IVGCVSW-3381 Break up LayerTests.hpp into more manageable files

Signed-off-by: Aron Virginas-Tar <Aron.Virginas-Tar@arm.com>
Change-Id: Icf39434f09fd340ad664cb3b97b8bee6d9da4838

1 files changed, 3145 insertions, 0 deletions

diff --git a/src/backends/backendsCommon/test/layerTests/Conv2dTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/Conv2dTestImpl.cpp
new file mode 100644
index 0000000000..01c1b1868e
--- /dev/null
+++ b/src/backends/backendsCommon/test/layerTests/Conv2dTestImpl.cpp
@@ -0,0 +1,3145 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "Conv2dTestImpl.hpp"
+
+#include <DataLayoutIndexed.hpp>
+#include <Permute.hpp>
+#include <TensorUtils.hpp>
+
+#include <armnn/ArmNN.hpp>
+
+#include <backendsCommon/CpuTensorHandle.hpp>
+
+#include <backendsCommon/test/QuantizeHelper.hpp>
+#include <backendsCommon/test/TensorCopyUtils.hpp>
+#include <backendsCommon/test/WorkloadTestUtils.hpp>
+
+#include <test/TensorHelpers.hpp>
+
+#include <boost/numeric/conversion/cast.hpp>
+
+#include <string>
+
+//
+// Static data
+//
+
+// 2-channel bias used by a number of Conv2d tests.
+static std::vector<float> Bias2({0, 2});
+
+static std::vector<float> Bias4({1, 2, 3, 4});
+
+static std::vector<float> Bias8({1, 2, 3, 4, 1, 2, 3, 4});
+
+// 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
+});
+
+//
+// Helper templates
+//
+
+// Helper template that returns either Bias2 or an empty vector depending on whether bias is enabled.
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+boost::multi_array<T, 1> GetBias2(bool biasEnabled, float qScale)
+{
+    if(biasEnabled)
+    {
+        armnn::TensorInfo biasDesc({static_cast<unsigned int>(Bias2.size())}, ArmnnType);
+        boost::multi_array<T, 1> bias = MakeTensor<T, 1>(biasDesc, QuantizedVector<T>(qScale, 0.0f, Bias2));
+        return bias;
+    }
+    else
+    {
+        return boost::multi_array<T, 1>();
+    }
+}
+
+// Helper template that returns either Bias4 or an empty vector depending on whether bias is enabled.
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+boost::multi_array<T, 1> GetBias4(bool biasEnabled, float qScale)
+{
+    if(biasEnabled)
+    {
+        armnn::TensorInfo biasDesc({static_cast<unsigned int>(Bias4.size())}, ArmnnType);
+        boost::multi_array<T, 1> bias = MakeTensor<T, 1>(biasDesc, QuantizedVector<T>(qScale, 0.0f, Bias4));
+        return bias;
+    }
+    else
+    {
+        return boost::multi_array<T, 1>();
+    }
+}
+
+// Helper template that returns either Bias8 or an empty vector depending on whether bias is enabled.
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+boost::multi_array<T, 1> GetBias8(bool biasEnabled, float qScale)
+{
+    if(biasEnabled)
+    {
+        armnn::TensorInfo biasDesc({static_cast<unsigned int>(Bias4.size())}, ArmnnType);
+        boost::multi_array<T, 1> bias = MakeTensor<T, 1>(biasDesc, QuantizedVector<T>(qScale, 0.0f, Bias8));
+        return bias;
+    }
+    else
+    {
+        return boost::multi_array<T, 1>();
+    }
+}
+
+// Helper template that returns either Bias4 or an empty vector depending on whether bias is enabled.
