094f,   0.051841937f,    -0.1401738f,    -0.02351249f,
-                                                   0.0365468f,      0.07590991f,     0.08838724f,    0.021681072f,
-                                                   -0.10086113f,    0.019608743f,    -0.06195883f,   0.077335775f,
-                                                   0.023646897f,    -0.095322326f,   0.02233014f,    0.09756986f,
-                                                   -0.048691444f,   -0.009579111f,   0.07595467f,    0.11480546f,
-                                                   -0.09801813f,    0.019894179f,    0.08502348f,    0.004032281f,
-                                                   0.037211012f,    0.068537936f,    -0.048005626f,  -0.091520436f,
-                                                   -0.028379958f,   -0.01556313f,    0.06554592f,    -0.045599163f,
-                                                   -0.01672207f,    -0.020169014f,   -0.011877351f,  -0.20212261f,
-                                                   0.010889619f,    0.0047078193f,   0.038385306f,   0.08540671f,
-                                                   -0.017140968f,   -0.0035865551f,  0.016678626f,   0.005633034f,
-                                                   0.015963363f,    0.00871737f,     0.060130805f,   0.028611384f,
-                                                   0.10109069f,     -0.015060172f,   -0.07894427f,   0.06401885f,
-                                                   0.011584063f,    -0.024466386f,   0.0047652307f,  -0.09041358f,
-                                                   0.030737216f,    -0.0046374933f,  0.14215417f,    -0.11823516f,
-                                                   0.019899689f,    0.006106124f,    -0.027092824f,  0.0786356f,
-                                                   0.05052217f,     -0.058925f,      -0.011402121f,  -0.024987547f,
-                                                   -0.0013661642f,  -0.06832946f,    -0.015667673f,  -0.1083353f,
-                                                   -0.00096863037f, -0.06988685f,    -0.053350925f,  -0.027275559f,
-                                                   -0.033664223f,   -0.07978348f,    -0.025200296f,  -0.017207067f,
-                                                   -0.058403496f,   -0.055697463f,   0.005798788f,   0.12965427f,
-                                                   -0.062582195f,   0.0013350133f,   -0.10482091f,   0.0379771f,
-                                                   0.072521195f,    -0.0029455067f,  -0.13797039f,   -0.03628521f,
-                                                   0.013806405f,    -0.017858358f,   -0.01008298f,   -0.07700066f,
-                                                   -0.017081132f,   0.019358726f,    0.0027079724f,  0.004635139f,
-                                                   0.062634714f,    -0.02338735f,    -0.039547626f,  -0.02050681f,
-                                                   0.03385117f,     -0.083611414f,   0.002862572f,   -0.09421313f,
-                                                   0.058618143f,    -0.08598433f,    0.00972939f,    0.023867095f,
-                                                   -0.053934585f,   -0.023203006f,   0.07452513f,    -0.048767887f,
-                                                   -0.07314807f,    -0.056307215f,   -0.10433547f,   -0.06440842f,
-                                                   0.04328182f,     0.04389765f,     -0.020006588f,  -0.09076438f,
-                                                   -0.11652589f,    -0.021705797f,   0.03345259f,    -0.010329105f,
-                                                   -0.025767034f,   0.013057034f,    -0.07316461f,   -0.10145612f,
-                                                   0.06358255f,     0.18531723f,     0.07759293f,    0.12006465f,
-                                                   0.1305557f,      0.058638252f,    -0.03393652f,   0.09622831f,
-                                                   -0.16253184f,    -2.4580743e-06f, 0.079869635f,   -0.070196845f,
-                                                   -0.005644518f,   0.06857898f,     -0.12598175f,   -0.035084512f,
-                                                   0.03156317f,     -0.12794146f,    -0.031963028f,  0.04692781f,
-                                                   0.030070418f,    0.0071660685f,   -0.095516115f,  -0.004643372f,
-                                                   0.040170413f,    -0.062104587f,   -0.0037324072f, 0.0554317f,
-                                                   0.08184801f,     -0.019164372f,   0.06791302f,    0.034257166f,
-                                                   -0.10307039f,    0.021943003f,    0.046745934f,   0.0790918f,
-                                                   -0.0265588f,     -0.007824208f,   0.042546265f,   -0.00977924f,
-                                                   -0.0002440307f,  -0.017384544f,   -0.017990116f,  0.12252321f,
-                                                   -0.014512694f,   -0.08251313f,    0.08861942f,    0.13589665f,
-                                                   0.026351685f,    0.012641483f,    0.07466548f,    0.044301085f,
-                                                   -0.045414884f,   -0.051112458f,   0.03444247f,    -0.08502782f,
-                                                   -0.04106223f,    -0.028126027f,   0.028473156f,   0.10467447f
-            });
-
-    auto recurrentToForgetWeights =
-            MakeTensor<float, 2>(tensorInfo20x16, {-0.057784554f,  -0.026057621f,  -0.068447545f,   -0.022581743f,
-                                                   0.14811787f,    0.10826372f,    0.09471067f,     0.03987225f,
-                                                   -0.0039523416f, 0.00030638507f, 0.053185795f,    0.10572994f,
-                                                   0.08414449f,    -0.022036452f,  -0.00066928595f, -0.09203576f,
-                                                   0.032950465f,   -0.10985798f,   -0.023809856f,   0.0021431844f,
-                                                   -0.02196096f,   -0.00326074f,   0.00058621005f,  -0.074678116f,
-                                                   -0.06193199f,   0.055729095f,   0.03736828f,     0.020123724f,
-                                                   0.061878487f,   -0.04729229f,   0.034919553f,    -0.07585433f,
-                                                   -0.04421272f,   -0.044019096f,  0.085488975f,    0.04058006f,
-                                                   -0.06890133f,   -0.030951202f,  -0.024628663f,   -0.07672815f,
-                                                   0.034293607f,   0.08556707f,    -0.05293577f,    -0.033561368f,
-                                                   -0.04899627f,   0.0241671f,     0.015736353f,    -0.095442444f,
-                                                   -0.029564252f,  0.016493602f,   -0.035026584f,   0.022337519f,
-                                                   -0.026871363f,  0.004780428f,   0.0077918363f,   -0.03601621f,
-                                                   0.016435321f,   -0.03263031f,   -0.09543275f,    -0.047392778f,
-                                                   0.013454138f,   0.028934088f,   0.01685226f,     -0.086110644f,
-                                                   -0.046250615f,  -0.01847454f,   0.047608484f,    0.07339695f,
-                                                   0.034546845f,   -0.04881143f,   0.009128804f,    -0.08802852f,
-                                                   0.03761666f,    0.008096139f,   -0.014454086f,   0.014361001f,
-                                                   -0.023502491f,  -0.0011840804f, -0.07607001f,    0.001856849f,
-                                                   -0.06509276f,   -0.006021153f,  -0.08570962f,    -0.1451793f,
-                                                   0.060212336f,   0.055259194f,   0.06974018f,     0.049454916f,
-                                                   -0.027794661f,  -0.08077226f,   -0.016179763f,   0.1169753f,
-                                                   0.17213494f,    -0.0056326236f, -0.053934924f,   -0.0124349f,
-                                                   -0.11520337f,   0.05409887f,    0.088759385f,    0.0019655675f,
-                                                   0.0042065294f,  0.03881498f,    0.019844765f,    0.041858196f,
-                                                   -0.05695512f,   0.047233116f,   0.038937137f,    -0.06542224f,
-                                                   0.014429736f,   -0.09719407f,   0.13908425f,     -0.05379757f,
-                                                   0.012321099f,   0.082840554f,   -0.029899208f,   0.044217527f,
-                                                   0.059855383f,   0.07711018f,    -0.045319796f,   0.0948846f,
-                                                   -0.011724666f,  -0.0033288454f, -0.033542685f,   -0.04764985f,
-                                                   -0.13873616f,   0.040668588f,   0.034832682f,    -0.015319203f,
-                                                   -0.018715994f,  0.046002675f,   0.0599172f,      -0.043107376f,
-                                                   0.0294216f,     -0.002314414f,  -0.022424703f,   0.0030315618f,
-                                                   0.0014641669f,  0.0029166266f,  -0.11878115f,    0.013738511f,
-                                                   0.12375372f,    -0.0006038222f, 0.029104086f,    0.087442465f,
-                                                   0.052958444f,   0.07558703f,    0.04817258f,     0.044462286f,
-                                                   -0.015213451f,  -0.08783778f,   -0.0561384f,     -0.003008196f,
-                                                   0.047060397f,   -0.002058388f,  0.03429439f,     -0.018839769f,
-                                                   0.024734668f,   0.024614193f,   -0.042046934f,   0.09597743f,
-                                                   -0.0043254104f, 0.04320769f,    0.0064070094f,   -0.0019131786f,
-                                                   -0.02558259f,   -0.022822596f,  -0.023273505f,   -0.02464396f,
-                                                   -0.10991725f,   -0.006240552f,  0.0074488563f,   0.024044557f,
-                                                   0.04383914f,    -0.046476185f,  0.028658995f,    0.060410924f,
-                                                   0.050786525f,   0.009452605f,   -0.0073054377f,  -0.024810238f,
-                                                   0.0052906186f,  0.0066939713f,  -0.0020913032f,  0.014515517f,
-                                                   0.015898481f,   0.021362653f,   -0.030262267f,   0.016587038f,
-                                                   -0.011442813f,  0.041154444f,   -0.007631438f,   -0.03423484f,
-                                                   -0.010977775f,  0.036152758f,   0.0066366293f,   0.11915515f,
-                                                   0.02318443f,    -0.041350313f,  0.021485701f,    -0.10906167f,
-                                                   -0.028218046f,  -0.00954771f,   0.020531068f,    -0.11995105f,
-                                                   -0.03672871f,   0.024019798f,   0.014255957f,    -0.05221243f,
-                                                   -0.00661567f,   -0.04630967f,   0.033188973f,    0.10107534f,
-                                                   -0.014027541f,  0.030796422f,   -0.10270911f,    -0.035999842f,
-                                                   0.15443139f,    0.07684145f,    0.036571592f,    -0.035900835f,
-                                                   -0.0034699554f, 0.06209149f,    0.015920248f,    -0.031122351f,
-                                                   -0.03858649f,   0.01849943f,    0.13872518f,     0.01503974f,
-                                                   0.069941424f,   -0.06948533f,   -0.0088794185f,  0.061282158f,
-                                                   -0.047401894f,  0.03100163f,    -0.041533746f,   -0.10430945f,
-                                                   0.044574402f,   -0.01425562f,   -0.024290353f,   0.034563623f,
-                                                   0.05866852f,    0.023947537f,   -0.09445152f,    0.035450947f,
-                                                   0.02247216f,    -0.0042998926f, 0.061146557f,    -0.10250651f,
-                                                   0.020881841f,   -0.06747029f,   0.10062043f,     -0.0023941975f,
-                                                   0.03532124f,    -0.016341697f,  0.09685456f,     -0.016764693f,
-                                                   0.051808182f,   0.05875331f,    -0.04536488f,    0.001626336f,
-                                                   -0.028892258f,  -0.01048663f,   -0.009793449f,   -0.017093895f,
-                                                   0.010987891f,   0.02357273f,    -0.00010856845f, 0.0099760275f,
-                                                   -0.001845119f,  -0.03551521f,   0.0018358806f,   0.05763657f,
-                                                   -0.01769146f,   0.040995963f,   0.02235177f,     -0.060430344f,
-                                                   0.11475477f,    -0.023854522f,  0.10071741f,     0.0686208f,
-                                                   -0.014250481f,  0.034261297f,   0.047418304f,    0.08562733f,
-                                                   -0.030519066f,  0.0060542435f,  0.014653856f,    -0.038836084f,
-                                                   0.04096551f,    0.032249358f,   -0.08355519f,    -0.026823482f,
-                                                   0.056386515f,   -0.010401743f,  -0.028396193f,   0.08507674f,
-                                                   0.014410365f,   0.020995233f,   0.17040324f,     0.11511526f,
-                                                   0.02459721f,    0.0066619175f,  0.025853224f,    -0.023133837f,
-                                                   -0.081302024f,  0.017264642f,   -0.009585969f,   0.09491168f,
-                                                   -0.051313367f,  0.054532815f,   -0.014298593f,   0.10657464f,
-                                                   0.007076659f,   0.10964551f,    0.0409152f,      0.008275321f,
-                                                   -0.07283536f,   0.07937492f,    0.04192024f,     -0.1075027f
-            });
-
-    auto recurrentToCellWeights =
-            MakeTensor<float, 2>(tensorInfo20x16, {-0.037322544f,   0.018592842f,   0.0056175636f,  -0.06253426f,
-                                                   0.055647098f,    -0.05713207f,   -0.05626563f,   0.005559383f,
-                                                   0.03375411f,     -0.025757805f,  -0.088049285f,  0.06017052f,
-                                                   -0.06570978f,    0.007384076f,   0.035123326f,   -0.07920549f,
-                                                   0.053676967f,    0.044480428f,   -0.07663568f,   0.0071805613f,
-                                                   0.08089997f,     0.05143358f,    0.038261272f,   0.03339287f,
-                                                   -0.027673481f,   0.044746667f,   0.028349208f,   0.020090483f,
-                                                   -0.019443132f,   -0.030755889f,  -0.0040000007f, 0.04465846f,
-                                                   -0.021585021f,   0.0031670958f,  0.0053199246f,  -0.056117613f,
-                                                   -0.10893326f,    0.076739706f,   -0.08509834f,   -0.027997585f,
-                                                   0.037871376f,    0.01449768f,    -0.09002357f,   -0.06111149f,
-                                                   -0.046195522f,   0.0422062f,     -0.005683705f,  -0.1253618f,
-                                                   -0.012925729f,   -0.04890792f,   0.06985068f,    0.037654128f,
-                                                   0.03398274f,     -0.004781977f,  0.007032333f,   -0.031787455f,
-                                                   0.010868644f,    -0.031489216f,  0.09525667f,    0.013939797f,
-                                                   0.0058680447f,   0.0167067f,     0.02668468f,    -0.04797466f,
-                                                   -0.048885044f,   -0.12722108f,   0.035304096f,   0.06554885f,
-                                                   0.00972396f,     -0.039238118f,  -0.05159735f,   -0.11329045f,
-                                                   0.1613692f,      -0.03750952f,   0.06529313f,    -0.071974665f,
-                                                   -0.11769596f,    0.015524369f,   -0.0013754242f, -0.12446318f,
-                                                   0.02786344f,     -0.014179351f,  0.005264273f,   0.14376344f,
-                                                   0.015983658f,    0.03406988f,    -0.06939408f,   0.040699873f,
-                                                   0.02111075f,     0.09669095f,    0.041345075f,   -0.08316494f,
-                                                   -0.07684199f,    -0.045768797f,  0.032298047f,   -0.041805092f,
-                                                   0.0119405f,      0.0061010392f,  0.12652606f,    0.0064572375f,
-                                                   -0.024950314f,   0.11574242f,    0.04508852f,    -0.04335324f,
-                                                   0.06760663f,     -0.027437469f,  0.07216407f,    0.06977076f,
-                                                   -0.05438599f,    0.034033038f,   -0.028602652f,  0.05346137f,
-                                                   0.043184172f,    -0.037189785f,  0.10420091f,    0.00882477f,
-                                                   -0.054019816f,   -0.074273005f,  -0.030617684f,  -0.0028467078f,
-                                                   0.024302477f,    -0.0038869337f, 0.005332455f,   0.0013399826f,
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-                                  -0.10100678f,   0.0628376f,     0.04447668f,    0.017961001f,  -0.10094388f,
-                                  -0.10190601f,   0.18335468f,    0.10494553f,    -0.052095775f, -0.0026118709f,
-                                  0.10539724f,    -0.04383912f,   -0.042349473f,  0.08438151f,   -0.1947263f,
-                                  0.02251204f,    0.11216432f,    -0.10307853f,   0.17351969f,   -0.039091777f,
-                                  0.08066188f,    -0.00561982f,   0.12633002f,    0.11335965f,   -0.0088127935f,
-                                  -0.019777594f,  0.06864014f,    -0.059751723f,  0.016233567f,  -0.06894641f,
-                                  -0.28651384f,   -0.004228674f,  0.019708522f,   -0.16305895f,  -0.07468996f,
-                                  -0.0855457f,    0.099339016f,   -0.07580735f,   -0.13775392f,  0.08434318f,
-                                  0.08330512f,    -0.12131499f,   0.031935584f,   0.09180414f,   -0.08876437f,
-                                  -0.08049874f,   0.008753825f,   0.03498998f,    0.030215185f,  0.03907079f,
-                                  0.089751154f,   0.029194152f,   -0.03337423f,   -0.019092513f, 0.04331237f,
-                                  0.04299654f,    -0.036394123f,  -0.12915532f,   0.09793732f,   0.07512415f,
-                                  -0.11319543f,   -0.032502122f,  0.15661901f,    0.07671967f,   -0.005491124f,
-                                  -0.19379048f,   -0.218606f,     0.21448623f,    0.017840758f,  0.1416943f,
-                                  -0.07051762f,   0.19488361f,    0.02664691f,    -0.18104725f,  -0.09334311f,
-                                  0.15026465f,    -0.15493552f,   -0.057762887f,  -0.11604192f,  -0.262013f,
-                                  -0.01391798f,   0.012185008f,   0.11156489f,    -0.07483202f,  0.06693364f,
-                                  -0.26151478f,   0.046425626f,   0.036540434f,   -0.16435726f,  0.17338543f,
-                                  -0.21401681f,   -0.11385144f,   -0.08283257f,   -0.069031075f, 0.030635102f,
-                                  0.010969227f,   0.11109743f,    0.010919218f,   0.027526086f,  0.13519906f,
-                                  0.01891392f,    -0.046839405f,  -0.040167913f,  0.017953383f,  -0.09700955f,
-                                  0.0061885654f,  -0.07000971f,   0.026893595f,   -0.038844477f, 0.14543656f
-                                 });
-
-    std::vector<float> projectionBiasVector(outputSize, 0.f);
-    auto projectionBias = MakeTensor<float,1>(tensorInfo16, projectionBiasVector);
-
-    armnn::ScopedCpuTensorHandle inputToInputWeightsTensor(tensorInfo20x5);
-    armnn::ScopedCpuTensorHandle inputToForgetWeightsTensor(tensorInfo20x5);
-    armnn::ScopedCpuTensorHandle inputToCellWeightsTensor(tensorInfo20x5);
-    armnn::ScopedCpuTensorHandle inputToOutputWeightsTensor(tensorInfo20x5);
-    armnn::ScopedCpuTensorHandle recurrentToForgetWeightsTensor(tensorInfo20x16);
-    armnn::ScopedCpuTensorHandle recurrentToInputWeightsTensor(tensorInfo20x16);
-    armnn::ScopedCpuTensorHandle recurrentToCellWeightsTensor(tensorInfo20x16);
-    armnn::ScopedCpuTensorHandle recurrentToOutputWeightsTensor(tensorInfo20x16);
-    armnn::ScopedCpuTensorHandle cellToInputWeightsTensor(tensorInfo20);
-    armnn::ScopedCpuTensorHandle inputGateBiasTensor(tensorInfo20);
-    armnn::ScopedCpuTensorHandle forgetGateBiasTensor(tensorInfo20);
-    armnn::ScopedCpuTensorHandle cellBiasTensor(tensorInfo20);
-    armnn::ScopedCpuTensorHandle outputGateBiasTensor(tensorInfo20);
-    armnn::ScopedCpuTensorHandle cellToForgetWeightsTensor(tensorInfo20);
-    armnn::ScopedCpuTensorHandle cellToOutputWeightsTensor(tensorInfo20);
-    armnn::ScopedCpuTensorHandle projectionWeightsTensor(tensorInfo16x20);
-    armnn::ScopedCpuTensorHandle projectionBiasTensor(tensorInfo16);
-
-    AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, &inputToInputWeights[0][0]);
-    AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, &inputToForgetWeights[0][0]);
-    AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, &inputToCellWeights[0][0]);
-    AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, &inputToOutputWeights[0][0]);
-    AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, &recurrentToInputWeights[0][0]);
-    AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, &recurrentToForgetWeights[0][0]);
-    AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, &recurrentToCellWeights[0][0]);
-    AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, &recurrentToOutputWeights[0][0]);
-    AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, &cellToInputWeights[0]);
-    AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, &inputGateBias[0]);
-    AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, &forgetGateBias[0]);
-    AllocateAndCopyDataToITensorHandle(&cellBiasTensor, &cellBias[0]);
-    AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, &outputGateBias[0]);
-    AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, &cellToForgetWeights[0]);
-    AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, &cellToOutputWeights[0]);
-    AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, &projectionWeights[0][0]);