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+boost::multi_array<T, 1> GetBias(bool biasEnabled, float qScale, armnn::TensorInfo outputInfo, armnn::DataLayout layout)
+{
+    const armnnUtils::DataLayoutIndexed dataLayoutIndexed(layout);
+    const unsigned int channelsIndex = dataLayoutIndexed.GetChannelsIndex();
+    const unsigned int outputChannels = outputInfo.GetShape()[channelsIndex];
+
+    switch (outputChannels)
+    {
+        case 2:
+        default:
+        {
+            return GetBias2<ArmnnType>(biasEnabled, qScale);
+        }
+        case 4:
+        {
+            return GetBias4<ArmnnType>(biasEnabled, qScale);
+        }
+        case 8:
+        {
+            return GetBias8<ArmnnType>(biasEnabled, qScale);
+        }
+    }
+}
+
+//
+// Implementation templates
+//
+
+// 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);
+            }
+        }
+    }
+}
+
+//
+// Convolution2d implementations
+//
+
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType,
+         typename T = armnn::ResolveType<ArmnnType>, typename B = armnn::ResolveType<ArmnnBType>>
+LayerTestResult<T, 4> SimpleConvolution2dTestImpl(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    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::DataLayout layout = armnn::DataLayout::NCHW,
+    uint32_t padLeft = 0,
+    uint32_t padTop = 0,
+    uint32_t padRight = 0,
+    uint32_t padBottom = 0,
+    uint32_t strideX = 1,
+    uint32_t strideY = 1,
+    uint32_t dilationX = 1,
+    uint32_t dilationY = 1)
+{
+    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 =
+            armnnUtils::GetTensorInfo(2*inputNum, inputChannels, inputHeight, inputWidth, layout, ArmnnType);
+    armnn::TensorInfo outputTensorInfo =
+            armnnUtils::GetTensorInfo(2*outputNum, outputChannels, outputHeight, outputWidth, layout, ArmnnType);
+    armnn::TensorInfo kernelDesc =
+            armnnUtils::GetTensorInfo(kernelDepthMul, kernelChannels, kernelHeight, kernelWidth, layout, ArmnnType);
+    armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, ArmnnBType);
+
+    // 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 == armnn::DataLayout::NHWC)
+    {
+        std::vector<T> tmp(inputData.size());
+        armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(T));
+        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 == armnn::DataLayout::NHWC)
+    {
+        std::vector<T> tmp(outputData.size());
+        armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp.data(), sizeof(T));
+        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::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 == armnn::DataLayout::NHWC)
+    {
+        armnnUtils::Permute(kernelDesc.GetShape(), NCHWToNHWC, originalKernel.data(), kernel.data(), sizeof(T));
+    }
+    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;
+    data.m_Parameters.m_DilationX = dilationX;
+    data.m_Parameters.m_DilationY = dilationY;
+
+    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConvolution2d(data, info);
+    inputHandle->Allocate();
+    outputHandle->Allocate();
+
+    CopyDataToITensorHandle(inputHandle.get(), &batchedInput[0][0][0][0]);
+
+    ExecuteWorkload(*workload, memoryManager);
+
+    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
+
+    return ret;
+}
+
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType,
+         typename T = armnn::ResolveType<ArmnnType>, typename B = armnn::ResolveType<ArmnnBType>>
+LayerTestResult<T, 4> SimpleConvolution2dNhwcTestImpl(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    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,
+    const 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}, ArmnnType);
+    armnn::TensorInfo outputTensorInfo({outputNum, outputHeight, outputWidth, outputChannels},
+                                       ArmnnType);
+    armnn::TensorInfo kernelDesc({kernelChanMul, kernelHeight, kernelWidth, kernelChannels}, ArmnnType);
+    armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, ArmnnBType);
+
+    // 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]);
+
+    ExecuteWorkload(*workload, memoryManager);
+
+    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
+
+    return ret;
+}
+
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T,4> Convolution1dTestImpl(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    float qScale,
+    int32_t qOffset,
+    bool biasEnabled)
+{
+    using B = armnn::ResolveType<ArmnnBType>;
+    // 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}, ArmnnType);
+    armnn::TensorInfo outputInfo({batchSize, outputChannels, outputSize, 1}, ArmnnType);
+    armnn::TensorInfo kernelInfo({outputChannels, inputChannels, kernelSize, 1}, ArmnnType);
+    armnn::TensorInfo biasInfo({outputChannels}, ArmnnBType);
+
+    // 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());
+
+    ExecuteWorkload(*workload, memoryManager);
+
+    // 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<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 4> SimpleConvolution2d3x3NhwcTestCommon(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    float qScale,
+    int32_t qOffset,
+    bool biasEnabled,
+    armnn::DataLayout dataLayout)
+{
+    // Use common single-batch 5x5 image.
+
+    armnn::TensorInfo inputDesc({1, 3, 4, 1}, ArmnnType);
+    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}, ArmnnType);
+    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}, ArmnnType);
+
+    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<ArmnnType, ArmnnType>(
+        workloadFactory,
+        memoryManager,
+        input,
+        kernel,
+        boost::multi_array<T, 1>(),
+        expectedOutput,
+        dataLayout,
+        qScale,
+        qOffset);
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 4> SimpleConvolution2d3x3Stride2x2TestCommon(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+        float qScale,
+        int32_t qOffset,
+        bool biasEnabled,
+        const armnn::DataLayout& dataLayout)
+{
+    // Input is a single-batch, 1 channel, 5x5 image.
+    armnn::TensorInfo inputDesc({1, 5, 5, 1}, ArmnnType);
+    boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc,
+            {
+                1, 5, 2, 3, 5,
+                8, 7, 3, 6, 3,
+                3, 3, 9, 1, 9,
+                4, 1, 8, 1, 3,
+                6, 8, 1, 9, 2
+            });
+
+    // Use a 3x3 kernel.
+    armnn::TensorInfo kernelDesc({1, 3, 3, 1}, ArmnnType);
+    boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc,
+            {
+                4, 5, 6,
+                0, 0, 0,
+                3, 2, 1
+            });
+
+    // Expected output is a single-batch, 1 channel, 3x3 image.
+    armnn::TensorInfo outputDesc({1, 3, 3, 1}, ArmnnType);
+
+    const std::vector<T> outputData =
+            {
+                23, 33, 24,
+                91, 99, 48,
+                26, 50, 19
+            };
+
+    boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, outputData);
+
+    uint32_t padLeft = 1;
+    uint32_t padTop = 1;
+    uint32_t padRight = 1;
+    uint32_t padBottom = 1;
+    uint32_t strideX  = 2;
+    uint32_t strideY  = 2;
+
+    return SimpleConvolution2dNhwcTestImpl<ArmnnType, ArmnnType>(
+        workloadFactory,
+        memoryManager,
+        input,
+        kernel,
+        boost::multi_array<T, 1>(),
+        expectedOutput,
+        dataLayout,
+        qScale,
+        qOffset,
+        padLeft,
+        padTop,
+        padRight,
+        padBottom,
+        strideX,
+        strideY);
+}
+
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 4> SimpleConvolution2d3x5TestCommon(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    float qScale,
+    int32_t qOffset,
+    bool biasEnabled,
+    const armnn::DataLayout layout)
+{
+    // Use common single-batch 3-channel 16x8 image.
+    armnn::TensorInfo inputDesc({1, 3, 8, 16}, ArmnnType);
+    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}, ArmnnType);
+    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}, ArmnnType);
+    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<ArmnnType, ArmnnBType>(
+        workloadFactory,
+        memoryManager,
+        input,
+        kernel,
+        GetBias2<ArmnnBType>(biasEnabled, qScale * qScale),
+        expectedOutput,
+        qScale,
+        qOffset,
+        layout);
+}
+
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType,
+         typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 4> SimpleConvolution2d3x3TestCommon(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    float qScale,
+    int32_t qOffset,
+    bool biasEnabled,
+    const armnn::DataLayout 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}, ArmnnType);
+    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}, ArmnnType);
+    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}, ArmnnType);
+    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<ArmnnType, ArmnnBType>(
+        workloadFactory,
+        memoryManager,
+        input,
+        kernel,
+        GetBias2<ArmnnBType>(biasEnabled, qScale * qScale),
+        expectedOutput,
+        qScale,
+        qOffset,
+        layout);
+}
+
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType,
+         typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 4> Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    const armnn::DataLayout layout,
+    float qScale,
+    int32_t qOffset)
+{
+    // Use a single-batch 1-channel 3x3 image as input.