-    AllocateAndCopyDataToITensorHandle(&projectionBiasTensor, &projectionBias[0]);
-
-    data.m_InputToInputWeights = &inputToInputWeightsTensor;
-    data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
-    data.m_InputToCellWeights = &inputToCellWeightsTensor;
-    data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
-    data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
-    data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
-    data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
-    data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
-    data.m_CellToInputWeights = &cellToInputWeightsTensor;
-    data.m_InputGateBias = &inputGateBiasTensor;
-    data.m_ForgetGateBias = &forgetGateBiasTensor;
-    data.m_CellBias = &cellBiasTensor;
-    data.m_OutputGateBias = &outputGateBiasTensor;
-    data.m_CellToForgetWeights = &cellToForgetWeightsTensor;
-    data.m_CellToOutputWeights = &cellToOutputWeightsTensor;
-    data.m_ProjectionWeights = &projectionWeightsTensor;
-    data.m_ProjectionBias = &projectionBiasTensor;
-
-    // Flags to set test configuration
-    data.m_Parameters.m_ActivationFunc = 4;
-    data.m_Parameters.m_CifgEnabled = false;
-    data.m_Parameters.m_PeepholeEnabled = true;
-    data.m_Parameters.m_ProjectionEnabled = true;
-
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateLstm(data, info);
-    inputHandle->Allocate();
-    outputStateInHandle->Allocate();
-    cellStateInHandle->Allocate();
-
-    scratchHandle->Allocate();
-    outputStateOutHandle->Allocate();
-    cellStateOutHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0]);
-    CopyDataToITensorHandle(outputStateInHandle.get(), &outputStateInTensor[0][0]);
-    CopyDataToITensorHandle(cellStateInHandle.get(), &cellStateInTensor[0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get());
-
-    return ret;
-
-}
-
-
-LayerTestResult<float, 2> LstmLayerWithCifgWithPeepholeNoProjectionTestImpl(armnn::IWorkloadFactory& workloadFactory,
-                                                                   const boost::multi_array<float, 2>& input,
-                                                                   const boost::multi_array<float, 2>& outputExpected)
-{
-    bool cifgEnabled = true;
-    bool peepholeEnabled = true;
-    bool projectionEnabled = false;
-    // These are not the input and the output of Lstm yet
-    unsigned int batchSize = boost::numeric_cast<unsigned int>(input.shape()[0]);
-    unsigned int inputSize = boost::numeric_cast<unsigned int>(input.shape()[1]);
-
-    unsigned int outputSize = boost::numeric_cast<unsigned int>(outputExpected.shape()[1]);
-
-    const unsigned int cellSize = outputSize;
-
-    // Decide the shape of all input tensors
-    armnn::TensorInfo inputTensorInfo({batchSize , inputSize}, armnn::GetDataType<float>());
-    armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, armnn::GetDataType<float>());
-    armnn::TensorInfo cellStateInTensorInfo({batchSize, cellSize}, armnn::GetDataType<float>());
-
-    unsigned int scratchBufferSize = cifgEnabled ? cellSize * 4 : cellSize * 3;
-    armnn::TensorInfo scratchBufferTensorInfo({batchSize, scratchBufferSize}, armnn::GetDataType<float>());
-    armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, armnn::GetDataType<float>());
-    armnn::TensorInfo cellStateOutTensorInfo({batchSize, cellSize}, armnn::GetDataType<float>());
-    armnn::TensorInfo outputTensorInfo({batchSize, outputSize}, armnn::GetDataType<float>());
-
-    // List of inputs
-    std::vector<float> inputData;
-    inputData.assign(input.data(), input.data() + batchSize*inputSize);
-    auto inputTensor = MakeTensor<float,2>(inputTensorInfo, inputData);
-
-    std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
-    auto outputStateInTensor = MakeTensor<float, 2>(outputStateInTensorInfo, outputStateInVector);
-
-    std::vector<float> cellStateInVector(batchSize * cellSize, 0.f);
-    auto cellStateInTensor = MakeTensor<float, 2>(cellStateInTensorInfo, cellStateInVector);
-
-
-    // Prepare all the weights in the descriptor for LSTM
-    armnn::LstmQueueDescriptor data;
-    armnn::TensorInfo tensorInfoInput({cellSize, inputSize}, armnn::GetDataType<float>());
-    armnn::TensorInfo tensorInfoOutput({cellSize, outputSize}, armnn::GetDataType<float>());
-    armnn::TensorInfo tensorInfoNumUnits({cellSize}, armnn::GetDataType<float>());
-
-    auto inputToCellWeights = MakeTensor<float, 2>(tensorInfoInput,
-                                                     {-0.49770179f, -0.27711356f, -0.09624726f, 0.05100781f,
-                                                     0.04717243f, 0.48944736f, -0.38535351f,
-                                                     -0.17212132f});
-    auto inputToForgetWeights = MakeTensor<float, 2>(tensorInfoInput,
-                                                     {-0.55291498f, -0.42866567f, 0.13056988f,
-                                                       -0.3633365f, -0.22755712f, 0.28253698f, 0.24407166f,
-                                                       0.33826375f});
-    auto inputToOutputWeights = MakeTensor<float, 2>(tensorInfoInput,
-                                                     {0.10725588f, -0.02335852f, -0.55932593f,
-                                                       -0.09426838f, -0.44257352f, 0.54939759f,
-                                                       0.01533556f, 0.42751634f});
-    auto cellBias = MakeTensor<float, 1>(tensorInfoNumUnits, {0.f, 0.f, 0.f, 0.f});
-    auto forgetGateBias = MakeTensor<float, 1>(tensorInfoNumUnits, {1.f, 1.f, 1.f, 1.f});
-    auto outputGateBias = MakeTensor<float, 1>(tensorInfoNumUnits, {0.f, 0.f, 0.f, 0.f});
-
-    auto recurrentToCellWeights = MakeTensor<float, 2>(tensorInfoOutput,
-                {0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f, 0.42957711f,
-                 0.01841056f, -0.32764608f, -0.33027974f, -0.10826075f, 0.20675004f,
-                 0.19069612f, -0.03026325f, -0.54532051f, 0.33003211f, 0.44901288f,
-                 0.21193194f});
-    auto recurrentToForgetWeights = MakeTensor<float, 2>(tensorInfoOutput,
-                 {-0.13832897f, -0.0515101f, -0.2359007f, -0.16661474f, -0.14340827f,
-                  0.36986142f, 0.23414481f, 0.55899f, 0.10798943f, -0.41174671f, 0.17751795f,
-                  -0.34484994f, -0.35874045f, -0.11352962f, 0.27268326f, 0.54058349f});
-
-    auto recurrentToOutputWeights = MakeTensor<float, 2>(tensorInfoOutput,
-                {0.41613156f, 0.42610586f, -0.16495961f, -0.5663873f, 0.30579174f, -0.05115908f,
-                 -0.33941799f, 0.23364776f, 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f,
-                 0.50248802f, 0.26114327f, -0.43736315f, 0.33149987f});
-
-    auto cellToForgetWeights = MakeTensor<float, 1>(tensorInfoNumUnits,
-                {0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f});
-    auto cellToOutputWeights = MakeTensor<float, 1>(tensorInfoNumUnits,
-                {-0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f});
-
-    armnn::ScopedCpuTensorHandle inputToCellWeightsTensor(tensorInfoInput);
-    armnn::ScopedCpuTensorHandle inputToForgetWeightsTensor(tensorInfoInput);
-    armnn::ScopedCpuTensorHandle inputToOutputWeightsTensor(tensorInfoInput);
-
-    armnn::ScopedCpuTensorHandle cellBiasTensor(tensorInfoNumUnits);
-    armnn::ScopedCpuTensorHandle forgetGateBiasTensor(tensorInfoNumUnits);
-    armnn::ScopedCpuTensorHandle outputGateBiasTensor(tensorInfoNumUnits);
-
-    armnn::ScopedCpuTensorHandle recurrentToCellWeightsTensor(tensorInfoOutput);
-    armnn::ScopedCpuTensorHandle recurrentToForgetWeightsTensor(tensorInfoOutput);
-    armnn::ScopedCpuTensorHandle recurrentToOutputWeightsTensor(tensorInfoOutput);
-
-
-    armnn::ScopedCpuTensorHandle cellToForgetWeightsTensor(tensorInfoNumUnits);
-    armnn::ScopedCpuTensorHandle cellToOutputWeightsTensor(tensorInfoNumUnits);
-
-    AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, &inputToCellWeights[0][0]);
-    AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, &inputToForgetWeights[0][0]);
-    AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, &inputToOutputWeights[0][0]);
-
-    AllocateAndCopyDataToITensorHandle(&cellBiasTensor, &cellBias[0]);
-    AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, &forgetGateBias[0]);
-    AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, &outputGateBias[0]);
-
-    AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, &recurrentToCellWeights[0][0]);
-    AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, &recurrentToForgetWeights[0][0]);
-    AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, &recurrentToOutputWeights[0][0]);
-
-    AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, &cellToForgetWeights[0]);
-    AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, &cellToOutputWeights[0]);
-
-
-    data.m_InputToCellWeights = &inputToCellWeightsTensor;
-    data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
-    data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
-
-    data.m_CellBias = &cellBiasTensor;
-    data.m_ForgetGateBias = &forgetGateBiasTensor;
-    data.m_OutputGateBias = &outputGateBiasTensor;
-
-    data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
-    data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
-    data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
-
-    data.m_CellToForgetWeights = &cellToForgetWeightsTensor;
-    data.m_CellToOutputWeights = &cellToOutputWeightsTensor;
-
-    // other parameters for the descriptor
-    data.m_Parameters.m_CifgEnabled = cifgEnabled;
-    data.m_Parameters.m_ProjectionEnabled = projectionEnabled;
-    data.m_Parameters.m_PeepholeEnabled = peepholeEnabled;
-
-    data.m_Parameters.m_ActivationFunc = 4;
-    data.m_Parameters.m_ClippingThresProj = 0.0;
-    data.m_Parameters.m_ClippingThresCell = 0.0;
-
-
-    // List of outputs
-    std::vector<float> scratchBufferVector(batchSize * scratchBufferSize, 0.f);
-    auto scratchBufferTensor = MakeTensor<float,2>(scratchBufferTensorInfo, scratchBufferVector);
-    LayerTestResult<float, 2> ret0(scratchBufferTensorInfo);
-
-    // Output state for a certain time step
-    std::vector<float> outputStateOutVector(batchSize * outputSize, 0.f);
-    auto outputStateOutTensor = MakeTensor<float,2>(outputStateOutTensorInfo, outputStateOutVector);
-    LayerTestResult<float, 2> ret1(outputStateOutTensorInfo);
-
-    // Cell state for a certain time step
-    std::vector<float> cellStateOutVector(batchSize * cellSize, 0.f);
-    auto cellStateOutTensor = MakeTensor<float,2>(cellStateOutTensorInfo, cellStateOutVector);
-    LayerTestResult<float, 2> ret2(cellStateOutTensorInfo);
-
-    // Output for a certain time step
-    std::vector<float> outputVector(batchSize * outputSize, 0.f);
-    auto outputTensor = MakeTensor<float, 2>(outputTensorInfo, outputVector);
-    std::vector<float> outputData;
-    outputData.assign(outputExpected.data(), outputExpected.data() + batchSize*outputSize);
-    LayerTestResult<float, 2> ret3(outputTensorInfo);
-    ret3.outputExpected = MakeTensor<float, 2>(outputTensorInfo, outputData);
-
-    // Prepare the inputs and outputs for the workload
-    std::unique_ptr<armnn::ITensorHandle> inputHandle =
-            workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
-            workloadFactory.CreateTensorHandle(outputStateInTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
-            workloadFactory.CreateTensorHandle(cellStateInTensorInfo);
-
-    std::unique_ptr<armnn::ITensorHandle> scratchBufferHandle =
-            workloadFactory.CreateTensorHandle(scratchBufferTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
-            workloadFactory.CreateTensorHandle(outputStateOutTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
-            workloadFactory.CreateTensorHandle(cellStateOutTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle =
-            workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
-    AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
-
-    AddOutputToWorkload(data, info, scratchBufferTensorInfo, scratchBufferHandle.get());
-    AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
-    AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateLstm(data, info);
-
-
-    inputHandle->Allocate();
-    outputStateInHandle->Allocate();
-    cellStateInHandle->Allocate();
-
-    scratchBufferHandle->Allocate();
-    outputStateOutHandle->Allocate();
-    cellStateOutHandle->Allocate();
-    outputHandle->Allocate();
-
-
-    CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0]);
-    CopyDataToITensorHandle(outputStateInHandle.get(), &outputStateInTensor[0][0]);
-    CopyDataToITensorHandle(cellStateInHandle.get(), &cellStateInTensor[0][0]);
-
-    CopyDataToITensorHandle(scratchBufferHandle.get(), &scratchBufferTensor[0][0]);
-    CopyDataToITensorHandle(outputStateOutHandle.get(), &outputStateOutTensor[0][0]);
-    CopyDataToITensorHandle(cellStateOutHandle.get(), &cellStateOutTensor[0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret0.output[0][0], scratchBufferHandle.get());
-    CopyDataFromITensorHandle(&ret1.output[0][0], outputStateOutHandle.get());
-    CopyDataFromITensorHandle(&ret2.output[0][0], cellStateOutHandle.get());
-    CopyDataFromITensorHandle(&ret3.output[0][0], outputHandle.get());
-
-    return ret3;
-}
diff --git a/src/backends/test/NormTestImpl.hpp b/src/backends/test/NormTestImpl.hpp
deleted file mode 100644
index de954b95e0..0000000000
--- a/src/backends/test/NormTestImpl.hpp
+++ /dev/null
@@ -1,343 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include <armnn/Exceptions.hpp>
-#include <armnn/LayerSupport.hpp>
-#include "armnn/Types.hpp"
-
-#include <backends/CpuTensorHandle.hpp>
-#include <backends/WorkloadFactory.hpp>
-
-LayerTestResult<float,4> SimpleNormalizationTestImpl(armnn::IWorkloadFactory& workloadFactory,
-                                                     armnn::NormalizationAlgorithmChannel normChannel,
-                                                     armnn::NormalizationAlgorithmMethod normMethod)
-{
-    const unsigned int inputHeight = 2;
-    const unsigned int inputWidth = 2;
-    const unsigned int inputChannels = 1;
-    const unsigned int inputNum = 2;
-
-    unsigned int outputHeight = inputHeight;
-    unsigned int outputWidth = inputWidth;
-    unsigned int outputChannels = inputChannels;
-    unsigned int outputNum = inputNum;
-
-    unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth };
-    unsigned int outputShape[] = { outputNum, outputChannels, outputHeight, outputWidth };
-
-    auto inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
-    auto outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32);
-
-    LayerTestResult<float,4> ret(outputTensorInfo);
-
-    auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({
-        // Batch #0
-        1.0f, 2.0f,
-        3.0f, 4.0f,
-        // Batch #1
-        5.0f, 6.0f,
-        7.0f, 8.0f
-    }));
-
-    float alpha = 1.f;
-    float beta = 1.f;
-    float kappa = 1.f;
-    uint32_t normSize = 3;
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::NormalizationQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-    data.m_Parameters.m_NormChannelType = normChannel;
-    data.m_Parameters.m_NormMethodType = normMethod;
-    data.m_Parameters.m_NormSize = normSize;
-    data.m_Parameters.m_Alpha = alpha;
-    data.m_Parameters.m_Beta = beta;
-    data.m_Parameters.m_K = kappa;
-    data.m_Parameters.m_DataLayout = armnn::DataLayout::NCHW;
-
-    armnn::PassthroughCpuTensorHandle refHandle(outputTensorInfo, &ret.outputExpected[0][0][0][0]);
-    armnn::NormalizationQueueDescriptor refData = data;
-    armnn::WorkloadInfo refInfo = info;
-    SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, &refHandle);
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateNormalization(data, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-
-    switch (normMethod)
-    {
-        case armnn::NormalizationAlgorithmMethod::LocalBrightness:
-        {
-            switch (normChannel)
-            {
-                case armnn::NormalizationAlgorithmChannel::Within:
-                {
-                    // When normalising within channels, the 3x3 kernel covers the entire 2x2 input at every index.
-                    // Therefore, all output values should equal the inputs, but divided by:
-                    // pow((kappa + (accumulatedScale * alpha)), beta)
-                    // ...where accumulatedScale is the sum of every element squared.
-                    float divisor[inputNum];
-                    for(int i = 0; i < boost::numeric_cast<int>(inputNum); i++)
-                    {
-                        float accumulatedScale = input[i][0][0][0]*input[i][0][0][0] +
-                                                 input[i][0][0][1]*input[i][0][0][1] +
-                                                 input[i][0][1][0]*input[i][0][1][0] +
-                                                 input[i][0][1][1]*input[i][0][1][1];
-                        divisor[i] = powf((kappa + accumulatedScale * alpha), beta);
-                    }
-                    ret.outputExpected = MakeTensor<float, 4>(outputTensorInfo,
-                                                              std::vector<float>({input[0][0][0][0]/divisor[0],
-                                                                                  input[0][0][0][1]/divisor[0],
-                                                                                  input[0][0][1][0]/divisor[0],
-                                                                                  input[0][0][1][1]/divisor[0],
-                                                                                  input[1][0][0][0]/divisor[1],
-                                                                                  input[1][0][0][1]/divisor[1],
-                                                                                  input[1][0][1][0]/divisor[1],
-                                                                                  input[1][0][1][1]/divisor[1]}));
-                    break;
-                }
-                case armnn::NormalizationAlgorithmChannel::Across:
-                {
-                    // When normalising across channels, all output values should equal the inputs, but multiplied by:
-                    // pow((kappa + (accumulatedScale * alpha)), -beta)
-                    // ...where accumulatedScale is the sum of the inputs for adjacent channels for this element squared
-                    // ...where adjacent channels means within half the normSize for the channel
-                    // The test data has only one channel, so this is simplified below.
-                    std::vector<float> outputVector;
-                    for (int n = 0; n < boost::numeric_cast<int>(inputNum); ++n)
-                    {
-                        for (int h = 0; h < boost::numeric_cast<int>(inputHeight); ++h)
-                        {
-                            for (int w = 0; w < boost::numeric_cast<int>(inputWidth); ++w)
-                            {
-                                float accumulatedScale = input[n][0][h][w]*input[n][0][h][w];
-                                float scale = powf((kappa + accumulatedScale * alpha), -beta);
-                                outputVector.push_back(input[n][0][h][w] * scale);
-                            }
-                        }
-                    }
-                    ret.outputExpected = MakeTensor<float, 4>(outputTensorInfo, outputVector);
-                    break;
-                }
-                default:
-                {
-                    throw armnn::UnimplementedException("Unsupported normalisation channel type, "
-                                                        "only Across and Within are supported");
-                }
-            }
-            break;
-        }
-        case armnn::NormalizationAlgorithmMethod::LocalContrast: // NOTE: intentional fallthrough.
-        default:
-        {
-            throw armnn::UnimplementedException("Unsupported normalisation method type, "
-                                                "only LocalBrightness is supported");
-        }
-    }
-
-    return ret;
-}
-
-LayerTestResult<float,4> SimpleNormalizationNhwcTestImpl(armnn::IWorkloadFactory& workloadFactory,
-                                                         armnn::NormalizationAlgorithmChannel normChannel,
-                                                         armnn::NormalizationAlgorithmMethod normMethod)
-{
-    const unsigned int inputHeight = 2;
-    const unsigned int inputWidth = 2;
-    const unsigned int inputChannels = 1;
-    const unsigned int inputNum = 2;
-
-    unsigned int outputHeight = inputHeight;
-    unsigned int outputWidth = inputWidth;
-    unsigned int outputChannels = inputChannels;
-    unsigned int outputNum = inputNum;
-
-    unsigned int inputShape[] = { inputNum, inputHeight, inputWidth, inputChannels };
-    unsigned int outputShape[] = { outputNum, outputHeight, outputWidth, outputChannels };
-
-    auto inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
-    auto outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32);
-
-    LayerTestResult<float,4> ret(outputTensorInfo);
-
-    auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({
-        // Batch #0
-        1.0f, 2.0f,
-        3.0f, 4.0f,
-        // Batch #1
-        5.0f, 6.0f,
-        7.0f, 8.0f
-    }));
-
-    float alpha = 1.f;
-    float beta = 1.f;
-    float kappa = 1.f;
-    uint32_t normSize = 3;
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::NormalizationQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-    data.m_Parameters.m_NormChannelType = normChannel;
-    data.m_Parameters.m_NormMethodType = normMethod;
-    data.m_Parameters.m_NormSize = normSize;
-    data.m_Parameters.m_Alpha = alpha;
-    data.m_Parameters.m_Beta = beta;
-    data.m_Parameters.m_K = kappa;
-    data.m_Parameters.m_DataLayout = armnn::DataLayout::NHWC;
-
-    armnn::PassthroughCpuTensorHandle refHandle(outputTensorInfo, &ret.outputExpected[0][0][0][0]);
-    armnn::NormalizationQueueDescriptor refData = data;
-    armnn::WorkloadInfo refInfo = info;
-    SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, &refHandle);
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateNormalization(data, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-
-    switch (normMethod)
-    {
-        case armnn::NormalizationAlgorithmMethod::LocalBrightness:
-        {
-            switch (normChannel)
-            {
-                case armnn::NormalizationAlgorithmChannel::Across:
-                {
-                    std::vector<float> expectedOutput{ 0.5f, 0.400000006f, 0.300000012f, 0.235294119f,
-                                                       0.192307696f, 0.16216217f, 0.140000001f, 0.123076923f };
-                    ret.outputExpected = MakeTensor<float, 4>(outputTensorInfo, expectedOutput);
-                    break;
-                }
-                default:
-                {
-                    throw armnn::UnimplementedException("Unsupported normalisation channel type, "
-                                                        "Only Cross-map is supported for NHWC layout");
-                }
-            }
-            break;
-        }
-        case armnn::NormalizationAlgorithmMethod::LocalContrast: // NOTE: intentional fallthrough.