+    armnn::TensorInfo inputDesc({1, 1, 3, 3}, ArmnnType);
+    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}, ArmnnType);
+    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}, ArmnnType);
+    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<ArmnnType, ArmnnBType>(
+        workloadFactory,
+        memoryManager,
+        input,
+        kernel,
+        GetBias2<ArmnnBType>(false, qScale * qScale),
+        expectedOutput,
+        qScale,
+        qOffset,
+        layout,
+        1,  // Padding left.
+        2,  // Padding top.
+        3,  // Padding right.
+        4); // Padding bottom.
+}
+
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType,
+         typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 4> SimpleConvolution2dAsymmetricPaddingTestCommon(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    const armnn::DataLayout layout,
+    float qScale,
+    int32_t qOffset)
+{
+    // Use a single-batch 1-channel 5x5 image as input.
+    armnn::TensorInfo inputDesc({ 1, 1, 5, 5 }, ArmnnType);
+    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 }, ArmnnType);
+    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 }, ArmnnType);
+    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<ArmnnType, ArmnnBType>(
+        workloadFactory,
+        memoryManager,
+        input,
+        kernel,
+        GetBias2<ArmnnBType>(false, qScale * qScale),
+        expectedOutput,
+        qScale,
+        qOffset,
+        layout,
+        1,  // Padding left.
+        1,  // Padding top.
+        2,  // Padding right.
+        2); // Padding bottom.
+}
+
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 4> Convolution2d3x3DilationTestCommon(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    const std::vector<float>& inputNoQuantizedValues,
+    armnn::TensorInfo& inputTensorInfo,
+    const std::vector<float>& kernelNoQuantizedValues,
+    armnn::TensorInfo& kernelTensorInfo,
+    const std::vector<float>& outputExpectedNoQuantizedValues,
+    armnn::TensorInfo& outputTensorInfo,
+    uint32_t dilationX,
+    uint32_t dilationY,
+    armnn::DataLayout layout = armnn::DataLayout::NCHW,
+    uint32_t padLeft = 0,
+    uint32_t padTop = 0,
+    uint32_t padRight = 0,
+    uint32_t padBottom = 0,
+    uint32_t strideX  = 1,
+    uint32_t strideY  = 1,
+    bool biasEnabled = false
+)
+{
+    float qScale;
+    int32_t qOffset;
+    switch (ArmnnType)
+    {
+        case armnn::DataType::QuantisedAsymm8:
+        {
+            qScale = 0.1f;
+            qOffset = 128;
+            break;
+        }
+        case armnn::DataType::QuantisedSymm16:
+        {
+            qScale = 0.1f;
+            qOffset = 0;
+            break;
+        }
+        case armnn::DataType::Float32:
+        default:
+        {
+            qScale = 0.f;
+            qOffset = 0;
+            break;
+        }
+    }
+
+    inputTensorInfo.SetQuantizationScale(qScale);
+    inputTensorInfo.SetQuantizationOffset(qOffset);
+    kernelTensorInfo.SetQuantizationScale(qScale);
+    kernelTensorInfo.SetQuantizationOffset(qOffset);
+    outputTensorInfo.SetQuantizationScale(qScale);
+    outputTensorInfo.SetQuantizationOffset(qOffset);
+
+    auto input = MakeTensor<T, 4>(inputTensorInfo,
+                                  std::vector<T>(QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(),
+                                                                    inputTensorInfo.GetQuantizationOffset(),
+                                                                    inputNoQuantizedValues)));
+    auto kernel = MakeTensor<T, 4>(kernelTensorInfo,
+                                  std::vector<T>(QuantizedVector<T>(kernelTensorInfo.GetQuantizationScale(),
+                                                                    kernelTensorInfo.GetQuantizationOffset(),
+                                                                    kernelNoQuantizedValues)));
+    auto expectedOutput = MakeTensor<T, 4>(outputTensorInfo,
+                                           std::vector<T>(QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(),
+                                                                             outputTensorInfo.GetQuantizationOffset(),
+                                                                             outputExpectedNoQuantizedValues)));
+
+    return SimpleConvolution2dTestImpl<ArmnnType, ArmnnBType>(
+            workloadFactory,
+            memoryManager,
+            input,
+            kernel,
+            GetBias2<ArmnnBType>(biasEnabled, qScale * qScale),
+            expectedOutput,
+            qScale,
+            qOffset,
+            layout,
+            padLeft,
+            padTop,
+            padRight,
+            padBottom,
+            strideX,
+            strideY,
+            dilationX,
+            dilationY);
+}
+
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T>
+LayerTestResult<T, 4> Convolution2d3x3Dilation3x3Test(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    bool biasEnabled,
+    const armnn::DataLayout layout)
+{
+    armnn::TensorInfo inputTensorInfo({1, 1, 10, 10}, ArmnnType);
+    std::vector<float> inputNoQuantizedValues =
+    {
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        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, 1, 1, 0, 0, 0,
+        0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
+        0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0
+    };
+
+    armnn::TensorInfo kernelTensorInfo({ 1, 1, 3, 3}, ArmnnType);
+    std::vector<float> kernelNoQuantizedValues =
+    {
+        1, 2, 3,
+        4, 5, 6,
+        7, 8, 9
+    };
+
+    // Since the dilation rate is 3 this will dilate the kernel to be like 7x7,
+    // therefore the output will be 4x4: (I−K+2P)/S +1 => (10-7 +0)/1 +1
+    armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4}, ArmnnType);
+    std::vector<float> outputExpectedNoQuantizedValues =
+    {
+        6., 5., 5., 5.,
+        6., 5., 5., 5.,
+        6., 5., 5., 5.,
+        3., 2., 2., 2.
+    };
+
+    return Convolution2d3x3DilationTestCommon<ArmnnType, ArmnnBType>(
+            workloadFactory,
+            memoryManager,
+            inputNoQuantizedValues,
+            inputTensorInfo,
+            kernelNoQuantizedValues,
+            kernelTensorInfo,
+            outputExpectedNoQuantizedValues,
+            outputTensorInfo,
+            3,
+            3,
+            layout,
+            biasEnabled);
+}
+
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T>
+LayerTestResult<T, 4> Convolution2d2x3x3Dilation3x3Test(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    bool biasEnabled,
+    const armnn::DataLayout layout)
+{
+    armnn::TensorInfo inputTensorInfo({1, 2, 10, 10}, ArmnnType);
+    std::vector<float> inputNoQuantizedValues =
+    {
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        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, 1, 1, 0, 0, 0,
+        0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
+        0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        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, 1, 1, 0, 0, 0,
+        0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
+        0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0
+    };
+
+    armnn::TensorInfo kernelTensorInfo({ 1, 2, 3, 3}, ArmnnType);
+    std::vector<float> kernelNoQuantizedValues =
+    {
+        1, 2, 3,
+        4, 5, 6,
+        7, 8, 9,
+
+        1, 2, 3,
+        4, 5, 6,
+        7, 8, 9
+    };
+
+    // Since the dilation rate is 3 this will dilate the kernel to be like 7x7,
+    // therefore the output will be 4x4: (I−K+2P)/S +1 => (10-7 +0)/1 +1
+    armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4}, ArmnnType);
+    std::vector<float> outputExpectedNoQuantizedValues =
+    {
+        12., 10., 10., 10.,
+        12., 10., 10., 10.,
+        12., 10., 10., 10.,
+         6.,  4.,  4.,  4.