-        default:
-        {
-            throw armnn::UnimplementedException("Unsupported normalisation method type, "
-                                                "only LocalBrightness is supported");
-        }
-    }
-
-    return ret;
-}
-
-LayerTestResult<float,4> CompareNormalizationTestImpl(armnn::IWorkloadFactory& workloadFactory,
-                                                      armnn::IWorkloadFactory& refWorkloadFactory,
-                                                      armnn::NormalizationAlgorithmChannel normChannel,
-                                                      armnn::NormalizationAlgorithmMethod normMethod)
-{
-    constexpr unsigned int inputNum = 5;
-    constexpr unsigned int inputChannels = 3;
-    constexpr unsigned int inputHeight = 32;
-    constexpr unsigned int inputWidth = 24;
-
-    constexpr unsigned int outputNum = inputNum;
-    constexpr unsigned int outputChannels = inputChannels;
-    constexpr unsigned int outputHeight = inputHeight;
-    constexpr unsigned int outputWidth = inputWidth;
-
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth};
-    unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth};
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
-    outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32);
-
-    LayerTestResult<float,4> ret(outputTensorInfo);
-
-    auto input = MakeRandomTensor<float, 4>(inputTensorInfo, 111234);
-
-    constexpr float alpha = 1.f;
-    constexpr float beta = 1.f;
-    constexpr float kappa = 1.f;
-    constexpr uint32_t normSize = 5;
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::NormalizationQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-    data.m_Parameters.m_NormChannelType = normChannel;
-    data.m_Parameters.m_NormMethodType  = normMethod;
-    data.m_Parameters.m_NormSize        = normSize;
-    data.m_Parameters.m_Alpha           = alpha;
-    data.m_Parameters.m_Beta            = beta;
-    data.m_Parameters.m_K               = kappa;
-
-    std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo);
-
-    armnn::NormalizationQueueDescriptor refData = data;
-    armnn::WorkloadInfo refInfo = info;
-    SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get());
-    SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());
-
-    // Don't execute if Normalization is not supported for the method and channel types, as an exception will be raised.
-    armnn::BackendId backend = workloadFactory.GetBackendId();
-    const size_t reasonIfUnsupportedMaxLen = 255;
-    char reasonIfUnsupported[reasonIfUnsupportedMaxLen+1];
-    ret.supported = armnn::IsNormalizationSupported(backend, inputTensorInfo, outputTensorInfo, data.m_Parameters,
-                                                    reasonIfUnsupported, reasonIfUnsupportedMaxLen);
-    if (!ret.supported)
-    {
-        return ret;
-    }
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateNormalization(data, info);
-    std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateNormalization(refData, refInfo);
-
-    outputHandleRef->Allocate();
-    inputHandleRef->Allocate();
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-    CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-    refWorkloadFactory.Finalize();
-    workloadRef->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-    CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get());
-
-    return ret;
-}
-
diff --git a/src/backends/test/OptimizedNetworkTests.cpp b/src/backends/test/OptimizedNetworkTests.cpp
deleted file mode 100644
index 72a35f99e0..0000000000
--- a/src/backends/test/OptimizedNetworkTests.cpp
+++ /dev/null
@@ -1,329 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include <armnn/ArmNN.hpp>
-#include <armnn/Graph.hpp>
-#include <armnn/Network.hpp>
-
-#include <backends/reference/RefWorkloadFactory.hpp>
-
-#include <boost/test/unit_test.hpp>
-
-BOOST_AUTO_TEST_SUITE(OptimizedNetwork)
-
-BOOST_AUTO_TEST_CASE(SerializeToDot)
-{
-    armnn::Network net;
-
-    //Defines layers.
-    auto input = net.AddInputLayer(0);
-    auto add = net.AddAdditionLayer();
-    auto output = net.AddOutputLayer(0);
-
-    // Connects layers.
-    input->GetOutputSlot(0).Connect(add->GetInputSlot(0));
-    input->GetOutputSlot(0).Connect(add->GetInputSlot(1));
-    add->GetOutputSlot(0).Connect(output->GetInputSlot(0));
-
-    armnn::TensorShape shape({4});
-    armnn::TensorInfo info(shape, armnn::DataType::Float32);
-    input->GetOutputSlot(0).SetTensorInfo(info);
-    add->GetOutputSlot(0).SetTensorInfo(info);
-
-    armnn::IRuntime::CreationOptions options;
-    armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
-
-    std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
-    armnn::IOptimizedNetworkPtr optimizedNet = armnn::Optimize(net, backends, runtime->GetDeviceSpec());
-
-    std::ostringstream ss;
-    optimizedNet->SerializeToDot(ss);
-
-    auto inputId = input->GetGuid();
-    auto addId = add->GetGuid();
-    auto outputId = output->GetGuid();
-
-    std::stringstream expected;
-    expected <<
-        "digraph Optimized {\n"
-        "    node [shape=\"record\"];\n"
-        "    edge [fontsize=8 fontcolor=\"blue\" fontname=\"arial-bold\"];\n"
-        "    " << inputId << " [label=\"{Input}\"];\n"
-        "    " << addId << " [label=\"{Addition}\"];\n"
-        "    " << outputId << " [label=\"{Output}\"];\n"
-        "    " << inputId << " -> " << addId << " [label=< [4] >];\n"
-        "    " << inputId << " -> " << addId << " [label=< [4] >];\n"
-        "    " << addId << " -> " << outputId << " [label=< [4] >];\n"
-        "}\n";
-
-    BOOST_TEST(ss.str() == expected.str());
-}
-
-BOOST_AUTO_TEST_CASE(OptimizeValidateDeviceNonSupportLayerNoFallback)
-{
-    // build up the structure of the network
-    armnn::INetworkPtr net(armnn::INetwork::Create());
-
-    armnn::IConnectableLayer* input = net->AddInputLayer(0);
-
-    // This layer configuration isn't supported by CpuAcc and isn't allowed to fall back, so Optimize will return null.
-    armnn::NormalizationDescriptor descriptor;
-    armnn::IConnectableLayer* normalize = net->AddNormalizationLayer(descriptor);
-
-    armnn::IConnectableLayer* output = net->AddOutputLayer(0);
-
-    input->GetOutputSlot(0).Connect(normalize->GetInputSlot(0));
-    normalize->GetOutputSlot(0).Connect(output->GetInputSlot(0));
-
-    input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32));
-    normalize->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32));
-
-    armnn::IRuntime::CreationOptions options;
-    armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
-
-    std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
-    armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec());
-    BOOST_CHECK(!optNet);
-}
-
-BOOST_AUTO_TEST_CASE(OptimizeValidateDeviceNonSupportLayerWithFallback)
-{
-    // build up the structure of the network
-    armnn::INetworkPtr net(armnn::INetwork::Create());
-
-    armnn::IConnectableLayer* input = net->AddInputLayer(0);
-
-    // This layer configuration isn't supported by CpuAcc but it allows to fallback to CpuRef.
-    armnn::NormalizationDescriptor descriptor;
-    armnn::IConnectableLayer* normalize = net->AddNormalizationLayer(descriptor);
-
-    armnn::IConnectableLayer* output = net->AddOutputLayer(0);
-
-    input->GetOutputSlot(0).Connect(normalize->GetInputSlot(0));
-    normalize->GetOutputSlot(0).Connect(output->GetInputSlot(0));
-
-    input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32));
-    normalize->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32));
-
-    armnn::IRuntime::CreationOptions options;
-    armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
-
-    std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc, armnn::Compute::CpuRef };
-    armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec());
-    BOOST_REQUIRE(optNet);
-
-    for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph())
-    {
-        // If NEON is enabled, Input and Output layers are supported by CpuAcc,
-        // the other layers are supported by CpuRef.
-        // If NEON is not enabled, all layers are supported by CpuRef.
-#if ARMCOMPUTENEON_ENABLED
-        if (layer->GetType() == armnn::LayerType::Input || layer->GetType() == armnn::LayerType::Output)
-        {
-            BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuAcc);
-        }
-        else if (layer->GetType() == armnn::LayerType::Normalization)
-        {
-            BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef);
-        }
-#else
-        BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef);
-#endif
-    }
-}
-
-BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsUndefinedComputeDevice)
-{
-    const armnn::TensorInfo desc({3, 5}, armnn::DataType::Float32);
-
-    armnn::Network  net;
-
-    armnn::NormalizationDescriptor nmDesc;
-    armnn::ActivationDescriptor acDesc;
-
-    //    in
-    //     |
-    //    nm
-    //   /  |
-    //  ac  |
-    //   \  |
-    //    ml
-    //     |
-    //    sm
-    //     |
-    //    ot
-    armnn::IConnectableLayer* layer = net.AddInputLayer(0, "in");
-    layer->GetOutputSlot(0).SetTensorInfo(desc);
-
-    armnn::IConnectableLayer* const normLayer = net.AddNormalizationLayer(nmDesc, "nm");
-
-    layer->GetOutputSlot(0).Connect(normLayer->GetInputSlot(0));
-    normLayer->GetOutputSlot(0).SetTensorInfo(desc);
-
-    layer = net.AddActivationLayer(acDesc, "ac");
-
-    normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
-    layer->GetOutputSlot(0).SetTensorInfo(desc);
-
-    armnn::IConnectableLayer* prevLayer = layer;
-    layer = net.AddMultiplicationLayer("ml");
-
-    prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
-    normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1));
-    layer->GetOutputSlot(0).SetTensorInfo(desc);
-
-    prevLayer = layer;
-    armnn::SoftmaxDescriptor softmaxDescriptor;
-    layer = net.AddSoftmaxLayer(softmaxDescriptor, "sm");
-
-    prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
-    layer->GetOutputSlot(0).SetTensorInfo(desc);
-
-    prevLayer = layer;
-    layer = net.AddOutputLayer(0, "ot");
-
-    prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
-
-    armnn::IRuntime::CreationOptions options;
-    armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
-
-    std::vector<armnn::BackendId> backends = { armnn::Compute::Undefined };
-
-    armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(net, backends, runtime->GetDeviceSpec());
-    BOOST_CHECK(!optNet);
-
-}
-
-BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsUndefinedComputeDeviceWithFallback)
-{
-    const armnn::TensorInfo desc({3, 5}, armnn::DataType::Float32);
-
-    armnn::Network  net;
-
-    armnn::NormalizationDescriptor nmDesc;
-    armnn::ActivationDescriptor acDesc;
-
-    //    in
-    //     |
-    //    nm
-    //   /  |
-    //  ac  |
-    //   \  |
-    //    ml
-    //     |
-    //    sm
-    //     |
-    //    ot
-    armnn::IConnectableLayer* layer = net.AddInputLayer(0, "in");
-    layer->GetOutputSlot(0).SetTensorInfo(desc);
-
-    armnn::IConnectableLayer* const normLayer = net.AddNormalizationLayer(nmDesc, "nm");
-
-    layer->GetOutputSlot(0).Connect(normLayer->GetInputSlot(0));
-    normLayer->GetOutputSlot(0).SetTensorInfo(desc);
-
-    layer = net.AddActivationLayer(acDesc, "ac");
-
-    normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
-    layer->GetOutputSlot(0).SetTensorInfo(desc);
-
-    armnn::IConnectableLayer* prevLayer = layer;
-    layer = net.AddMultiplicationLayer("ml");
-
-    prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
-    normLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1));
-    layer->GetOutputSlot(0).SetTensorInfo(desc);
-
-    prevLayer = layer;
-    armnn::SoftmaxDescriptor softmaxDescriptor;
-    layer = net.AddSoftmaxLayer(softmaxDescriptor, "sm");
-
-    prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
-    layer->GetOutputSlot(0).SetTensorInfo(desc);
-
-    prevLayer = layer;
-    layer = net.AddOutputLayer(0, "ot");
-
-    prevLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
-
-    armnn::IRuntime::CreationOptions options;
-    armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
-
-    std::vector<armnn::BackendId> backends = { armnn::Compute::Undefined, armnn::Compute::CpuRef };
-
-    armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(net, backends, runtime->GetDeviceSpec());
-    BOOST_CHECK(optNet);
-
-    // validate workloads
-    armnn::RefWorkloadFactory fact;
-    for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph())
-    {
-        BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef);
-        BOOST_CHECK_NO_THROW(
-            layer->CreateWorkload(static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph(), fact));
-    }
-}
-
-BOOST_AUTO_TEST_CASE(OptimizeValidateWorkloadsDuplicateComputeDeviceWithFallback)
-{
-    // build up the structure of the network
-    armnn::INetworkPtr net(armnn::INetwork::Create());
-
-    armnn::IConnectableLayer* input = net->AddInputLayer(0);
-
-    // This layer configuration isn't supported by CpuAcc but it allows to fallback to CpuRef.
-    armnn::NormalizationDescriptor descriptor;
-    armnn::IConnectableLayer* normalize = net->AddNormalizationLayer(descriptor);
-
-    armnn::IConnectableLayer* output = net->AddOutputLayer(0);
-
-    input->GetOutputSlot(0).Connect(normalize->GetInputSlot(0));
-    normalize->GetOutputSlot(0).Connect(output->GetInputSlot(0));
-
-    input->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32));
-    normalize->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo({ 1, 1, 4, 4 }, armnn::DataType::Float32));
-
-    armnn::IRuntime::CreationOptions options;
-    armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
-
-    std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc,
-                                             armnn::Compute::GpuAcc,
-                                             armnn::Compute::CpuRef };
-
-    armnn::IOptimizedNetworkPtr optNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec());
-    BOOST_REQUIRE(optNet);
-
-    for (auto&& layer : static_cast<armnn::OptimizedNetwork*>(optNet.get())->GetGraph())
-    {
-        // If NEON is enabled, Input and Output layers are supported by CpuAcc,
-        // the other layers are supported by CpuRef.
-        // If only CL is enabled, Input and Output layers are supported by GpuAcc,
-        // the other layers are supported by CpuRef.
-        // If neither NEON, nor CL is enabled, all layers are supported by CpuRef.
-#if ARMCOMPUTENEON_ENABLED
-        if (layer->GetType() == armnn::LayerType::Input || layer->GetType() == armnn::LayerType::Output)
-        {
-            BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuAcc);
-        }
-        else if (layer->GetType() == armnn::LayerType::Normalization)
-        {
-            BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef);
-        }
-#elif ARMCOMPUTECL_ENABLED
-        if (layer->GetType() == armnn::LayerType::Input || layer->GetType() == armnn::LayerType::Output)
-        {
-            BOOST_CHECK(layer->GetBackendId() == armnn::Compute::GpuAcc);
-        }
-        else if (layer->GetType() == armnn::LayerType::Normalization)
-        {
-            BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef);
-        }
-#else
-        BOOST_CHECK(layer->GetBackendId() == armnn::Compute::CpuRef);
-#endif
-    }
-}
-
-BOOST_AUTO_TEST_SUITE_END()
diff --git a/src/backends/test/PermuteTestImpl.hpp b/src/backends/test/PermuteTestImpl.hpp
deleted file mode 100644
index 9e5dda491f..0000000000
--- a/src/backends/test/PermuteTestImpl.hpp
+++ /dev/null
@@ -1,224 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#pragma once
-
-#include <armnn/ArmNN.hpp>
-#include <armnn/Tensor.hpp>
-#include <armnn/TypesUtils.hpp>
-
-#include <test/TensorHelpers.hpp>
-#include "QuantizeHelper.hpp"
-
-#include <backends/CpuTensorHandle.hpp>
-#include <backends/WorkloadFactory.hpp>
-
-template<typename T>
-LayerTestResult<T, 4> SimplePermuteTestImpl(
-        armnn::IWorkloadFactory& workloadFactory,
-        armnn::PermuteDescriptor descriptor,
-        armnn::TensorInfo inputTensorInfo,
-        armnn::TensorInfo outputTensorInfo,
-        const std::vector<T>& inputData,
-        const std::vector<T>& outputExpectedData)
-{
-    auto input = MakeTensor<T, 4>(inputTensorInfo, inputData);
-
-    LayerTestResult<T, 4> ret(outputTensorInfo);
-    ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputExpectedData);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::PermuteQueueDescriptor data;
-    data.m_Parameters = descriptor;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePermute(data, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-
-    return ret;
-}
-
-LayerTestResult<float, 4> SimplePermuteFloat32TestCommon(armnn::IWorkloadFactory& workloadFactory)
-{
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int inputShape[] = { 1, 2, 2, 2 };
-    unsigned int outputShape[] = { 1, 2, 2, 2 };
-
-    armnn::PermuteDescriptor descriptor;
-    descriptor.m_DimMappings = {0U, 3U, 1U, 2U};
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
-    outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32);
-
-    std::vector<float> input = std::vector<float>(
-            {
-                    1.0f, 2.0f,
-                    3.0f, 4.0f,
-
-                    5.0f, 6.0f,
-                    7.0f, 8.0f
-            });
-
-    std::vector<float> outputExpected = std::vector<float>(
-            {
-                    1.0f, 5.0f, 2.0f, 6.0f,
-                    3.0f, 7.0f, 4.0f, 8.0f
-            });
-
-    return SimplePermuteTestImpl<float>(workloadFactory, descriptor, inputTensorInfo,
-                                        outputTensorInfo, input, outputExpected);
-}
-
-LayerTestResult<uint8_t, 4> SimplePermuteUint8TestCommon(armnn::IWorkloadFactory& workloadFactory)
-{
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int inputShape[] = { 1, 2, 2, 2 };
-    unsigned int outputShape[] = { 1, 2, 2, 2 };
-
-    armnn::PermuteDescriptor descriptor;
-    descriptor.m_DimMappings = {0U, 3U, 1U, 2U};
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::QuantisedAsymm8);
-    inputTensorInfo.SetQuantizationScale(1.0f);
-    outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::QuantisedAsymm8);
-    outputTensorInfo.SetQuantizationScale(1.0f);
-
-    std::vector<uint8_t> input = std::vector<uint8_t>(
-            {
-                    1, 2,
-                    3, 4,
-
-                    5, 6,
-                    7, 8
-            });
-
-    std::vector<uint8_t> outputExpected = std::vector<uint8_t>(
-            {
-                    1, 5, 2, 6,
-                    3, 7, 4, 8
-            });
-
-    return SimplePermuteTestImpl<uint8_t>(workloadFactory, descriptor, inputTensorInfo,
-                                          outputTensorInfo, input, outputExpected);
-}
-
-LayerTestResult<float, 4>
-PermuteFloat32ValueSet1TestCommon(armnn::IWorkloadFactory& workloadFactory)
-{
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int inputShape[]  = { 1, 2, 2, 3 };
-    unsigned int outputShape[] = { 1, 3, 2, 2 };
-
-    armnn::PermuteDescriptor descriptor;
-    descriptor.m_DimMappings = {0U, 2U, 3U, 1U};
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
-    outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32);
-
-    std::vector<float> input = std::vector<float>(
-            {
-                    1.0f,   2.0f,  3.0f,
-                    11.0f, 12.0f, 13.0f,
-                    21.0f, 22.0f, 23.0f,
-                    31.0f, 32.0f, 33.0f,
-            });
-
-    std::vector<float> outputExpected = std::vector<float>(
-            {
-                    1.0f, 11.0f, 21.0f, 31.0f,
-                    2.0f, 12.0f, 22.0f, 32.0f,
-                    3.0f, 13.0f, 23.0f, 33.0f,
-            });
-
-    return SimplePermuteTestImpl<float>(workloadFactory, descriptor, inputTensorInfo,
-                                        outputTensorInfo, input, outputExpected);
-}
-
-LayerTestResult<float, 4>
-PermuteFloat32ValueSet2TestCommon(armnn::IWorkloadFactory& workloadFactory)
-{
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int inputShape[]  = { 1, 3, 2, 2 };
-    unsigned int outputShape[] = { 1, 2, 2, 3 };
-
-    armnn::PermuteDescriptor descriptor;
-    descriptor.m_DimMappings = {0U, 3U, 1U, 2U};
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
-    outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32);
-
-    std::vector<float> input = std::vector<float>(
-            {
-                1.0f, 11.0f, 21.0f, 31.0f,
-                2.0f, 12.0f, 22.0f, 32.0f,
-                3.0f, 13.0f, 23.0f, 33.0f,
-            });
-
-    std::vector<float> outputExpected = std::vector<float>(
-            {
-                1.0f,   2.0f,  3.0f,
-                11.0f, 12.0f, 13.0f,
-                21.0f, 22.0f, 23.0f,
-                31.0f, 32.0f, 33.0f,
-            });
-
-    return SimplePermuteTestImpl<float>(workloadFactory, descriptor, inputTensorInfo,
-                                        outputTensorInfo, input, outputExpected);
-}
-
-LayerTestResult<float, 4>
-PermuteFloat32ValueSet3TestCommon(armnn::IWorkloadFactory& workloadFactory)
-{
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int inputShape[]  = { 1, 2, 3, 3 };
-    unsigned int outputShape[] = { 1, 3, 2, 3 };
-
-    armnn::PermuteDescriptor descriptor;
-    descriptor.m_DimMappings = {0U, 2U, 3U, 1U};
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
-    outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32);
-
-    std::vector<float> input = std::vector<float>(
-            {
-                1.0f,   2.0f,  3.0f,
-                11.0f, 12.0f, 13.0f,
-                21.0f, 22.0f, 23.0f,
-                31.0f, 32.0f, 33.0f,
-                41.0f, 42.0f, 43.0f,
-                51.0f, 52.0f, 53.0f,
-            });
-
-    std::vector<float> outputExpected = std::vector<float>(
-            {
-                1.0f, 11.0f, 21.0f, 31.0f, 41.0f, 51.0f,
-                2.0f, 12.0f, 22.0f, 32.0f, 42.0f, 52.0f,
-                3.0f, 13.0f, 23.0f, 33.0f, 43.0f, 53.0f,
-            });
-
-    return SimplePermuteTestImpl<float>(workloadFactory, descriptor, inputTensorInfo,
-                                        outputTensorInfo, input, outputExpected);
-}
diff --git a/src/backends/test/Pooling2dTestImpl.hpp b/src/backends/test/Pooling2dTestImpl.hpp
deleted file mode 100644
index eea423275c..0000000000
--- a/src/backends/test/Pooling2dTestImpl.hpp
+++ /dev/null
@@ -1,1236 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#pragma once
-
-#include <string>
-#include <armnn/ArmNN.hpp>
-
-#include <test/TensorHelpers.hpp>
-#include "QuantizeHelper.hpp"
-
-#include <backends/CpuTensorHandle.hpp>
-#include <backends/WorkloadFactory.hpp>
-#include <backends/WorkloadInfo.hpp>
-#include <algorithm>
-#include "Permute.hpp"
-#include <boost/numeric/conversion/cast.hpp>
-
-template<typename T>
-LayerTestResult<T, 4> SimplePooling2dTestImpl(armnn::IWorkloadFactory& workloadFactory,
-                                              armnn::Pooling2dDescriptor descriptor,
-                                              float qScale,
-                                              int32_t qOffset,
-                                              const boost::multi_array<T, 4>& input,
-                                              const boost::multi_array<T, 4>& outputExpected)
-{
-    const armnn::DataLayoutIndexed dataLayout = descriptor.m_DataLayout;
-    auto heightIndex = dataLayout.GetHeightIndex();
-    auto widthIndex = dataLayout.GetWidthIndex();
-    auto channelsIndex = dataLayout.GetChannelsIndex();
-
-    unsigned int inputHeight     = boost::numeric_cast<unsigned int>(input.shape()[heightIndex]);
-    unsigned int inputWidth      = boost::numeric_cast<unsigned int>(input.shape()[widthIndex]);
-    unsigned int inputChannels   = boost::numeric_cast<unsigned int>(input.shape()[channelsIndex]);
-    unsigned int inputBatchSize  = boost::numeric_cast<unsigned int>(input.shape()[0]);
-
-    unsigned int outputHeight    = boost::numeric_cast<unsigned int>(outputExpected.shape()[heightIndex]);
-    unsigned int outputWidth     = boost::numeric_cast<unsigned int>(outputExpected.shape()[widthIndex]);
-    unsigned int outputChannels  = boost::numeric_cast<unsigned int>(outputExpected.shape()[channelsIndex]);
-    unsigned int outputBatchSize = boost::numeric_cast<unsigned int>(outputExpected.shape()[0]);
-
-    armnn::TensorInfo inputTensorInfo = GetTensorInfo<T>(inputBatchSize, inputChannels, inputHeight,
-                                                         inputWidth, dataLayout);
-    armnn::TensorInfo outputTensorInfo = GetTensorInfo<T>(outputBatchSize, outputChannels, outputHeight,
-                                                          outputWidth, dataLayout);
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    LayerTestResult<T, 4> result(outputTensorInfo);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::Pooling2dQueueDescriptor queueDescriptor;
-    queueDescriptor.m_Parameters = descriptor;
-    queueDescriptor.m_Parameters.m_DataLayout = dataLayout;
-
-    armnn::WorkloadInfo workloadInfo;
-    AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get());
-
-    // Don't execute if Pooling is not supported, as an exception will be raised.
-    armnn::BackendId backend = workloadFactory.GetBackendId();
-    const size_t reasonIfUnsupportedMaxLen = 255;
-    char reasonIfUnsupported[reasonIfUnsupportedMaxLen+1];
-    result.supported = armnn::IsPooling2dSupported(backend, inputTensorInfo, outputTensorInfo,
-                                                   queueDescriptor.m_Parameters,
-                                                   reasonIfUnsupported, reasonIfUnsupportedMaxLen);
-    if (!result.supported)
-    {
-        return result;
-    }
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePooling2d(queueDescriptor, workloadInfo);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-
-    result.outputExpected = outputExpected;
-
-    return result;
-}
-
-//
-// Tests max pooling with the following parameters:
-//
-//   Pooling size: 3x3
-//   Stride:       (2,4)
-//   input size:   8x13
-//   channels:     2
-//   batch size:   2
-//
-template<typename T>
-LayerTestResult<T, 4> SimpleMaxPooling2dSize3x3Stride2x4TestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                                   bool forceNoPadding,
-                                                                   float qScale = 1.0f,
-                                                                   int32_t qOffset = 0)
-{
-    armnn::Pooling2dDescriptor descriptor;
-    descriptor.m_PoolType = armnn::PoolingAlgorithm::Max;
-    descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3;
-    descriptor.m_StrideX = 2;
-    descriptor.m_StrideY = 4;
-    // forceNoPadding is mainly used for compatibility with ARM Compute.
-    // As of 16/05/2017, it errors if padX or padY are equal to or greater than the pool size.
-    descriptor.m_PadLeft = descriptor.m_PadRight = forceNoPadding ? 0 : 3;
-    descriptor.m_PadTop = descriptor.m_PadBottom = 0;
-    descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor;
-    descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude;
-
-    unsigned int inputWidth = 8;
-    unsigned int inputHeight = 13;
-    unsigned int outputWidth =
-        (inputWidth + descriptor.m_PadLeft + descriptor.m_PadRight + descriptor.m_StrideX - descriptor.m_PoolWidth) /
-        descriptor.m_StrideX;
-    unsigned int outputHeight =
-        (inputHeight + descriptor.m_PadTop + descriptor.m_PadBottom + descriptor.m_StrideY - descriptor.m_PoolHeight) /
-        descriptor.m_StrideY;
-    unsigned int channels = 2;
-    unsigned int batchSize = 2;
-
-    armnn::TensorInfo inputTensorInfo({ batchSize, channels, inputHeight, inputWidth }, armnn::GetDataType<T>());
-    armnn::TensorInfo outputTensorInfo({ batchSize, channels, outputHeight, outputWidth }, armnn::GetDataType<T>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    std::vector<float> singleChannelData({
-        0.0f, 4.0f, 8.0f, 1.0f, 6.0f, 4.0f, 5.0f, 8.0f,
-        1.0f, 1.0f, 6.0f, 0.0f, 3.0f, 7.0f, 4.0f, 7.0f,
-        8.0f, 5.0f, 0.0f, 0.0f, 8.0f, 3.0f, 4.0f, 3.0f,
-        8.0f, 2.0f, 5.0f, 4.0f, 1.0f, 9.0f, 2.0f, 0.0f,
-        5.0f, 4.0f, 5.0f, 0.0f, 0.0f, 0.0f, 7.0f, 2.0f,
-        1.0f, 2.0f, 6.0f, 2.0f, 7.0f, 9.0f, 5.0f, 2.0f,
-        9.0f, 7.0f, 3.0f, 1.0f, 3.0f, 4.0f, 8.0f, 3.0f,
-        1.0f, 0.0f, 0.0f, 5.0f, 5.0f, 4.0f, 2.0f, 0.0f,
-        6.0f, 4.0f, 3.0f, 6.0f, 9.0f, 5.0f, 5.0f, 6.0f,
-        8.0f, 7.0f, 9.0f, 6.0f, 1.0f, 4.0f, 1.0f, 9.0f,
-        7.0f, 1.0f, 9.0f, 2.0f, 9.0f, 9.0f, 8.0f, 1.0f,
-        4.0f, 4.0f, 5.0f, 9.0f, 2.0f, 6.0f, 6.0f, 4.0f,
-        3.0f, 5.0f, 4.0f, 0.0f, 1.0f, 5.0f, 9.0f, 7.0f,
-    });
-
-    // Constructs input data.
-    std::vector<float> inputData;
-    auto negator = [](float f) { return -f; };
-
-    // First image (two channels where the second channel is the negative of the first one).
-    inputData.insert(inputData.end(), singleChannelData.begin(), singleChannelData.end());
-    std::transform(singleChannelData.begin(), singleChannelData.end(), std::back_inserter(inputData), negator);
-
-    // Second image (same as first image).
-    inputData.insert(inputData.end(), singleChannelData.begin(), singleChannelData.end());
-    std::transform(singleChannelData.begin(), singleChannelData.end(), std::back_inserter(inputData), negator);
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData));
-
-    // These were calculated manually.
-    auto shape(GetTensorShapeAsArray<4>(outputTensorInfo));
-    boost::multi_array<T, 4> outputExpected(shape);
-    if (forceNoPadding)
-    {
-        outputExpected = MakeTensor<T, 4>(outputTensorInfo,
-            QuantizedVector<T>(qScale, qOffset, {
-                 8.0f,  8.0f,  8.0f,
-                 9.0f,  7.0f,  9.0f,
-                 9.0f,  9.0f,  9.0f,
-
-                 0.0f,  0.0f, -3.0f,
-                -1.0f,  0.0f,  0.0f,
-                -1.0f, -1.0f, -1.0f,
-
-                 8.0f,  8.0f,  8.0f,
-                 9.0f,  7.0f,  9.0f,
-                 9.0f,  9.0f,  9.0f,
-
-                 0.0f,  0.0f, -3.0f,
-                -1.0f,  0.0f,  0.0f,
-                -1.0f, -1.0f, -1.0f
-        }));
-    }
-    else
-    {
-        outputExpected = MakeTensor<T, 4>(outputTensorInfo,
-            QuantizedVector<T>(qScale, qOffset, {
-                0.0f, 8.0f, 8.0f, 8.0f, 8.0f, 8.0f,
-                0.0f, 9.0f, 7.0f, 9.0f, 9.0f, 3.0f,
-                0.0f, 8.0f, 9.0f, 9.0f, 9.0f, 9.0f,
-
-                0.0f, 0.0f, 0.0f, 0.0f,-3.0f, 0.0f,
-                0.0f,-1.0f, 0.0f, 0.0f, 0.0f, 0.0f,
-                0.0f,-1.0f,-1.0f,-1.0f,-1.0f, 0.0f,
-
-                0.0f, 8.0f, 8.0f, 8.0f, 8.0f, 8.0f,
-                0.0f, 9.0f, 7.0f, 9.0f, 9.0f, 3.0f,
-                0.0f, 8.0f, 9.0f, 9.0f, 9.0f, 9.0f,
-
-                0.0f, 0.0f, 0.0f, 0.0f,-3.0f, 0.0f,
-                0.0f,-1.0f, 0.0f, 0.0f, 0.0f, 0.0f,
-                0.0f,-1.0f,-1.0f,-1.0f,-1.0f, 0.0f
-        }));
-    }
-
-    return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected);
-}
-
-template<typename T>
-LayerTestResult<T, 4> SimpleMaxPooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                   const armnn::DataLayoutIndexed& dataLayout = armnn::DataLayout::NCHW,
-                                                   float qScale = 1.0f,
-                                                   int32_t qOffset = 0)
-{
-    armnn::Pooling2dDescriptor descriptor;
-    descriptor.m_PoolType = armnn::PoolingAlgorithm::Max;
-    descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2;
-    descriptor.m_StrideX = descriptor.m_StrideY = 2;
-    descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude;
-    descriptor.m_DataLayout = dataLayout;
-
-    armnn::TensorInfo inputTensorInfo  = GetTensorInfo<T>(1, 2, 4, 4, dataLayout);
-    armnn::TensorInfo outputTensorInfo = GetTensorInfo<T>(1, 2, 2, 2, dataLayout);
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    std::vector<T> inputData(
-        QuantizedVector<T>(qScale, qOffset, {
-             1.0f,  2.0f,  5.0f,  6.0f,
-             3.0f,  4.0f,  7.0f,  8.0f,
-             9.0f, 10.0f, 13.0f, 14.0f,
-            11.0f, 12.0f, 15.0f, 16.0f,
-
-            17.0f, 18.0f, 21.0f, 22.0f,
-            19.0f, 20.0f, 23.0f, 24.0f,
-            25.0f, 26.0f, 29.0f, 30.0f,
-            27.0f, 28.0f, 31.0f, 32.0f,
-        }));
-
-    std::vector<T> outputData(
-        QuantizedVector<T>(qScale, qOffset, {
-             4.0f,  8.0f,
-            12.0f, 16.0f,
-
-            20.0f, 24.0f,
-            28.0f, 32.0f,
-        }));
-
-    const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
-    if (dataLayout.GetDataLayout() == armnn::DataLayout::NHWC)
-    {
-        std::vector<T> tmp(inputData.size());
-        armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data());
-        inputData = tmp;
-
-        std::vector<T> tmp1(outputData.size());
-        armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data());
-        outputData = tmp1;
-    }
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo, inputData);
-
-    auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData);
-
-    return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected);
-}
-
-template<typename T>
-LayerTestResult<T, 4> SimpleAveragePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                       armnn::DataLayoutIndexed dataLayout = armnn::DataLayout::NCHW,
-                                                       float qScale = 1.0f,
-                                                       int32_t qOffset = 0)
-{
-    armnn::Pooling2dDescriptor descriptor;
-    descriptor.m_PoolType = armnn::PoolingAlgorithm::Average;
-    descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2;
-    descriptor.m_StrideX = descriptor.m_StrideY = 2;
-    descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude;
-    descriptor.m_DataLayout = dataLayout;
-
-    armnn::TensorInfo inputTensorInfo  = GetTensorInfo<T>(1, 2, 4, 4, dataLayout);
-    armnn::TensorInfo outputTensorInfo = GetTensorInfo<T>(1, 2, 2, 2, dataLayout);
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    std::vector<T> inputData(
-        QuantizedVector<T>(qScale, qOffset, {
-             2.0f,  2.0f,  6.0f,  6.0f,
-             4.0f,  4.0f,  8.0f,  8.0f,
-            10.0f, 12.0f, 14.0f, 16.0f,
-            10.0f, 12.0f, 16.0f, 14.0f,
-
-            18.0f, 20.0f, 24.0f, 22.0f,
-            20.0f, 18.0f, 22.0f, 24.0f,
-            26.0f, 28.0f,  0.0f,  0.0f,
-            26.0f, 28.0f,  0.0f,  0.0f,
-        }));
-
-    std::vector<T> outputData(
-        QuantizedVector<T>(qScale, qOffset, {
-             3.0f,  7.0f,
-            11.0f, 15.0f,
-
-            19.0f, 23.0f,
-            27.0f,  0.0f,
-        }));
-
-    const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
-    if (dataLayout.GetDataLayout() == armnn::DataLayout::NHWC)
-    {
-        std::vector<T> tmp(inputData.size());
-        armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data());
-        inputData = tmp;
-
-        std::vector<T> tmp1(outputData.size());
-        armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data());