+    };
+
+    return Convolution2d3x3DilationTestCommon<ArmnnType, ArmnnBType>(
+            workloadFactory,
+            memoryManager,
+            inputNoQuantizedValues,
+            inputTensorInfo,
+            kernelNoQuantizedValues,
+            kernelTensorInfo,
+            outputExpectedNoQuantizedValues,
+            outputTensorInfo,
+            3,
+            3,
+            layout,
+            biasEnabled);
+}
+
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T>
+LayerTestResult<T, 4> Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test(
+        armnn::IWorkloadFactory &workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager,
+        bool biasEnabled,
+        const armnn::DataLayout layout)
+{
+    armnn::TensorInfo inputTensorInfo({1, 1, 10, 10}, ArmnnType);
+    std::vector<float> inputNoQuantizedValues =
+    {
+        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
+    };
+
+    armnn::TensorInfo kernelTensorInfo({ 1, 1, 2, 2}, ArmnnType);
+    std::vector<float> kernelNoQuantizedValues =
+    {
+        1, 2,
+        3, 4
+    };
+
+    // Since the dilation rate is 2 this will dilate the kernel to be like 3x3: d(K-1)+1 --> 2 x (2-1) + 1 = 3,
+    // therefore the output will be 4x4: (I − K + 2P)/S +1 => trunc ( (10 - 3 + 2x2 ) / 3 + 1 )
+    // where, dilation size = d = 2; kernel size = K = 2; input size = I = 10; padding size = P = 2; stride = S = 3
+    armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4}, ArmnnType);
+    std::vector<float> outputExpectedNoQuantizedValues =
+    {
+        4,  7,  7, 3,
+        6, 10, 10, 4,
+        6, 10, 10, 4,
+        2,  3,  3, 1
+    };
+    uint32_t padLeft = 1;
+    uint32_t padTop = 1;
+    uint32_t padRight = 1;
+    uint32_t padBottom = 1;
+
+    return Convolution2d3x3DilationTestCommon<ArmnnType, ArmnnBType>(
+            workloadFactory,
+            memoryManager,
+            inputNoQuantizedValues,
+            inputTensorInfo,
+            kernelNoQuantizedValues,
+            kernelTensorInfo,
+            outputExpectedNoQuantizedValues,
+            outputTensorInfo,
+            2,
+            2,
+            layout,
+            padLeft,
+            padTop,
+            padRight,
+            padBottom,
+            3,
+            3,
+            biasEnabled
+            );
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T,4> CompareConvolution2dTestImpl(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    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, ArmnnType);
+    outputTensorInfo = armnn::TensorInfo(4, outputShape, ArmnnType);
+    kernelDesc = armnn::TensorInfo(4, kernelShape, ArmnnType);
+    biasDesc = armnn::TensorInfo(1, biasShape, ArmnnType);
+
+    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]);
+
+    ExecuteWorkload(*workload, memoryManager);
+
+    workloadRef->PostAllocationConfigure();
+    workloadRef->Execute();
+
+    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
+    CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get());
+
+    return ret;
+}
+
+//
+// DepthwiseConvolution2d implementations
+//
+
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType,
+         typename T = armnn::ResolveType<ArmnnType>, typename B = armnn::ResolveType<ArmnnBType>>
+LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestImpl(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    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,
+    const armnn::DataLayout 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>(kernel.shape()[0]);
+    unsigned int kernelChannels = boost::numeric_cast<unsigned int>(kernel.shape()[1]);
+    unsigned int kernelHeight   = boost::numeric_cast<unsigned int>(kernel.shape()[2]);
+    unsigned int kernelWidth    = boost::numeric_cast<unsigned int>(kernel.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 =
+            armnnUtils::GetTensorInfo(inputNum, inputChannels, inputHeight, inputWidth, layout, ArmnnType);
+    armnn::TensorInfo outputTensorInfo =
+            armnnUtils::GetTensorInfo(outputNum, outputChannels, outputHeight, outputWidth, layout, ArmnnType);
+    armnn::TensorInfo kernelDesc({kernelChanMul, kernelChannels, kernelHeight, kernelWidth}, ArmnnType);
+    armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, ArmnnBType);
+
+    // 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 == armnn::DataLayout::NHWC)
+    {
+        std::vector<T> tmp(inputData.size());
+        armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(T));
+        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 == armnn::DataLayout::NHWC)
+    {
+        std::vector<T> tmp(outputData.size());
+        armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp.data(), sizeof(T));
+        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);
+
+    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;
+
+    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]);
+
+    ExecuteWorkload(*workload, memoryManager);
+
+    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
+
+    return ret;
+}
+
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 4> DepthwiseConvolution2dDepthMul1TestImpl(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    float qScale,
+    int32_t qOffset,
+    bool biasEnabled,
+    const armnn::DataLayout layout)
+{
+    using B = armnn::ResolveType<ArmnnBType>;
+
+    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 kernelDepthMultiplier = 1;
+
+    unsigned int outputHeight = 1;
+    unsigned int outputWidth = 1;
+    unsigned int outputChannels = kernelChannels;
+    unsigned int outputNum = inputNum;
+
+    armnn::TensorInfo inputTensorInfo =
+            armnnUtils::GetTensorInfo(inputNum, inputChannels, inputHeight, inputWidth, layout, ArmnnType);
+    armnn::TensorInfo outputTensorInfo =
+            armnnUtils::GetTensorInfo(outputNum, outputChannels, outputHeight, outputWidth, layout, ArmnnType);
+    armnn::TensorInfo kernelDesc({kernelDepthMultiplier, kernelChannels, kernelHeight, kernelWidth},
+                                 ArmnnType);
+    armnn::TensorInfo biasDesc({ outputChannels }, ArmnnBType);
+
+    // 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 == armnn::DataLayout::NHWC)
+    {
+        std::vector<T> tmp(inputData.size());
+        armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(T));
+        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,
+            }));
+    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 == armnn::DataLayout::NHWC)
+    {
+        std::vector<T> tmp(outputImage.size());
+        armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputImage.data(), tmp.data(), sizeof(T));
+        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;
+
+    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDepthwiseConvolution2d(data, info);
+    inputHandle->Allocate();
+    outputHandle->Allocate();
+
+    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+
+    ExecuteWorkload(*workload, memoryManager);
+
+    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
+
+    return ret;
+}
+
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    float qScale,
+    int32_t qOffset,
+    bool biasEnabled,
+    const armnn::DataLayout layout)
+{
+    using B = armnn::ResolveType<ArmnnBType>;
+
+    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 = armnnUtils::GetTensorInfo(
+            inputBatchSize, inputChannels, inputHeight, inputWidth, layout, ArmnnType);
+    armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo(
+            outputBatchSize, outputChannels, outputHeight, outputWidth, layout, ArmnnType);
+    armnn::TensorInfo kernelDesc({depthMultiplier, inputChannels, kernelHeight, kernelWidth},
+                                 ArmnnType);
+    armnn::TensorInfo biasDesc({outputChannels}, ArmnnBType);
+
+    // 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 == armnn::DataLayout::NHWC)
+    {
+        armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC,
+                            originalInputData.data(), inputData.data(), sizeof(T));
+    }
+    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> kernelData = 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
+
+            }));
+    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 == armnn::DataLayout::NHWC)
+    {
+        armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC,
+                            originalOutputImage.data(), outputImage.data(), sizeof(T));
+    }
+
+    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;
+
+    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDepthwiseConvolution2d(data, info);
+    inputHandle->Allocate();
+    outputHandle->Allocate();
+
+    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
+
+    ExecuteWorkload(*workload, memoryManager);
+
+    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
+
+    return ret;
+}
+
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType,
+        typename T = armnn::ResolveType<ArmnnType>, typename B = armnn::ResolveType<ArmnnBType>>
+LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    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::DataLayout layout = armnn::DataLayout::NCHW,
+    uint32_t padLeft = 0,
+    uint32_t padTop = 0,
+    uint32_t padRight = 0,
+    uint32_t padBottom = 0,
+    uint32_t strideX = 1,
+    uint32_t strideY = 1,
+    uint32_t dilationX = 1,
+    uint32_t dilationY = 1)
+{
+    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 =
+            armnnUtils::GetTensorInfo(2*inputNum, inputChannels, inputHeight, inputWidth, layout, ArmnnType);
+    armnn::TensorInfo outputTensorInfo =
+            armnnUtils::GetTensorInfo(2*outputNum, outputChannels, outputHeight, outputWidth, layout, ArmnnType);
+
+    // Kernel must be NCHW layout always, independently of the layout of the input and output for depthwise convolution.