-        outputData = tmp1;
-    }
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo, inputData);
-
-    auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData);
-
-    return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected);
-}
-
-template<typename T>
-LayerTestResult<T, 4> LargeTensorsAveragePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                             float qScale = 1.0f,
-                                                             int32_t qOffset = 0)
-{
-    armnn::Pooling2dDescriptor descriptor;
-    descriptor.m_PoolType = armnn::PoolingAlgorithm::Average;
-    descriptor.m_PoolWidth = descriptor.m_PoolHeight = 100;
-    descriptor.m_StrideX = descriptor.m_StrideY = 5;
-    descriptor.m_PadLeft = 50;
-    descriptor.m_PadRight = 50;
-    descriptor.m_PadTop = 50;
-    descriptor.m_PadBottom = 50;
-    descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude;
-
-    armnn::TensorInfo inputTensorInfo({ 5, 3, 52, 60 }, armnn::GetDataType<T>());
-    armnn::TensorInfo outputTensorInfo({ 5, 3, 11, 13 }, armnn::GetDataType<T>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    std::vector<T> inputVec;
-
-    for (unsigned int i = 0 ; i < inputTensorInfo.GetShape().GetNumElements(); ++i)
-    {
-        inputVec.push_back(1);
-    }
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo, inputVec);
-
-    std::vector<T> outputVec;
-
-    for (unsigned int i = 0 ; i < outputTensorInfo.GetShape().GetNumElements(); ++i)
-    {
-        outputVec.push_back(1);
-    }
-
-    auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputVec);
-
-    return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected);
-}
-
-template<typename T>
-LayerTestResult<T, 4> SimpleL2Pooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                  armnn::DataLayoutIndexed dataLayout = armnn::DataLayout::NCHW,
-                                                  float qScale = 1.0f,
-                                                  int32_t qOffset = 0)
-{
-    armnn::Pooling2dDescriptor descriptor;
-    descriptor.m_PoolType = armnn::PoolingAlgorithm::L2;
-    descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2;
-    descriptor.m_StrideX = descriptor.m_StrideY = 2;
-    descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude;
-    descriptor.m_DataLayout = dataLayout;
-
-    armnn::TensorInfo inputTensorInfo  = GetTensorInfo<T>(1, 2, 4, 4, dataLayout);
-    armnn::TensorInfo outputTensorInfo = GetTensorInfo<T>(1, 2, 2, 2, dataLayout);
-
-    std::vector<T> inputData(
-        QuantizedVector<T>(qScale, qOffset, {
-            1.0f, 7.0f, 5.0f, 5.0f,
-            1.0f, 7.0f, 5.0f, 5.0f,
-            3.0f, 3.0f, 1.0f, 1.0f,
-            3.0f, 3.0f, 1.0f, 1.0f,
-
-            1.0f, 7.0f, 0.0f, 0.0f,
-            1.0f, 7.0f, 2.0f, 0.0f,
-            0.0f, 2.0f, 1.0f, 1.0f,
-            0.0f, 0.0f, 1.0f, 1.0f,
-        }));
-
-    std::vector<T> outputData(
-        QuantizedVector<T>(qScale, qOffset, {
-            5.0f, 5.0f,
-            3.0f, 1.0f,
-
-            5.0f, 1.0f,
-            1.0f, 1.0f,
-        }));
-
-    const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
-    if (dataLayout.GetDataLayout() == armnn::DataLayout::NHWC)
-    {
-        std::vector<T> tmp(inputData.size());
-        armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data());
-        inputData = tmp;
-
-        std::vector<T> tmp1(outputData.size());
-        armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data());
-        outputData = tmp1;
-    }
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo, inputData);
-
-    auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData);
-
-    return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected);
-}
-
-template<typename T>
-LayerTestResult<T, 4> L2Pooling2dSize3Stride1TestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                        float qScale = 1.0f,
-                                                        int32_t qOffset = 0)
-{
-    armnn::Pooling2dDescriptor descriptor;
-    descriptor.m_PoolType = armnn::PoolingAlgorithm::L2;
-    descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3;
-    descriptor.m_StrideX = descriptor.m_StrideY = 1;
-    descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude;
-
-    armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>());
-    auto input = MakeTensor<T, 4>(inputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-            2.0f, 1.0f, 5.0f, 2.0f,
-            1.0f, 2.0f, 2.0f, 1.0f,
-            5.0f, 4.0f, 1.0f, 5.0f,
-            2.0f, 1.0f, 5.0f, 2.0f,
-        }));
-
-    armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType<T>());
-    auto outputExpected = MakeTensor<T, 4>(outputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-            3.0f, 3.0f,
-            3.0f, 3.0f,
-        }));
-
-    return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected);
-}
-
-template<typename T>
-LayerTestResult<T, 4> L2Pooling2dSize3Stride3TestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                        float qScale = 1.0f,
-                                                        int32_t qOffset = 0)
-{
-    armnn::Pooling2dDescriptor descriptor;
-    descriptor.m_PoolType = armnn::PoolingAlgorithm::L2;
-    descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3;
-    descriptor.m_StrideX = descriptor.m_StrideY = 3;
-    descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude;
-
-    armnn::TensorInfo inputTensorInfo({ 1, 1, 9, 9 }, armnn::GetDataType<T>());
-    auto input = MakeTensor<T, 4>(inputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-            2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f,
-            1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f,
-            5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f,
-            2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f,
-            1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f,
-            5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f,
-            2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f,
-            1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f,
-            5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f,
-        }));
-
-    armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType<T>());
-    auto outputExpected = MakeTensor<T, 4>(outputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-            3.0f, 3.0f, 3.0f,
-            3.0f, 3.0f, 3.0f,
-            3.0f, 3.0f, 3.0f,
-        }));
-
-    return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected);
-}
-
-template<typename T>
-LayerTestResult<T, 4> L2Pooling2dSize3Stride4TestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                        float qScale = 1.0f,
-                                                        int32_t qOffset = 0)
-{
-    armnn::Pooling2dDescriptor descriptor;
-    descriptor.m_PoolType = armnn::PoolingAlgorithm::L2;
-    descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3;
-    descriptor.m_StrideX = descriptor.m_StrideY = 4;
-    descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude;
-
-    armnn::TensorInfo inputTensorInfo({ 1, 1, 7, 7 }, armnn::GetDataType<T>());
-    auto input = MakeTensor<T, 4>(inputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-            2.0f, 1.0f, 5.0f, 0.0f, 2.0f, 1.0f, 5.0f,
-            1.0f, 2.0f, 2.0f, 0.0f, 1.0f, 2.0f, 2.0f,
-            5.0f, 4.0f, 1.0f, 0.0f, 5.0f, 4.0f, 1.0f,
-            0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
-            2.0f, 1.0f, 5.0f, 0.0f, 2.0f, 1.0f, 5.0f,
-            1.0f, 2.0f, 2.0f, 0.0f, 1.0f, 2.0f, 2.0f,
-            5.0f, 4.0f, 1.0f, 0.0f, 5.0f, 4.0f, 1.0f,
-        }));
-
-    armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType<T>());
-    auto outputExpected = MakeTensor<T, 4>(outputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-            3.0f, 3.0f,
-            3.0f, 3.0f,
-        }));
-
-    return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected);
-}
-
-template<typename T>
-LayerTestResult<T, 4> L2Pooling2dSize7TestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                 float qScale = 1.0f,
-                                                 int32_t qOffset = 0)
-{
-    armnn::Pooling2dDescriptor descriptor;
-    descriptor.m_PoolType = armnn::PoolingAlgorithm::L2;
-    descriptor.m_PoolWidth = descriptor.m_PoolHeight = 7;
-    descriptor.m_StrideX = descriptor.m_StrideY = 7;
-    descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude;
-
-    armnn::TensorInfo inputTensorInfo({ 1, 1, 7, 7 }, armnn::GetDataType<T>());
-    auto input = MakeTensor<T, 4>(inputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-            1.0f, 0.0f, 2.0f, 0.0f,  3.0f, 0.0f, 4.0f,
-            0.0f, 0.0f, 0.0f, 0.0f,  0.0f, 0.0f, 0.0f,
-            0.0f, 5.0f, 0.0f, 6.0f,  0.0f, 7.0f, 0.0f,
-            8.0f, 0.0f, 9.0f, 0.0f, 10.0f, 0.0f, 5.0f,
-            0.0f, 5.0f, 0.0f, 2.0f,  0.0f, 1.0f, 1.0f,
-            0.0f, 0.0f, 0.0f, 0.0f,  0.0f, 0.0f, 0.0f,
-            0.0f, 0.0f, 0.0f, 0.0f,  0.0f, 0.0f, 0.0f,
-        }));
-
-    armnn::TensorInfo outputTensorInfo({ 1, 1, 1, 1 }, armnn::GetDataType<T>());
-    auto outputExpected = MakeTensor<T, 4>(outputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-            3.0f,
-        }));
-
-    return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected);
-}
-
-template<typename T>
-LayerTestResult<T, 4> L2Pooling2dSize9TestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                 float qScale = 1.0f,
-                                                 int32_t qOffset = 0)
-{
-    armnn::Pooling2dDescriptor descriptor;
-    descriptor.m_PoolType = armnn::PoolingAlgorithm::L2;
-    descriptor.m_PoolWidth = descriptor.m_PoolHeight = 9;
-    descriptor.m_StrideX = descriptor.m_StrideY = 9;
-    descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude;
-
-    armnn::TensorInfo inputTensorInfo({ 1, 1, 9, 9 }, armnn::GetDataType<T>());
-    auto input = MakeTensor<T, 4>(inputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-            2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f,
-            1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f,
-            5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f,
-            2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f,
-            1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f,
-            5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f,
-            2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f,
-            1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f,
-            5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f,
-        }));
-
-    armnn::TensorInfo outputTensorInfo({ 1, 1, 1, 1 }, armnn::GetDataType<T>());
-    auto outputExpected = MakeTensor<T, 4>(outputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-            3.0f,
-        }));
-
-    return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected);
-}
-
-template<typename T>
-LayerTestResult<T, 4> AsymmetricNonSquarePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                             float qScale = 1.0f,
-                                                             int32_t qOffset = 0)
-{
-    armnn::TensorInfo inputTensorInfo({ 1, 1, 1, 3 }, armnn::GetDataType<T>());
-    armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType<T>());
-
-    armnn::Pooling2dDescriptor descriptor;
-    descriptor.m_PoolType = armnn::PoolingAlgorithm::Max;
-    descriptor.m_PoolWidth = 2;
-    descriptor.m_PoolHeight = 3;
-    descriptor.m_StrideX = 2;
-    descriptor.m_StrideY = 1;
-    descriptor.m_PadLeft = 2;
-    descriptor.m_PadRight = 0;
-    descriptor.m_PadTop = 1;
-    descriptor.m_PadBottom = 2;
-    descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor;
-    descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude;
-
-    // Construct input data.
-    auto input = MakeTensor<T, 4>(inputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-            1.0f, 3.0f, 4.0f,
-        }));
-
-    // These were calculated manually.
-    auto outputExpected = MakeTensor<T, 4>(outputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-            0.0f, 3.0f, 0.0f, 3.0f,
-        }));
-
-    return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected);
-}
-
-template<typename T>
-LayerTestResult<T, 4> ComparePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                 armnn::IWorkloadFactory& refWorkloadFactory,
-                                                 armnn::PoolingAlgorithm poolingType,
-                                                 float qScale = 1.0f,
-                                                 int32_t qOffset = 0)
-{
-    const unsigned int inputWidth = 16;
-    const unsigned int inputHeight = 32;
-    const unsigned int channelCount = 2;
-    const unsigned int batchSize = 5;
-
-    const unsigned int poolSize = 3;
-    const unsigned int strideX = 2;
-    const unsigned int strideY = 4;
-    const unsigned int padX = 0;
-    const unsigned int padY = 0;
-
-    const unsigned int outputWidth = (inputWidth + 2 * padX + strideX - poolSize) / strideX;
-    const unsigned int outputHeight = (inputHeight + 2 * padY + strideY - poolSize) / strideY;
-
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int inputShape[] = { batchSize, channelCount, inputHeight, inputWidth };
-    unsigned int outputShape[] = { batchSize, channelCount, outputHeight, outputWidth };
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::GetDataType<T>());
-    outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::GetDataType<T>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    boost::multi_array<T, 4> input = MakeRandomTensor<T, 4>(inputTensorInfo, 81715);
-
-    LayerTestResult<T, 4> comparisonResult(outputTensorInfo);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::Pooling2dQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-    data.m_Parameters.m_PoolType = poolingType;
-    data.m_Parameters.m_PoolWidth = poolSize;
-    data.m_Parameters.m_PoolHeight = poolSize;
-    data.m_Parameters.m_StrideX = strideX;
-    data.m_Parameters.m_StrideY = strideY;
-    data.m_Parameters.m_PadLeft = padX;
-    data.m_Parameters.m_PadRight = padX;
-    data.m_Parameters.m_PadTop = padY;
-    data.m_Parameters.m_PadBottom = padY;
-    data.m_Parameters.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor;
-
-    std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo);
-
-    // Don't execute if Pooling is not supported, as an exception will be raised.
-    armnn::BackendId backend = workloadFactory.GetBackendId();
-    const size_t reasonIfUnsupportedMaxLen = 255;
-    char reasonIfUnsupported[reasonIfUnsupportedMaxLen+1];
-    comparisonResult.supported = armnn::IsPooling2dSupported(backend, inputTensorInfo, outputTensorInfo,
-                                                             data.m_Parameters,
-                                                             reasonIfUnsupported, reasonIfUnsupportedMaxLen);
-    if (!comparisonResult.supported)
-    {
-        return comparisonResult;
-    }
-
-    armnn::Pooling2dQueueDescriptor refData = data;
-    armnn::WorkloadInfo refInfo = info;
-    SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get());
-    SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePooling2d(data, info);
-    std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreatePooling2d(refData, refInfo);
-
-    outputHandleRef->Allocate();
-    inputHandleRef->Allocate();
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-    CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]);
-
-    workload->Execute();
-    workloadRef->Execute();
-
-    CopyDataFromITensorHandle(&comparisonResult.output[0][0][0][0], outputHandle.get());
-    CopyDataFromITensorHandle(&comparisonResult.outputExpected[0][0][0][0], outputHandleRef.get());
-
-    return comparisonResult;
-}
-
-//
-// Tests max pooling with the following parameters:
-//
-//   Pooling size: 2x2
-//   Stride:       (2,2)
-//   input size:   4x4
-//   channels:     1
-//   batch size:   1
-//
-template<typename T>
-LayerTestResult<T, 4> SimpleMaxPooling2dSize2x2Stride2x2TestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                                   bool forceNoPadding,
-                                                                   float qScale = 1.0f,
-                                                                   int32_t qOffset = 0)
-{
-    armnn::Pooling2dDescriptor descriptor;
-    descriptor.m_PoolType = armnn::PoolingAlgorithm::Max;
-    descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2;
-    descriptor.m_StrideX = 2;
-    descriptor.m_StrideY = 2;
-    descriptor.m_PadLeft = descriptor.m_PadRight = forceNoPadding ? 0 : 3;
-    descriptor.m_PadTop = descriptor.m_PadBottom = 0;
-    descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor;
-    descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude;
-
-    unsigned int inputWidth = 4;
-    unsigned int inputHeight = 4;
-    unsigned int outputWidth =
-        (inputWidth + descriptor.m_PadLeft + descriptor.m_PadRight + descriptor.m_StrideX - descriptor.m_PoolWidth) /
-        descriptor.m_StrideX;
-    unsigned int outputHeight =
-        (inputHeight + descriptor.m_PadTop + descriptor.m_PadBottom + descriptor.m_StrideY - descriptor.m_PoolHeight) /
-        descriptor.m_StrideY;
-    unsigned int channels = 1;
-    unsigned int batchSize = 1;
-
-    std::vector<float> inputData = {
-        510.0f, 222.0f, 780.0f, 654.0f,
-        141.0f, 276.0f,  15.0f, 546.0f,
-        303.0f, 618.0f, 582.0f, 339.0f,
-        438.0f, 564.0f, 573.0f, 402.0f
-    };
-
-    // Note that left and right edges will be 0.f, due to the 2x2 max pooling only accessing zeros here.
-    std::vector<float> expectedOutputDataWithPadding = {
-        0.0f, 510.0f, 780.0f, 654.0f, 0.0f,
-        0.0f, 438.0f, 618.0f, 402.0f, 0.0f
-    };
-
-    std::vector<float> expectedOutputDataNoPadding = {
-        510.0f, 780.0f,
-        618.0f, 582.0f
-    };
-
-    armnn::TensorInfo inputTensorInfo({ batchSize, channels, inputHeight, inputWidth }, armnn::GetDataType<T>());
-
-    // Scale and offset should match input - we're just calculating maximum values.
-    armnn::TensorInfo outputTensorInfo({ batchSize, channels, outputHeight, outputWidth }, armnn::GetDataType<T>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData));
-
-    auto outputExpected = MakeTensor<T, 4>(outputTensorInfo,
-        forceNoPadding ? QuantizedVector<T>(qScale, qOffset, expectedOutputDataNoPadding) :
-                         QuantizedVector<T>(qScale, qOffset, expectedOutputDataWithPadding));
-
-    return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected);
-}
-
-//
-// Tests max pooling with the following parameters:
-//
-//   Pooling size: 3x2
-//   Stride:       (2,2)
-//   input size:   3x2
-//   channels:     1
-//   batch size:   1
-//
-template<typename T>
-LayerTestResult<T, 4> IgnorePaddingAveragePooling2dSize3x2Stride2x2TestCommon(
-        armnn::IWorkloadFactory& workloadFactory,
-        bool forceNoPadding,
-        float qScale = 1.0f,
-        int32_t qOffset = 0)
-{
-    armnn::Pooling2dDescriptor descriptor;
-    descriptor.m_PoolType = armnn::PoolingAlgorithm::Average;
-    descriptor.m_PoolWidth = 3;
-    descriptor.m_PoolHeight = 2;
-    descriptor.m_StrideX = 2;
-    descriptor.m_StrideY = 2;
-    descriptor.m_PadLeft = (forceNoPadding) ? 0 : 1;
-    descriptor.m_PadRight = descriptor.m_PadLeft;
-    descriptor.m_PadTop = 0;
-    descriptor.m_PadBottom = 0;
-    descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor;
-    descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue;
-
-    unsigned int inputWidth = 3;
-    unsigned int inputHeight = 2;
-    unsigned int outputWidth =
-        (inputWidth + descriptor.m_PadLeft + descriptor.m_PadRight + descriptor.m_StrideX - descriptor.m_PoolWidth) /
-        descriptor.m_StrideX;
-    unsigned int outputHeight =
-        (inputHeight + descriptor.m_PadTop + descriptor.m_PadBottom + descriptor.m_StrideY - descriptor.m_PoolHeight) /
-        descriptor.m_StrideY;
-    unsigned int channels = 1;
-    unsigned int batchSize = 1;
-
-    std::vector<float> inputData = {
-        3.0f, 6.0f, 9.0f,
-        12.0f, 15.0f, 18.0f,
-    };
-
-    std::vector<float> expectedOutputDataWithPadding = {
-        6.0f, 8.0f,
-    };
-
-    std::vector<float> expectedOutputDataNoPadding = {
-        10.5f,
-    };
-
-    armnn::TensorInfo inputTensorInfo({ batchSize, channels, inputHeight, inputWidth }, armnn::GetDataType<T>());
-
-    // Scale and offset should match input - we're just calculating average values.
-    armnn::TensorInfo outputTensorInfo({ batchSize, channels, outputHeight, outputWidth }, armnn::GetDataType<T>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData));
-
-    auto outputExpected = MakeTensor<T, 4>(outputTensorInfo,
-        forceNoPadding ? QuantizedVector<T>(qScale, qOffset, expectedOutputDataNoPadding) :
-                         QuantizedVector<T>(qScale, qOffset, expectedOutputDataWithPadding));
-
-    return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected);
-}
-
-
-template<typename T>
-LayerTestResult<T, 4> IgnorePaddingSimpleMaxPooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                            float qScale = 1.0f,
-                                                            int32_t qOffset = 0)
-{
-    armnn::Pooling2dDescriptor descriptor;
-    descriptor.m_PoolType = armnn::PoolingAlgorithm::Max;
-    descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2;
-    descriptor.m_StrideX = descriptor.m_StrideY = 2;
-    descriptor.m_PadLeft = 1;
-    descriptor.m_PadRight = 1;
-    descriptor.m_PadTop = 1;
-    descriptor.m_PadBottom = 1;
-    descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue;
-
-    armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>());
-    armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType<T>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-            -1.0f, -2.0f,  3.0f,  4.0f,
-            -1.0f, -2.0f,  3.0f,  4.0f,
-             1.0f,  2.0f, -3.0f, -4.0f,
-             1.0f,  2.0f, -3.0f, -4.0f,
-        }));
-
-    auto outputExpected = MakeTensor<T, 4>(outputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-            -1.0f,  3.0f,  4.0f,
-             1.0f,  3.0f,  4.0f,
-             1.0f,  2.0f, -4.0f,
-        }));
-
-    return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected);
-}
-
-template<typename T>
-LayerTestResult<T, 4> IgnorePaddingMaxPooling2dSize3TestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                            float qScale = 1.0f,
-                                                            int32_t qOffset = 0)
-{
-    armnn::Pooling2dDescriptor descriptor;
-    descriptor.m_PoolType = armnn::PoolingAlgorithm::Max;
-    descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3;
-    descriptor.m_StrideX = descriptor.m_StrideY = 1;
-    descriptor.m_PadLeft = 1;
-    descriptor.m_PadRight = 1;
-    descriptor.m_PadTop = 1;
-    descriptor.m_PadBottom = 1;
-    descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue;
-
-    armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>());
-    armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-            -1.0f, -2.0f,  3.0f,  4.0f,
-            -1.0f, -2.0f,  3.0f,  4.0f,
-             1.0f,  2.0f, -3.0f, -4.0f,
-             1.0f,  2.0f, -3.0f, -4.0f,
-        }));
-
-    auto outputExpected = MakeTensor<T, 4>(outputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-            -1.0f,  3.0f,  4.0f,  4.0f,
-             2.0f,  3.0f,  4.0f,  4.0f,
-             2.0f,  3.0f,  4.0f,  4.0f,
-             2.0f,  2.0f,  2.0f, -3.0f,
-        }));
-
-    return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected);
-}
-
-template<typename T>
-LayerTestResult<T, 4> IgnorePaddingSimpleAveragePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                                 float qScale = 1.0f,
-                                                                 int32_t qOffset = 0)
-{
-    armnn::Pooling2dDescriptor descriptor;
-    descriptor.m_PoolType = armnn::PoolingAlgorithm::Average;
-    descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2;
-    descriptor.m_StrideX = descriptor.m_StrideY = 2;
-    descriptor.m_PadLeft = 1;
-    descriptor.m_PadRight = 1;
-    descriptor.m_PadTop = 1;
-    descriptor.m_PadBottom = 1;
-    descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue;
-
-    armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>());
-    armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType<T>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-            12.0f, 20.0f, 32.0f, 40.0f,
-            12.0f, 20.0f, 32.0f, 40.0f,
-            12.0f, 20.0f, 32.0f, 40.0f,
-            12.0f, 20.0f, 32.0f, 40.0f,
-        }));
-
-    auto outputExpected = MakeTensor<T, 4>(outputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-            3.0f,  13.0f,  10.0f,
-            6.0f,  26.0f,  20.0f,
-            3.0f,  13.0f,  10.0f,
-        }));
-
-    return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected);
-}
-
-template<typename T>
-LayerTestResult<T, 4> IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                                 float qScale = 1.0f,
-                                                                 int32_t qOffset = 0)
-{
-    armnn::Pooling2dDescriptor descriptor;
-    descriptor.m_PoolType = armnn::PoolingAlgorithm::Average;
-    descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3;
-    descriptor.m_StrideX = descriptor.m_StrideY = 2;
-    descriptor.m_PadLeft = 0;
-    descriptor.m_PadRight = 0;
-    descriptor.m_PadTop = 0;
-    descriptor.m_PadBottom = 0;
-    descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue;
-    descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Ceiling;
-
-    armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4}, armnn::GetDataType<T>());
-    armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType<T>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-            1.0f, 2.0f, 3.0f, 4.0f,
-            1.0f, 2.0f, 3.0f, 4.0f,
-            1.0f, 2.0f, 3.0f, 4.0f,
-            1.0f, 2.0f, 3.0f, 4.0f,
-        }));
-
-    auto outputExpected = MakeTensor<T, 4>(outputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-            2.0f, 3.5f,
-            2.0f, 3.5f
-        }));
-
-    return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected);
-}
-
-template<typename T>
-LayerTestResult<T, 4> IgnorePaddingAveragePooling2dSize3TestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                                float qScale = 1.0f,
-                                                                int32_t qOffset = 0)
-{
-    armnn::Pooling2dDescriptor descriptor;
-    descriptor.m_PoolType = armnn::PoolingAlgorithm::Average;
-    descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3;
-    descriptor.m_StrideX = descriptor.m_StrideY = 1;
-    descriptor.m_PadLeft = 1;
-    descriptor.m_PadRight = 1;
-    descriptor.m_PadTop = 1;
-    descriptor.m_PadBottom = 1;
-    descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue;
-
-    armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>());
-    armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-            9.0f,   27.0f,  18.0f,  36.0f,
-            18.0f,   9.0f,  18.0f,   9.0f,
-            27.0f,  18.0f,   9.0f,  27.0f,
-            9.0f,   27.0f,   9.0f,  18.0f,
-        }));
-
-    auto outputExpected = MakeTensor<T, 4>(outputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-             7.0f,  11.0f,  13.0f, 9.0f,
-            12.0f,  17.0f,  19.0f, 13.0f,
-            12.0f,  16.0f,  16.0f, 10.0f,
-             9.0f,  11.0f,  12.0f, 7.0f,
-        }));
-
-    return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected);
-}
-
-template<typename T>
-LayerTestResult<T, 4> IgnorePaddingSimpleL2Pooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                            float qScale = 1.0f,
-                                                            int32_t qOffset = 0)
-{
-    armnn::Pooling2dDescriptor descriptor;
-    descriptor.m_PoolType = armnn::PoolingAlgorithm::L2;
-    descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2;
-    descriptor.m_StrideX = descriptor.m_StrideY = 2;
-    descriptor.m_PadLeft = 1;
-    descriptor.m_PadRight = 1;
-    descriptor.m_PadTop = 1;
-    descriptor.m_PadBottom = 1;
-    descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue;
-
-    armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>());
-    armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType<T>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-            2.0f,  4.0f, 8.0f, 16.0f,
-            4.0f,  2.0f, 2.0f, 4.0f,
-            8.0f,  2.0f, 4.0f, 2.0f,
-            16.0f, 2.0f, 2.0f, 8.0f,
-        }));
-
-    auto outputExpected = MakeTensor<T, 4>(outputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-               1.0f,     4.4721f,   8.0f,
-            4.4721f,     2.6457f,   2.236f,
-               8.0f,     1.4142f,   4.0f,
-        }));
-
-    return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected);
-}
-
-template<typename T>
-LayerTestResult<T, 4> IgnorePaddingL2Pooling2dSize3TestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                           float qScale = 1.0f,
-                                                           int32_t qOffset = 0)
-{
-    armnn::Pooling2dDescriptor descriptor;
-    descriptor.m_PoolType = armnn::PoolingAlgorithm::L2;
-    descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3;
-    descriptor.m_StrideX = descriptor.m_StrideY = 1;
-    descriptor.m_PadLeft = 1;
-    descriptor.m_PadRight = 1;
-    descriptor.m_PadTop = 1;
-    descriptor.m_PadBottom = 1;
-    descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue;
-
-    armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>());
-    armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-            1.0f, 2.0f, 3.0f, 4.0f,
-            1.0f, 2.0f, 3.0f, 4.0f,
-            1.0f, 2.0f, 3.0f, 4.0f,
-            1.0f, 2.0f, 3.0f, 4.0f,
-        }));
-
-    auto outputExpected = MakeTensor<T, 4>(outputTensorInfo,
-        QuantizedVector<T>(qScale, qOffset, {
-            1.0540f, 1.7638f, 2.5385f, 2.3570f,
-            1.2909f, 2.1602f, 3.1091f, 2.8867f,
-            1.2909f, 2.1602f, 3.1091f, 2.8867f,
-            1.0540f, 1.7638f, 2.5385f, 2.3570f,
-        }));
-
-    return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected);
-}
diff --git a/src/backends/test/QuantizeHelper.hpp b/src/backends/test/QuantizeHelper.hpp
deleted file mode 100644
index bb4e561d59..0000000000
--- a/src/backends/test/QuantizeHelper.hpp
+++ /dev/null
@@ -1,91 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#pragma once
-
-#include <armnn/ArmNN.hpp>
-#include <armnn/TypesUtils.hpp>
-
-#include <initializer_list>
-#include <iterator>
-#include <vector>
-#include <boost/core/ignore_unused.hpp>
-
-template<typename T, bool DoQuantize=true>
-struct SelectiveQuantizer
-{
-    static T Quantize(float value, float scale, int32_t offset)
-    {
-        return armnn::Quantize<T>(value, scale, offset);
-    }
-
-    static float Dequantize(T value, float scale, int32_t offset)
-    {
-        return armnn::Dequantize(value, scale, offset);
-    }
-};
-
-template<typename T>
-struct SelectiveQuantizer<T, false>
-{
-    static T Quantize(float value, float scale, int32_t offset)
-    {
-        boost::ignore_unused(scale, offset);
-        return value;
-    }
-
-    static float Dequantize(T value, float scale, int32_t offset)
-    {
-        boost::ignore_unused(scale, offset);
-        return value;
-    }
-};
-
-template<typename T>
-T SelectiveQuantize(float value, float scale, int32_t offset)
-{
-    return SelectiveQuantizer<T, armnn::IsQuantizedType<T>()>::Quantize(value, scale, offset);
-};
-
-template<typename T>
-float SelectiveDequantize(T value, float scale, int32_t offset)
-{
-    return SelectiveQuantizer<T, armnn::IsQuantizedType<T>()>::Dequantize(value, scale, offset);
-};
-
-template<typename ItType>
-struct IsFloatingPointIterator
-{
-    static constexpr bool value=std::is_floating_point<typename std::iterator_traits<ItType>::value_type>::value;
-};
-
-template <typename T, typename FloatIt,
-typename std::enable_if<IsFloatingPointIterator<FloatIt>::value, int>::type=0 // Makes sure fp iterator is valid.
->
-std::vector<T> QuantizedVector(float qScale, int32_t qOffset, FloatIt first, FloatIt last)
-{
-    std::vector<T> quantized;
-    quantized.reserve(boost::numeric_cast<size_t>(std::distance(first, last)));
-
-    for (auto it = first; it != last; ++it)
-    {
-        auto f = *it;
-        T q =SelectiveQuantize<T>(f, qScale, qOffset);
-        quantized.push_back(q);
-    }
-
-    return quantized;
-}
-
-template<typename T>
-std::vector<T> QuantizedVector(float qScale, int32_t qOffset, const std::vector<float>& array)
-{
-    return QuantizedVector<T>(qScale, qOffset, array.begin(), array.end());
-}
-
-template<typename T>
-std::vector<T> QuantizedVector(float qScale, int32_t qOffset, std::initializer_list<float> array)
-{
-    return QuantizedVector<T>(qScale, qOffset, array.begin(), array.end());
-}
diff --git a/src/backends/test/ReshapeTestImpl.hpp b/src/backends/test/ReshapeTestImpl.hpp
deleted file mode 100644
index 198de53595..0000000000
--- a/src/backends/test/ReshapeTestImpl.hpp
+++ /dev/null
@@ -1,176 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#pragma once
-
-#include <armnn/ArmNN.hpp>
-#include <armnn/Tensor.hpp>
-#include <armnn/TypesUtils.hpp>
-
-#include <test/TensorHelpers.hpp>
-#include "QuantizeHelper.hpp"
-
-#include <backends/CpuTensorHandle.hpp>
-#include <backends/WorkloadFactory.hpp>
-
-template<typename T>
-LayerTestResult<T, 4> SimpleReshapeTestImpl(
-    armnn::IWorkloadFactory& workloadFactory,
-    armnn::TensorInfo inputTensorInfo,
-    armnn::TensorInfo outputTensorInfo,
-    const std::vector<T>& inputData,
-    const std::vector<T>& outputExpectedData)
-{
-    auto input = MakeTensor<T, 4>(inputTensorInfo, inputData);
-
-    LayerTestResult<T, 4> ret(outputTensorInfo);
-    ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputExpectedData);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ReshapeQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateReshape(data, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-
-    return ret;
-}
-
-LayerTestResult<float, 4> SimpleReshapeFloat32Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int inputShape[] = { 2, 2, 3, 3 };
-    unsigned int outputShape[] = { 2, 2, 9, 1 };
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
-    outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32);
-
-    std::vector<float> input = std::vector<float>(
-    {
-        0.0f, 1.0f, 2.0f,
-        3.0f, 4.0f, 5.0f,
-        6.0f, 7.0f, 8.0f,
-
-        9.0f, 10.0f, 11.0f,
-        12.0f, 13.0f, 14.0f,
-        15.0f, 16.0f, 17.0f,
-
-        18.0f, 19.0f, 20.0f,
-        21.0f, 22.0f, 23.0f,
-        24.0f, 25.0f, 26.0f,
-
-        27.0f, 28.0f, 29.0f,
-        30.0f, 31.0f, 32.0f,
-        33.0f, 34.0f, 35.0f,
-    });
-
-    std::vector<float> outputExpected = std::vector<float>(
-    {
-        0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f,
-
-        9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f,
-
-        18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f, 25.0f, 26.0f,
-
-        27.0f, 28.0f, 29.0f, 30.0f, 31.0f, 32.0f, 33.0f, 34.0f, 35.0f,
-    });
-
-    return SimpleReshapeTestImpl<float>(workloadFactory, inputTensorInfo, outputTensorInfo, input, outputExpected);
-}
-
-LayerTestResult<float, 4> SimpleFloorTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    const armnn::TensorInfo inputTensorInfo({1, 3, 2, 3}, armnn::DataType::Float32);
-    const armnn::TensorInfo outputTensorInfo(inputTensorInfo);
-
-    auto input = MakeTensor<float, 4>(inputTensorInfo,
-        { -37.5f, -15.2f, -8.76f, -2.0f, -1.5f, -1.3f, -0.5f, -0.4f, 0.0f,
-          1.0f, 0.4f, 0.5f, 1.3f, 1.5f, 2.0f, 8.76f, 15.2f, 37.5f });
-
-    LayerTestResult<float, 4> ret(outputTensorInfo);
-    ret.outputExpected = MakeTensor<float, 4>(outputTensorInfo,
-        { -38.0f, -16.0f, -9.0f, -2.0f, -2.0f, -2.0f, -1.0f, -1.0f, 0.0f,
-          1.0f, 0.0f, 0.0f, 1.0f, 1.0f, 2.0f, 8.0f, 15.0f, 37.0f });
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::FloorQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateFloor(data, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-
-    return ret;
-}
-
-LayerTestResult<uint8_t, 4> SimpleReshapeUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int inputShape[] = { 2, 2, 3, 3 };
-    unsigned int outputShape[] = { 2, 2, 9, 1 };
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::QuantisedAsymm8);
-    inputTensorInfo.SetQuantizationScale(1.0f);
-    outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::QuantisedAsymm8);
-    outputTensorInfo.SetQuantizationScale(1.0f);
-
-    std::vector<uint8_t> input = std::vector<uint8_t>(
-    {
-        0, 1, 2,
-        3, 4, 5,
-        6, 7, 8,
-
-        9, 10, 11,
-        12, 13, 14,
-        15, 16, 17,
-
-        18, 19, 20,
-        21, 22, 23,
-        24, 25, 26,
-
-        27, 28, 29,
-        30, 31, 32,
-        33, 34, 35,
-    });
-
-    std::vector<uint8_t> outputExpected = std::vector<uint8_t>(
-    {
-        0, 1, 2, 3, 4, 5, 6, 7, 8,
-
-        9, 10, 11, 12, 13, 14, 15, 16, 17,
-
-        18, 19, 20, 21, 22, 23, 24, 25, 26,
-
-        27, 28, 29, 30, 31, 32, 33, 34, 35,
-    });
-
-    return SimpleReshapeTestImpl<uint8_t>(workloadFactory, inputTensorInfo, outputTensorInfo, input, outputExpected);
-}
diff --git a/src/backends/test/RuntimeTestImpl.hpp b/src/backends/test/RuntimeTestImpl.hpp
deleted file mode 100644
index 671f94b0bb..0000000000
--- a/src/backends/test/RuntimeTestImpl.hpp
+++ /dev/null
@@ -1,42 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#pragma once
-
-#include <armnn/ArmNN.hpp>
-#include <armnn/Runtime.hpp>
-
-namespace
-{
-
-inline void CreateAndDropDummyNetwork(const std::vector<armnn::BackendId>& backends, armnn::Runtime& runtime)
-{
-    armnn::NetworkId networkIdentifier;
-    {
-        armnn::TensorInfo inputTensorInfo(armnn::TensorShape({ 7, 7 }), armnn::DataType::Float32);
-        armnn::TensorInfo outputTensorInfo(armnn::TensorShape({ 7, 7 }), armnn::DataType::Float32);
-
-        armnn::INetworkPtr network(armnn::INetwork::Create());
-
-        armnn::IConnectableLayer* input = network->AddInputLayer(0, "input");
-        armnn::IConnectableLayer* layer = network->AddActivationLayer(armnn::ActivationDescriptor(), "test");
-        armnn::IConnectableLayer* output = network->AddOutputLayer(0, "output");
-
-        input->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
-        layer->GetOutputSlot(0).Connect(output->GetInputSlot(0));
-
-        // Sets the tensors in the network.
-        input->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
-        layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
-
-        // optimize the network
-        armnn::IOptimizedNetworkPtr optNet = Optimize(*network, backends, runtime.GetDeviceSpec());
-
-        runtime.LoadNetwork(networkIdentifier, std::move(optNet));
-    }
-
-    runtime.UnloadNetwork(networkIdentifier);
-}
-
-} // anonymous namespace
diff --git a/src/backends/test/SoftmaxTestImpl.hpp b/src/backends/test/SoftmaxTestImpl.hpp
deleted file mode 100644
index 0bca8be49d..0000000000
--- a/src/backends/test/SoftmaxTestImpl.hpp
+++ /dev/null
@@ -1,152 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#pragma once
-
-#include <armnn/ArmNN.hpp>
-#include <armnn/Tensor.hpp>
-#include <armnn/TypesUtils.hpp>
-
-#include <test/TensorHelpers.hpp>
-#include "QuantizeHelper.hpp"
-
-#include <backends/CpuTensorHandle.hpp>
-#include <backends/WorkloadFactory.hpp>
-
-#include <algorithm>
-
-template<typename T>
-LayerTestResult<T, 2> SimpleSoftmaxTestImpl(armnn::IWorkloadFactory& workloadFactory, float beta)
-{
-    using std::exp;
-
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int inputShape[] = { 2, 4 };
-
-    inputTensorInfo = armnn::TensorInfo(2, inputShape, armnn::GetDataType<T>());
-    float qScale = 1.f / 256.f;
-    int qOffset = 0;
-    inputTensorInfo.SetQuantizationScale(qScale);
-    inputTensorInfo.SetQuantizationOffset(qOffset);
-
-    outputTensorInfo = armnn::TensorInfo(2, inputShape, armnn::GetDataType<T>());
-    outputTensorInfo.SetQuantizationScale(qScale);
-    outputTensorInfo.SetQuantizationOffset(qOffset);
-
-    LayerTestResult<T, 2> ret(outputTensorInfo);
-
-    // Each row is independently softmax'd.
-    auto input = MakeTensor<T, 2>(inputTensorInfo, std::vector<T>(
-        QuantizedVector<T>(qScale, 0, {
-            0.f, 1.f, 0.f, 0.f,
-            .5f, 0.f, 0.f, 0.f,
-        })));
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::SoftmaxQueueDescriptor data;
-    data.m_Parameters.m_Beta = beta;
-
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateSoftmax(data, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get());
-
-    float x0[4] = { exp((0.f - 1.0f) * beta), exp((1.0f - 1.0f) * beta),
-        exp((0.0f - 1.0f) * beta), exp((0.0f - 1.0f) * beta) };
-    float sum0 = x0[0] + x0[1] + x0[2] + x0[3];
-    float x1[4] = { exp((0.5f - 0.5f) * beta), exp((0.0f - 0.5f) * beta),
-        exp((0.0f - 0.5f) * beta), exp((0.0f - 0.5f) * beta) };
-    float sum1 = x1[0] + x1[1] + x1[2] + x1[3];
-
-    ret.outputExpected = MakeTensor<T, 2>(outputTensorInfo, std::vector<T>(
-        QuantizedVector<T>(qScale, qOffset, {
-        x0[0] / sum0, x0[1] / sum0, x0[2] / sum0, x0[3] / sum0,
-        x1[0] / sum1, x1[1] / sum1, x1[2] / sum1, x1[3] / sum1
-        })));
-
-    return ret;
-}
-
-template<typename T>
-LayerTestResult<T, 2> CompareSoftmaxTestImpl(armnn::IWorkloadFactory& workloadFactory,
-    armnn::IWorkloadFactory& refWorkloadFactory,
-    float beta)
-{
-
-    const int batchSize = 20;
-    const int channels = 30;
-
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int inputShape[] = { batchSize, channels };
-
-    inputTensorInfo = armnn::TensorInfo(2, inputShape, armnn::GetDataType<T>());
-    outputTensorInfo = armnn::TensorInfo(2, inputShape, armnn::GetDataType<T>());
-    float qScale = 1.f / 256.f;
-    int qOffset = 0;
-    inputTensorInfo.SetQuantizationScale(qScale);
-    inputTensorInfo.SetQuantizationOffset(qOffset);
-    outputTensorInfo.SetQuantizationScale(qScale);
-    outputTensorInfo.SetQuantizationOffset(qOffset);
-
-
-    LayerTestResult<T, 2> ret(outputTensorInfo);
-    auto input = MakeRandomTensor<T, 2>(inputTensorInfo, 0xF00D, 0.0f, 1.0f);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::SoftmaxQueueDescriptor data;
-    data.m_Parameters.m_Beta = beta;
-
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo);
-
-
-    armnn::SoftmaxQueueDescriptor refData = data;
-    armnn::WorkloadInfo refInfo = info;
-    SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get());
-    SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateSoftmax(data, info);
-    std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateSoftmax(refData, refInfo);
-
-    outputHandleRef->Allocate();
-    inputHandleRef->Allocate();
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0]);
-    CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-    refWorkloadFactory.Finalize();
-    workloadRef->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get());
-    CopyDataFromITensorHandle(&ret.outputExpected[0][0], outputHandleRef.get());
-
-    return ret;
-}
diff --git a/src/backends/test/SplitterTestImpl.hpp b/src/backends/test/SplitterTestImpl.hpp
deleted file mode 100644
index 396cc1bcb2..0000000000
--- a/src/backends/test/SplitterTestImpl.hpp
+++ /dev/null
@@ -1,306 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#pragma once
-
-#include <armnn/ArmNN.hpp>
-#include <armnn/Tensor.hpp>
-
-#include <test/TensorHelpers.hpp>
-
-#include <backends/CpuTensorHandle.hpp>
-#include <backends/WorkloadFactory.hpp>
-
-#include <backends/test/QuantizeHelper.hpp>
-
-
-template<typename T>
-std::vector<LayerTestResult<T,3>> SplitterTestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                     float qScale = 0.0f,
-                                                     int32_t qOffset = 0)
-{
-    unsigned int inputWidth = 5;
-    unsigned int inputHeight = 6;
-    unsigned int inputChannels = 3;
-
-    // NOTE: Compute Library imposes a restriction that the x and y dimension (input height and width)
-    //       cannot be split.
-    //       For the reasons for this, see first comment on https://jira.arm.com/browse/IVGCVSW-1239
-    //
-    // This test has therefore been recast to split the channels, then split the resulting subtensor.
-
-    // To take channel 0 of original output
-    // and channel 0 and channel 1 of the split subtensor.
-    unsigned int outputWidth1 = inputWidth;
-    unsigned int outputHeight1 = inputHeight;
-    unsigned int outputChannels1 = 1;
-
-    // To take channel 1 and 2 of the original output.
-    unsigned int outputWidth2 = inputWidth;
-    unsigned int outputHeight2 = inputHeight;
-    unsigned int outputChannels2 = 2;
-
-
-    // Define the tensor descriptors.
-    armnn::TensorInfo inputTensorInfo({ inputChannels, inputHeight, inputWidth }, armnn::GetDataType<T>());
-
-    // Outputs of the original split.
-    armnn::TensorInfo outputTensorInfo1({ outputChannels1, outputHeight1, outputWidth1 }, armnn::GetDataType<T>());
-    armnn::TensorInfo outputTensorInfo2({ outputChannels2, outputHeight2, outputWidth2 }, armnn::GetDataType<T>());
-
-    // Outputs of the subsequent subtensor split.
-    armnn::TensorInfo outputTensorInfo3({ outputChannels1, outputHeight1, outputWidth1 }, armnn::GetDataType<T>());
-    armnn::TensorInfo outputTensorInfo4({ outputChannels1, outputHeight1, outputWidth1 }, armnn::GetDataType<T>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    // The quantization doesn't really matter as the splitter operator doesn't dequantize/quantize.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo1.SetQuantizationScale(qScale);
-        outputTensorInfo1.SetQuantizationOffset(qOffset);
-        outputTensorInfo2.SetQuantizationScale(qScale);
-        outputTensorInfo2.SetQuantizationOffset(qOffset);
-        outputTensorInfo3.SetQuantizationScale(qScale);
-        outputTensorInfo3.SetQuantizationOffset(qOffset);
-        outputTensorInfo4.SetQuantizationScale(qScale);
-        outputTensorInfo4.SetQuantizationOffset(qOffset);
-    }
-
-    LayerTestResult<T,3> ret1(outputTensorInfo1);
-    LayerTestResult<T,3> ret2(outputTensorInfo2);
-    LayerTestResult<T,3> ret3(outputTensorInfo3);
-    LayerTestResult<T,3> ret4(outputTensorInfo4);
-
-    auto input = MakeTensor<T, 3>(inputTensorInfo, std::vector<T>(
-        QuantizedVector<T>(qScale, qOffset, {
-            1.0f, 2.0f, 3.0f, 4.0f, 5.0f,
-            6.0f, 7.0f, 8.0f, 9.0f, 10.0f,
-            11.0f, 12.0f, 13.0f, 14.0f, 15.0f,
-            16.0f, 17.0f, 18.0f, 19.0f, 20.0f,
-            21.0f, 22.0f, 23.0f, 24.0f, 25.0f,
-            26.0f, 27.0f, 28.0f, 29.0f, 30.0f,
-
-            31.0f, 32.0f, 33.0f, 34.0f, 35.0f,
-            36.0f, 37.0f, 38.0f, 39.0f, 40.0f,
-            41.0f, 42.0f, 43.0f, 44.0f, 45.0f,
-            46.0f, 47.0f, 48.0f, 49.0f, 50.0f,
-            51.0f, 52.0f, 53.0f, 54.0f, 55.0f,
-            56.0f, 57.0f, 58.0f, 59.0f, 60.0f,
-
-            61.0f, 62.0f, 63.0f, 64.0f, 65.0f,
-            66.0f, 67.0f, 68.0f, 69.0f, 70.0f,
-            71.0f, 72.0f, 73.0f, 74.0f, 75.0f,
-            76.0f, 77.0f, 78.0f, 79.0f, 80.0f,
-            81.0f, 82.0f, 83.0f, 84.0f, 85.0f,
-            86.0f, 87.0f, 88.0f, 89.0f, 90.0f,
-        })
-    ));
-
-    // Channel 0 of the original input.
-    ret1.outputExpected = MakeTensor<T, 3>(outputTensorInfo1, std::vector<T>(
-        QuantizedVector<T>(qScale, qOffset, {
-            1.0f, 2.0f, 3.0f, 4.0f, 5.0f,
-            6.0f, 7.0f, 8.0f, 9.0f, 10.0f,
-            11.0f, 12.0f, 13.0f, 14.0f, 15.0f,
-            16.0f, 17.0f, 18.0f, 19.0f, 20.0f,
-            21.0f, 22.0f, 23.0f, 24.0f, 25.0f,
-            26.0f, 27.0f, 28.0f, 29.0f, 30.0f,
-        })
-    ));
-
-    // Channel 1 & 2 of the original input.
-    ret2.outputExpected = MakeTensor<T, 3>(outputTensorInfo2, std::vector<T>(
-        QuantizedVector<T>(qScale, qOffset, {
-            31.0f, 32.0f, 33.0f, 34.0f, 35.0f,
-            36.0f, 37.0f, 38.0f, 39.0f, 40.0f,
-            41.0f, 42.0f, 43.0f, 44.0f, 45.0f,
-            46.0f, 47.0f, 48.0f, 49.0f, 50.0f,
-            51.0f, 52.0f, 53.0f, 54.0f, 55.0f,
-            56.0f, 57.0f, 58.0f, 59.0f, 60.0f,
-
-            61.0f, 62.0f, 63.0f, 64.0f, 65.0f,
-            66.0f, 67.0f, 68.0f, 69.0f, 70.0f,
-            71.0f, 72.0f, 73.0f, 74.0f, 75.0f,
-            76.0f, 77.0f, 78.0f, 79.0f, 80.0f,
-            81.0f, 82.0f, 83.0f, 84.0f, 85.0f,
-            86.0f, 87.0f, 88.0f, 89.0f, 90.0f,
-        })
-    ));
-
-    // Channel 0 of return 2 (i.e. channels 1 and 2 of the original input).
-    ret3.outputExpected = MakeTensor<T, 3>(outputTensorInfo3, std::vector<T>(
-        QuantizedVector<T>(qScale, qOffset, {
-            31.0f, 32.0f, 33.0f, 34.0f, 35.0f,
-            36.0f, 37.0f, 38.0f, 39.0f, 40.0f,
-            41.0f, 42.0f, 43.0f, 44.0f, 45.0f,
-            46.0f, 47.0f, 48.0f, 49.0f, 50.0f,
-            51.0f, 52.0f, 53.0f, 54.0f, 55.0f,
-            56.0f, 57.0f, 58.0f, 59.0f, 60.0f,
-        })
-    ));
-
-    // Channel 1 of return 2.
-    ret4.outputExpected = MakeTensor<T, 3>(outputTensorInfo4, std::vector<T>(
-        QuantizedVector<T>(qScale, qOffset, {
-            61.0f, 62.0f, 63.0f, 64.0f, 65.0f,
-            66.0f, 67.0f, 68.0f, 69.0f, 70.0f,
-            71.0f, 72.0f, 73.0f, 74.0f, 75.0f,
-            76.0f, 77.0f, 78.0f, 79.0f, 80.0f,
-            81.0f, 82.0f, 83.0f, 84.0f, 85.0f,
-            86.0f, 87.0f, 88.0f, 89.0f, 90.0f,
-        })
-    ));
-
-    // NOTE: as a corollary of the splitting of x and y restriction the x and y values of the view origins
-    //       have to be zero, the co-ordinates are as per the tensor info above channels, height/y, width/x
-    //       note that under the hood the compute engine reverses these i.e. its coordinate system is x, y, channels.
-    std::vector<unsigned int> wOrigin1 = {0, 0, 0}; //Extent of the window is defined by size of output[0].
-    armnn::SplitterQueueDescriptor::ViewOrigin window1(wOrigin1);
-
-    std::vector<unsigned int> wOrigin2 = {1, 0, 0}; //Extent of the window is defined by size of output[1].
-    armnn::SplitterQueueDescriptor::ViewOrigin window2(wOrigin2);
-
-    std::vector<unsigned int> wOrigin3 = {0, 0, 0}; //Extent of the window is defined by size of output[2].
-    armnn::SplitterQueueDescriptor::ViewOrigin window3(wOrigin3);
-
-    std::vector<unsigned int> wOrigin4 = {1, 0, 0}; //Extent of the window is defined by size of output[3].
-    armnn::SplitterQueueDescriptor::ViewOrigin window4(wOrigin4);
-
-    bool subTensorsSupported = workloadFactory.SupportsSubTensors();
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle  = workloadFactory.CreateTensorHandle(inputTensorInfo);
-
-    std::unique_ptr<armnn::ITensorHandle> outputHandle1 =
-        subTensorsSupported ?
-            workloadFactory.CreateSubTensorHandle(*inputHandle, outputTensorInfo1.GetShape(), wOrigin1.data()) :
-            workloadFactory.CreateTensorHandle(outputTensorInfo1);
-
-    std::unique_ptr<armnn::ITensorHandle> outputHandle2 =
-        subTensorsSupported ?
-            workloadFactory.CreateSubTensorHandle(*inputHandle, outputTensorInfo2.GetShape(), wOrigin2.data()) :
-            workloadFactory.CreateTensorHandle(outputTensorInfo2);
-
-    std::unique_ptr<armnn::ITensorHandle> outputHandle3 =
-        subTensorsSupported ?
-            workloadFactory.CreateSubTensorHandle(*outputHandle2, outputTensorInfo3.GetShape(), wOrigin3.data()) :
-            workloadFactory.CreateTensorHandle(outputTensorInfo3);
-
-    std::unique_ptr<armnn::ITensorHandle> outputHandle4 =
-        subTensorsSupported ?
-            workloadFactory.CreateSubTensorHandle(*outputHandle2, outputTensorInfo4.GetShape(), wOrigin4.data()) :
-            workloadFactory.CreateTensorHandle(outputTensorInfo4);
-
-    // Do the first split
-    armnn::SplitterQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo1, outputHandle1.get());
-    AddOutputToWorkload(data, info, outputTensorInfo2, outputHandle2.get());
-
-    data.m_ViewOrigins.push_back(window1);
-    data.m_ViewOrigins.push_back(window2);
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateSplitter(data, info);
-
-    inputHandle->Allocate();
-    outputHandle1->Allocate();
-    outputHandle2->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0]);
-
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret1.output[0][0][0], outputHandle1.get());
-    CopyDataFromITensorHandle(&ret2.output[0][0][0], outputHandle2.get());
-
-//    // Do the second split.
-    armnn::SplitterQueueDescriptor data2;
-    armnn::WorkloadInfo info2;
-    AddInputToWorkload(data2, info2, outputTensorInfo2, outputHandle2.get());
-    AddOutputToWorkload(data2, info2, outputTensorInfo3, outputHandle3.get());
-    AddOutputToWorkload(data2, info2, outputTensorInfo4, outputHandle4.get());
-
-    data2.m_ViewOrigins.push_back(window3);
-    data2.m_ViewOrigins.push_back(window4);
-
-    std::unique_ptr<armnn::IWorkload> workload2 = workloadFactory.CreateSplitter(data2, info2);
-
-    outputHandle3->Allocate();
-    outputHandle4->Allocate();
-
-    workload2->Execute();
-
-    CopyDataFromITensorHandle(&ret3.output[0][0][0], outputHandle3.get());
-    CopyDataFromITensorHandle(&ret4.output[0][0][0], outputHandle4.get());
-
-    std::vector<LayerTestResult<T,3>> ret = {ret1, ret2, ret3, ret4,};
-
-    return ret;
-}
-
-
-template <typename T>
-LayerTestResult<T, 3> CopyViaSplitterTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset)
-{
-    const armnn::TensorInfo tensorInfo({ 3, 6, 5 }, armnn::GetDataType<T>());
-    auto input = MakeTensor<T, 3>(tensorInfo, QuantizedVector<T>(qScale, qOffset,
-                                                                 {
-                                                                     1.0f, 2.0f, 3.0f, 4.0f, 5.0f,
-                                                                     6.0f, 7.0f, 8.0f, 9.0f, 10.0f,
-                                                                     11.0f, 12.0f, 13.0f, 14.0f, 15.0f,
-                                                                     16.0f, 17.0f, 18.0f, 19.0f, 20.0f,
-                                                                     21.0f, 22.0f, 23.0f, 24.0f, 25.0f,
-                                                                     26.0f, 27.0f, 28.0f, 29.0f, 30.0f,
-
-                                                                     31.0f, 32.0f, 33.0f, 34.0f, 35.0f,
-                                                                     36.0f, 37.0f, 38.0f, 39.0f, 40.0f,
-                                                                     41.0f, 42.0f, 43.0f, 44.0f, 45.0f,
-                                                                     46.0f, 47.0f, 48.0f, 49.0f, 50.0f,
-                                                                     51.0f, 52.0f, 53.0f, 54.0f, 55.0f,
-                                                                     56.0f, 57.0f, 58.0f, 59.0f, 60.0f,
-
-                                                                     61.0f, 62.0f, 63.0f, 64.0f, 65.0f,
-                                                                     66.0f, 67.0f, 68.0f, 69.0f, 70.0f,
-                                                                     71.0f, 72.0f, 73.0f, 74.0f, 75.0f,
-                                                                     76.0f, 77.0f, 78.0f, 79.0f, 80.0f,
-                                                                     81.0f, 82.0f, 83.0f, 84.0f, 85.0f,
-                                                                     86.0f, 87.0f, 88.0f, 89.0f, 90.0f,
-                                                                 }));
-
-    std::vector<unsigned int> origin = { 0, 0, 0 };
-    armnn::SplitterQueueDescriptor::ViewOrigin window(origin);
-
-    const bool subTensorsSupported = workloadFactory.SupportsSubTensors();
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(tensorInfo);
-
-    std::unique_ptr<armnn::ITensorHandle> outputHandle =
-        subTensorsSupported ?
-            workloadFactory.CreateSubTensorHandle(*inputHandle, tensorInfo.GetShape(), origin.data()) :
-            workloadFactory.CreateTensorHandle(tensorInfo);
-
-    armnn::SplitterQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, tensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, tensorInfo, outputHandle.get());
-
-    data.m_ViewOrigins.push_back(window);
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateSplitter(data, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0]);
-
-    workload->Execute();
-
-    LayerTestResult<T, 3> ret(tensorInfo);
-    CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get());
-    ret.outputExpected = input;
-
-    return ret;
-}
diff --git a/src/backends/test/TensorCopyUtils.cpp b/src/backends/test/TensorCopyUtils.cpp
deleted file mode 100644
index 7e17e8b9fd..0000000000
--- a/src/backends/test/TensorCopyUtils.cpp
+++ /dev/null
@@ -1,161 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include "TensorCopyUtils.hpp"
-
-#include <armnnUtils/Half.hpp>
-
-
-#ifdef ARMCOMPUTECL_ENABLED
-#include <backends/cl/ClTensorHandle.hpp>
-#endif
-
-#if ARMCOMPUTENEON_ENABLED
-#include <backends/neon/NeonTensorHandle.hpp>
-#endif
-
-#if ARMCOMPUTECLENABLED || ARMCOMPUTENEON_ENABLED
-#include <backends/aclCommon/ArmComputeTensorUtils.hpp>
-#endif
-
-#include <backends/CpuTensorHandle.hpp>
-
-#include <boost/cast.hpp>
-#include <algorithm>
-#include <cstring>
-
-void CopyDataToITensorHandle(armnn::ITensorHandle* tensorHandle, const void* mem)
-{
-    switch (tensorHandle->GetType())
-    {
-        case armnn::ITensorHandle::Cpu:
-        {
-            auto handle = boost::polymorphic_downcast<armnn::ScopedCpuTensorHandle*>(tensorHandle);
-            memcpy(handle->GetTensor<void>(), mem, handle->GetTensorInfo().GetNumBytes());
-            break;
-        }
-#ifdef ARMCOMPUTECL_ENABLED
-        case armnn::ITensorHandle::CL:
-        {
-            using armnn::armcomputetensorutils::CopyArmComputeITensorData;
-            auto handle = boost::polymorphic_downcast<armnn::IClTensorHandle*>(tensorHandle);
-            handle->Map(true);
-            switch(handle->GetDataType())
-            {
-                case arm_compute::DataType::F32:
-                    CopyArmComputeITensorData(static_cast<const float*>(mem), handle->GetTensor());
-                    break;
-                case arm_compute::DataType::QASYMM8:
-                    CopyArmComputeITensorData(static_cast<const uint8_t*>(mem), handle->GetTensor());
-                    break;
-                case arm_compute::DataType::F16:
-                    CopyArmComputeITensorData(static_cast<const armnn::Half*>(mem), handle->GetTensor());
-                    break;
-                default:
-                {
-                    throw armnn::UnimplementedException();
-                }
-            }
-            handle->Unmap();
-            break;
-        }
-#endif
-#if ARMCOMPUTENEON_ENABLED
-        case armnn::ITensorHandle::Neon:
-        {
-            using armnn::armcomputetensorutils::CopyArmComputeITensorData;
-            auto handle = boost::polymorphic_downcast<armnn::INeonTensorHandle*>(tensorHandle);
-            switch (handle->GetDataType())
-            {
-                case arm_compute::DataType::F32:
-                    CopyArmComputeITensorData(static_cast<const float*>(mem), handle->GetTensor());
-                    break;
-                case arm_compute::DataType::QASYMM8:
-                    CopyArmComputeITensorData(static_cast<const uint8_t*>(mem), handle->GetTensor());
-                    break;
-                default:
-                {
-                    throw armnn::UnimplementedException();
-                }
-            }
-            break;
-        }
-#endif
-        default:
-        {
-            throw armnn::UnimplementedException();
-        }
-    }
-}
-
-void CopyDataFromITensorHandle(void* mem, const armnn::ITensorHandle* tensorHandle)
-{
-    switch (tensorHandle->GetType())
-    {
-        case armnn::ITensorHandle::Cpu:
-        {
-            auto handle = boost::polymorphic_downcast<const armnn::ScopedCpuTensorHandle*>(tensorHandle);
-            memcpy(mem, handle->GetTensor<void>(), handle->GetTensorInfo().GetNumBytes());
-            break;
-        }
-#ifdef ARMCOMPUTECL_ENABLED
-        case armnn::ITensorHandle::CL:
-        {
-            using armnn::armcomputetensorutils::CopyArmComputeITensorData;
-            auto handle = boost::polymorphic_downcast<const armnn::IClTensorHandle*>(tensorHandle);
-            const_cast<armnn::IClTensorHandle*>(handle)->Map(true);
-            switch(handle->GetDataType())
-            {
-                case arm_compute::DataType::F32:
-                    CopyArmComputeITensorData(handle->GetTensor(), static_cast<float*>(mem));
-                    break;
-                case arm_compute::DataType::QASYMM8:
-                    CopyArmComputeITensorData(handle->GetTensor(), static_cast<uint8_t*>(mem));
-                    break;
-                case arm_compute::DataType::F16:
-                    CopyArmComputeITensorData(handle->GetTensor(), static_cast<armnn::Half*>(mem));
-                    break;
-                default:
-                {
-                    throw armnn::UnimplementedException();
-                }
-            }
-            const_cast<armnn::IClTensorHandle*>(handle)->Unmap();
-            break;
-        }
-#endif
-#if ARMCOMPUTENEON_ENABLED
-        case armnn::ITensorHandle::Neon:
-        {
-            using armnn::armcomputetensorutils::CopyArmComputeITensorData;
-            auto handle = boost::polymorphic_downcast<const armnn::INeonTensorHandle*>(tensorHandle);
-            switch (handle->GetDataType())
-            {
-                case arm_compute::DataType::F32:
-                    CopyArmComputeITensorData(handle->GetTensor(), static_cast<float*>(mem));
-                    break;
-                case arm_compute::DataType::QASYMM8:
-                    CopyArmComputeITensorData(handle->GetTensor(), static_cast<uint8_t*>(mem));
-                    break;
-                default:
-                {
-                    throw armnn::UnimplementedException();
-                }
-            }
-            break;
-        }
-#endif
-        default:
-        {
-            throw armnn::UnimplementedException();
-        }
-    }
-}
-
-void AllocateAndCopyDataToITensorHandle(armnn::ITensorHandle* tensorHandle, const void* mem)
-{
-    tensorHandle->Allocate();
-    CopyDataToITensorHandle(tensorHandle, mem);
-}
diff --git a/src/backends/test/TensorCopyUtils.hpp b/src/backends/test/TensorCopyUtils.hpp
deleted file mode 100644
index 4a3ba64239..0000000000
--- a/src/backends/test/TensorCopyUtils.hpp
+++ /dev/null
@@ -1,14 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#pragma once
-
-#include <armnn/Tensor.hpp>
-#include <backends/ITensorHandle.hpp>
-
-void CopyDataToITensorHandle(armnn::ITensorHandle* tensorHandle, const void* mem);
-
-void CopyDataFromITensorHandle(void* mem, const armnn::ITensorHandle* tensorHandle);
-
-void AllocateAndCopyDataToITensorHandle(armnn::ITensorHandle* tensorHandle, const void* mem);
\ No newline at end of file
diff --git a/src/backends/test/WorkloadDataValidation.cpp b/src/backends/test/WorkloadDataValidation.cpp
deleted file mode 100644
index 7e3a90feda..0000000000
--- a/src/backends/test/WorkloadDataValidation.cpp
+++ /dev/null
@@ -1,471 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#include <boost/test/unit_test.hpp>
-#include <backends/CpuTensorHandle.hpp>
-#include <backends/Workload.hpp>
-#include <backends/reference/workloads/RefWorkloads.hpp>
-#include <backends/reference/RefWorkloadFactory.hpp>
-
-#include <armnn/Exceptions.hpp>
-
-#include "WorkloadTestUtils.hpp"
-
-using namespace armnn;
-
-BOOST_AUTO_TEST_SUITE(WorkloadInfoValidation)
-
-
-
-BOOST_AUTO_TEST_CASE(QueueDescriptor_Validate_WrongNumOfInputsOutputs)
-{
-    InputQueueDescriptor invalidData;
-    WorkloadInfo invalidInfo;
-    //Invalid argument exception is expected, because no inputs and no outputs were defined.
-    BOOST_CHECK_THROW(RefWorkloadFactory().CreateInput(invalidData, invalidInfo), armnn::InvalidArgumentException);
-}
-
-BOOST_AUTO_TEST_CASE(RefPooling2dFloat32Workload_Validate_WrongDimTensor)
-{
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int inputShape[]  = {2, 3, 4}; // <- Invalid - input tensor has to be 4D.
-    unsigned int outputShape[] = {2, 3, 4, 5};
-
-    outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32);
-    inputTensorInfo  = armnn::TensorInfo(3, inputShape, armnn::DataType::Float32);
-
-    Pooling2dQueueDescriptor invalidData;
-    WorkloadInfo           invalidInfo;
-
-    AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr);
-    AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr);
-
-    // Invalid argument exception is expected, input tensor has to be 4D.
-    BOOST_CHECK_THROW(RefPooling2dFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-}
-
-BOOST_AUTO_TEST_CASE(SoftmaxQueueDescriptor_Validate_WrongInputHeight)
-{
-    unsigned int inputHeight = 1;
-    unsigned int inputWidth = 1;
-    unsigned int inputChannels = 4;
-    unsigned int inputNum = 2;
-
-    unsigned int outputChannels = inputChannels;
-    unsigned int outputHeight = inputHeight + 1;    //Makes data invalid - Softmax expects height and width to be 1.
-    unsigned int outputWidth = inputWidth;
-    unsigned int outputNum = inputNum;
-
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth };
-    unsigned int outputShape[] = { outputNum, outputChannels, outputHeight, outputWidth };
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
-    outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32);
-
-    SoftmaxQueueDescriptor invalidData;
-    WorkloadInfo           invalidInfo;
-
-    AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr);
-    AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr);
-
-    //Invalid argument exception is expected, because height != 1.
-    BOOST_CHECK_THROW(RefSoftmaxFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-}
-
-BOOST_AUTO_TEST_CASE(FullyConnectedQueueDescriptor_Validate_RequiredDataMissing)
-{
-    unsigned int inputWidth = 1;
-    unsigned int inputHeight = 1;
-    unsigned int inputChannels = 5;
-    unsigned int inputNum = 2;
-
-    unsigned int outputWidth = 1;
-    unsigned int outputHeight = 1;
-    unsigned int outputChannels = 3;
-    unsigned int outputNum = 2;
-
-    // Define the tensor descriptors.
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-    armnn::TensorInfo weightsDesc;
-    armnn::TensorInfo biasesDesc;
-
-    unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth };
-    unsigned int outputShape[] = { outputNum, outputChannels, outputHeight, outputWidth };
-    unsigned int weightsShape[] = { 1, 1, inputChannels, outputChannels };
-    unsigned int biasShape[] = { 1, outputChannels, outputHeight, outputWidth };
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
-    outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32);
-    weightsDesc = armnn::TensorInfo(4, weightsShape, armnn::DataType::Float32);
-    biasesDesc = armnn::TensorInfo(4, biasShape, armnn::DataType::Float32);
-
-    FullyConnectedQueueDescriptor invalidData;
-    WorkloadInfo                  invalidInfo;
-
-    ScopedCpuTensorHandle weightTensor(weightsDesc);
-    ScopedCpuTensorHandle biasTensor(biasesDesc);
-
-    AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr);
-    AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr);
-    invalidData.m_Weight = &weightTensor;
-    invalidData.m_Bias = &biasTensor;
-    invalidData.m_Parameters.m_BiasEnabled = true;
-    invalidData.m_Parameters.m_TransposeWeightMatrix = false;
-
-
-    //Invalid argument exception is expected, because not all required fields have been provided.
-    //In particular inputsData[0], outputsData[0] and weightsData can not be null.
-    BOOST_CHECK_THROW(RefFullyConnectedFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-}
-
-
-BOOST_AUTO_TEST_CASE(NormalizationQueueDescriptor_Validate_WrongInputHeight)
-{
-    constexpr unsigned int inputNum = 5;
-    constexpr unsigned int inputHeight   = 32;
-    constexpr unsigned int inputWidth    = 24;
-    constexpr unsigned int inputChannels = 3;
-
-    constexpr unsigned int outputNum = inputNum;
-    constexpr unsigned int outputChannels = inputChannels;
-    constexpr unsigned int outputHeight = inputHeight + 1; //Makes data invalid - normalization requires.
-                                                           //Input and output to have the same dimensions.
-    constexpr unsigned int outputWidth  = inputWidth;
-
-
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int inputShape[]  = {inputNum, inputChannels, inputHeight, inputWidth};
-    unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth};
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
-    outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32);
-
-
-    armnn::NormalizationAlgorithmMethod normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness;
-    armnn::NormalizationAlgorithmChannel normChannel = armnn::NormalizationAlgorithmChannel::Across;
-    float alpha = 1.f;
-    float beta = 1.f;
-    float kappa = 1.f;
-    uint32_t normSize = 5;
-
-    NormalizationQueueDescriptor invalidData;
-    WorkloadInfo                 invalidInfo;
-
-    AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr);
-    AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr);
-    invalidData.m_Parameters.m_NormChannelType = normChannel;
-    invalidData.m_Parameters.m_NormMethodType  = normMethod;
-    invalidData.m_Parameters.m_NormSize        = normSize;
-    invalidData.m_Parameters.m_Alpha           = alpha;
-    invalidData.m_Parameters.m_Beta            = beta;
-    invalidData.m_Parameters.m_K               = kappa;
-
-    //Invalid argument exception is expected, because input height != output height.
-    BOOST_CHECK_THROW(RefNormalizationFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-}
-
-BOOST_AUTO_TEST_CASE(SplitterQueueDescriptor_Validate_WrongWindow)
-{
-    constexpr unsigned int inputNum = 1;
-    constexpr unsigned int inputHeight   = 32;
-    constexpr unsigned int inputWidth    = 24;
-    constexpr unsigned int inputChannels = 3;
-
-    constexpr unsigned int outputNum = inputNum;
-    constexpr unsigned int outputChannels = inputChannels;
-    constexpr unsigned int outputHeight = 18;
-    constexpr unsigned int outputWidth  = inputWidth;
-
-
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int inputShape[]  = {inputNum, inputChannels, inputHeight, inputWidth};
-    unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth};
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
-    outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32);
-
-    SplitterQueueDescriptor invalidData;
-    WorkloadInfo            invalidInfo;
-
-    AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr);
-    AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr);
-
-    // Invalid, since it has only 3 dimensions while the input tensor is 4d.
-    std::vector<unsigned int> wOrigin = {0, 0, 0};
-    armnn::SplitterQueueDescriptor::ViewOrigin window(wOrigin);
-    invalidData.m_ViewOrigins.push_back(window);
-
-    BOOST_TEST_INFO("Invalid argument exception is expected, because split window dimensionality does not "
-        "match input.");
-    BOOST_CHECK_THROW(RefSplitterFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-
-    // Invalid, since window extends past the boundary of input tensor.
-    std::vector<unsigned int> wOrigin3 = {0, 0, 15, 0};
-    armnn::SplitterQueueDescriptor::ViewOrigin window3(wOrigin3);
-    invalidData.m_ViewOrigins[0] = window3;
-    BOOST_TEST_INFO("Invalid argument exception is expected (wOrigin3[2]+ outputHeight > inputHeight");
-    BOOST_CHECK_THROW(RefSplitterFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-
-
-    std::vector<unsigned int> wOrigin4 = {0, 0, 0, 0};
-    armnn::SplitterQueueDescriptor::ViewOrigin window4(wOrigin4);
-    invalidData.m_ViewOrigins[0] = window4;
-
-    std::vector<unsigned int> wOrigin5 = {1, 16, 20, 2};
-    armnn::SplitterQueueDescriptor::ViewOrigin window5(wOrigin4);
-    invalidData.m_ViewOrigins.push_back(window5);
-
-    BOOST_TEST_INFO("Invalid exception due to number of split windows not matching number of outputs.");
-    BOOST_CHECK_THROW(RefSplitterFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-}
-
-
-BOOST_AUTO_TEST_CASE(MergerQueueDescriptor_Validate_WrongWindow)
-{
-    constexpr unsigned int inputNum = 1;
-    constexpr unsigned int inputChannels = 3;
-    constexpr unsigned int inputHeight   = 32;
-    constexpr unsigned int inputWidth    = 24;
-
-    constexpr unsigned int outputNum = 1;
-    constexpr unsigned int outputChannels = 3;
-    constexpr unsigned int outputHeight = 32;
-    constexpr unsigned int outputWidth  = 24;
-
-
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int inputShape[]  = {inputNum, inputChannels, inputHeight, inputWidth};
-    unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth};
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
-    outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32);
-
-    MergerQueueDescriptor invalidData;
-    WorkloadInfo          invalidInfo;
-
-    AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr);
-    AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr);
-
-    // Invalid, since it has only 3 dimensions while the input tensor is 4d.
-    std::vector<unsigned int> wOrigin = {0, 0, 0};
-    armnn::MergerQueueDescriptor::ViewOrigin window(wOrigin);
-    invalidData.m_ViewOrigins.push_back(window);
-
-    BOOST_TEST_INFO("Invalid argument exception is expected, because merge window dimensionality does not "
-        "match input.");
-    BOOST_CHECK_THROW(RefMergerFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-
-    // Invalid, since window extends past the boundary of output tensor.
-    std::vector<unsigned int> wOrigin3 = {0, 0, 15, 0};
-    armnn::MergerQueueDescriptor::ViewOrigin window3(wOrigin3);
-    invalidData.m_ViewOrigins[0] = window3;
-    BOOST_TEST_INFO("Invalid argument exception is expected (wOrigin3[2]+ inputHeight > outputHeight");
-    BOOST_CHECK_THROW(RefMergerFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-
-
-    std::vector<unsigned int> wOrigin4 = {0, 0, 0, 0};
-    armnn::MergerQueueDescriptor::ViewOrigin window4(wOrigin4);
-    invalidData.m_ViewOrigins[0] = window4;
-
-    std::vector<unsigned int> wOrigin5 = {1, 16, 20, 2};
-    armnn::MergerQueueDescriptor::ViewOrigin window5(wOrigin4);
-    invalidData.m_ViewOrigins.push_back(window5);
-
-    BOOST_TEST_INFO("Invalid exception due to number of merge windows not matching number of inputs.");
-    BOOST_CHECK_THROW(RefMergerFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-}
-
-BOOST_AUTO_TEST_CASE(AdditionQueueDescriptor_Validate_InputNumbers)
-{
-    armnn::TensorInfo input1TensorInfo;
-    armnn::TensorInfo input2TensorInfo;
-    armnn::TensorInfo input3TensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int shape[]  = {1, 1, 1, 1};
-
-    input1TensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
-    input2TensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
-    input3TensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
-    outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
-
-    AdditionQueueDescriptor invalidData;
-    WorkloadInfo            invalidInfo;
-
-    AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr);
-    AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr);
-
-    // Too few inputs.
-    BOOST_CHECK_THROW(RefAdditionFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-
-    AddInputToWorkload(invalidData, invalidInfo, input2TensorInfo, nullptr);
-
-    // Correct.
-    BOOST_CHECK_NO_THROW(RefAdditionFloat32Workload(invalidData, invalidInfo));
-
-    AddInputToWorkload(invalidData, invalidInfo, input3TensorInfo, nullptr);
-
-    // Too many inputs.
-    BOOST_CHECK_THROW(RefAdditionFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-}
-
-BOOST_AUTO_TEST_CASE(AdditionQueueDescriptor_Validate_InputShapes)
-{
-    armnn::TensorInfo input1TensorInfo;
-    armnn::TensorInfo input2TensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int shape1[] = {1, 1, 2, 1};
-    unsigned int shape2[] = {1, 1, 3, 2};
-
-    // Incompatible shapes even with broadcasting.
-    {
-        input1TensorInfo = armnn::TensorInfo(4, shape1, armnn::DataType::Float32);
-        input2TensorInfo = armnn::TensorInfo(4, shape2, armnn::DataType::Float32);
-        outputTensorInfo = armnn::TensorInfo(4, shape1, armnn::DataType::Float32);
-
-        AdditionQueueDescriptor invalidData;
-        WorkloadInfo            invalidInfo;
-
-        AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr);
-        AddInputToWorkload(invalidData, invalidInfo, input2TensorInfo, nullptr);
-        AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr);
-
-        BOOST_CHECK_THROW(RefAdditionFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-    }
-
-    // Output size not compatible with input sizes.
-    {
-        input1TensorInfo = armnn::TensorInfo(4, shape1, armnn::DataType::Float32);
-        input2TensorInfo = armnn::TensorInfo(4, shape1, armnn::DataType::Float32);
-        outputTensorInfo = armnn::TensorInfo(4, shape2, armnn::DataType::Float32);
-
-        AdditionQueueDescriptor invalidData;
-        WorkloadInfo            invalidInfo;
-
-        AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr);
-        AddInputToWorkload(invalidData, invalidInfo, input2TensorInfo, nullptr);
-        AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr);
-
-        // Output differs.
-        BOOST_CHECK_THROW(RefAdditionFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-    }
-}
-
-BOOST_AUTO_TEST_CASE(MultiplicationQueueDescriptor_Validate_InputTensorDimensionMismatch)
-{
-    armnn::TensorInfo input0TensorInfo;
-    armnn::TensorInfo input1TensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    constexpr unsigned int input0Shape[] = { 2, 2, 4, 4 };
-    constexpr std::size_t dimensionCount = std::extent<decltype(input0Shape)>::value;
-
-    // Checks dimension consistency for input tensors.
-    for (unsigned int dimIndex = 0; dimIndex < dimensionCount; ++dimIndex)
-    {
-        unsigned int input1Shape[dimensionCount];
-        for (unsigned int i = 0; i < dimensionCount; ++i)
-        {
-            input1Shape[i] = input0Shape[i];
-        }
-
-        ++input1Shape[dimIndex];
-
-        input0TensorInfo = armnn::TensorInfo(dimensionCount, input0Shape, armnn::DataType::Float32);
-        input1TensorInfo = armnn::TensorInfo(dimensionCount, input1Shape, armnn::DataType::Float32);
-        outputTensorInfo = armnn::TensorInfo(dimensionCount, input0Shape, armnn::DataType::Float32);
-
-        MultiplicationQueueDescriptor invalidData;
-        WorkloadInfo                  invalidInfo;
-
-        AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr);
-        AddInputToWorkload(invalidData, invalidInfo, input0TensorInfo, nullptr);
-        AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr);
-
-        BOOST_CHECK_THROW(RefMultiplicationFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-    }
-
-    // Checks dimension consistency for input and output tensors.
-    for (unsigned int dimIndex = 0; dimIndex < dimensionCount; ++dimIndex)
-    {
-        unsigned int outputShape[dimensionCount];
-        for (unsigned int i = 0; i < dimensionCount; ++i)
-        {
-            outputShape[i] = input0Shape[i];
-        }
-
-        ++outputShape[dimIndex];
-
-        input0TensorInfo = armnn::TensorInfo(dimensionCount, input0Shape, armnn::DataType::Float32);
-        input1TensorInfo = armnn::TensorInfo(dimensionCount, input0Shape, armnn::DataType::Float32);
-        outputTensorInfo = armnn::TensorInfo(dimensionCount, outputShape, armnn::DataType::Float32);
-
-        MultiplicationQueueDescriptor invalidData;
-        WorkloadInfo                  invalidInfo;
-
-        AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr);
-        AddInputToWorkload(invalidData, invalidInfo, input0TensorInfo, nullptr);
-        AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr);
-
-        BOOST_CHECK_THROW(RefMultiplicationFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-    }
-}
-
-BOOST_AUTO_TEST_CASE(ReshapeQueueDescriptor_Validate_MismatchingNumElements)
-{
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    // The input and output shapes should have the same number of elements, but these don't.
-    unsigned int inputShape[] = { 1, 1, 2, 3 };
-    unsigned int outputShape[] = { 1, 1, 1, 2 };
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
-    outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32);
-
-    ReshapeQueueDescriptor invalidData;
-    WorkloadInfo           invalidInfo;
-
-    AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr);
-    AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr);
-
-    // InvalidArgumentException is expected, because the number of elements don't match.
-    BOOST_CHECK_THROW(RefReshapeFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-}
-
-
-BOOST_AUTO_TEST_CASE(LstmQueueDescriptor_Validate)
-{
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int inputShape[] = { 1, 2 };
-    unsigned int outputShape[] = { 1 };
-
-    inputTensorInfo = armnn::TensorInfo(2, inputShape, armnn::DataType::Float32);
-    outputTensorInfo = armnn::TensorInfo(1, outputShape, armnn::DataType::Float32);
-
-    LstmQueueDescriptor invalidData;
-    WorkloadInfo        invalidInfo;
-
-    AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr);
-    AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr);
-
-    BOOST_CHECK_THROW(invalidData.Validate(invalidInfo), armnn::InvalidArgumentException);
-}
-
-BOOST_AUTO_TEST_SUITE_END()
diff --git a/src/backends/test/WorkloadTestUtils.hpp b/src/backends/test/WorkloadTestUtils.hpp
deleted file mode 100644
index 97f8ebd7d2..0000000000
--- a/src/backends/test/WorkloadTestUtils.hpp
+++ /dev/null
@@ -1,54 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#pragma once
-
-#include <armnn/Tensor.hpp>
-
-namespace armnn
-{
-class ITensorHandle;
-}
-
-template <typename QueueDescriptor>
-void AddInputToWorkload(QueueDescriptor& descriptor,
-    armnn::WorkloadInfo& info,
-    const armnn::TensorInfo& tensorInfo,
-    armnn::ITensorHandle* tensorHandle)
-{
-    descriptor.m_Inputs.push_back(tensorHandle);
-    info.m_InputTensorInfos.push_back(tensorInfo);
-}
-
-template <typename QueueDescriptor>
-void AddOutputToWorkload(QueueDescriptor& descriptor,
-    armnn::WorkloadInfo& info,
-    const armnn::TensorInfo& tensorInfo,
-    armnn::ITensorHandle* tensorHandle)
-{
-    descriptor.m_Outputs.push_back(tensorHandle);
-    info.m_OutputTensorInfos.push_back(tensorInfo);
-}
-
-template <typename QueueDescriptor>
-void SetWorkloadInput(QueueDescriptor& descriptor,
-    armnn::WorkloadInfo& info,
-    unsigned int index,
-    const armnn::TensorInfo& tensorInfo,
-    armnn::ITensorHandle* tensorHandle)
-{
-    descriptor.m_Inputs[index] = tensorHandle;
-    info.m_InputTensorInfos[index] = tensorInfo;
-}
-
-template <typename QueueDescriptor>
-void SetWorkloadOutput(QueueDescriptor& descriptor,
-    armnn::WorkloadInfo& info,
-    unsigned int index,
-    const armnn::TensorInfo& tensorInfo,
-    armnn::ITensorHandle* tensorHandle)
-{
-    descriptor.m_Outputs[index] = tensorHandle;
-    info.m_OutputTensorInfos[index] = tensorInfo;
-}
\ No newline at end of file