+    armnn::TensorInfo kernelDesc({kernelDepthMul, kernelChannels, kernelHeight, kernelWidth}, ArmnnType);
+
+    armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, ArmnnBType);
+
+    // 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
+    std::vector<T> input;
+    input.assign(originalInput.data(), originalInput.data() + 1*inputChannels*inputHeight*inputWidth);
+    std::vector<T> inputData;
+    inputData.insert(inputData.end(), input.begin(), input.end());
+    inputData.insert(inputData.end(), input.begin(), input.end());
+
+    // at this point if we require it permute the input data
+    const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
+    if (layout == armnn::DataLayout::NHWC)
+    {
+        std::vector<T> tmp(inputData.size());
+        armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(T));
+        inputData = tmp;
+    }
+
+    auto batchedInput = MakeTensor<T, 4>(inputTensorInfo, inputData);
+
+    std::vector<T> output;
+    output.assign(originalOutputExpected.data(),
+                       originalOutputExpected.data() + outputChannels*outputHeight*outputWidth);
+
+    // Apply bias to output data if it is enabled.
+    if(biasEnabled)
+    {
+        std::vector<T> biasV;
+        biasV.assign(bias.data(), bias.data() + outputChannels);
+        ApplyBias(output, outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(),
+                  biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(),
+                  outputWidth, outputHeight);
+    }
+
+    // Construct expected output data
+    std::vector<T> outputData;
+    outputData.insert(outputData.end(), output.begin(), output.end());
+    outputData.insert(outputData.end(), output.begin(), output.end());
+
+    // at this point if we require it permute the expected output
+    if (layout == armnn::DataLayout::NHWC)
+    {
+        std::vector<T> tmp(outputData.size());
+        armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp.data(), sizeof(T));
+        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::DepthwiseConvolution2dQueueDescriptor data;
+    armnn::WorkloadInfo info;
+    armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc);
+    armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
+
+    boost::multi_array<T, 4> kernel = boost::multi_array<T, 4>(originalKernel);
+    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;
+    data.m_Parameters.m_DilationX = dilationX;
+    data.m_Parameters.m_DilationY = dilationY;
+
+    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDepthwiseConvolution2d(data, info);
+    inputHandle->Allocate();
+    outputHandle->Allocate();
+
+    CopyDataToITensorHandle(inputHandle.get(), &batchedInput[0][0][0][0]);
+
+    ExecuteWorkload(*workload, memoryManager);
+
+    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
+
+    return ret;
+}
+
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType,
+         typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestCommon(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    float qScale,
+    int32_t qOffset,
+    bool biasEnabled,
+    const armnn::DataLayout layout)
+{
+    // Use a single-batch 2-channel 5x5 image as input.
+    armnn::TensorInfo inputTensorInfo({ 1, 2, 5, 5 }, ArmnnType);
+    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 }, ArmnnType);
+    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 }, ArmnnType);
+    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<ArmnnType, ArmnnBType>(
+        workloadFactory,
+        memoryManager,
+        input,
+        kernel,
+        GetBias2<ArmnnBType>(biasEnabled, qScale * qScale),
+        expectedOutput,
+        qScale,
+        qOffset,
+        layout,
+        1,  // Padding left.
+        1,  // Padding top.
+        2,  // Padding right.
+        2,  // Padding bottom.
+        1,  // strideX
+        1); // strideY
+}
+
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType,
+         typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 4> DepthwiseConvolution2dNhwcTestCommon(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    float qScale,
+    int32_t qOffset,
+    bool biasEnabled)
+{
+    auto layout = armnn::DataLayout::NHWC;
+
+    armnn::TensorInfo inputTensorInfo({ 1, 2, 5, 5}, ArmnnType);
+    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
+        })));
+
+    armnn::TensorInfo kernelTensorInfo({ 1, 2, 4, 4 }, ArmnnType);
+    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
+        })));
+
+    armnn::TensorInfo outputTensorInfo({ 1, 2, 5, 5}, ArmnnType);
+    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 DepthwiseConvolution2dTestImpl<ArmnnType, ArmnnBType>(
+        workloadFactory,
+        memoryManager,
+        input,
+        kernel,
+        GetBias2<ArmnnBType>(biasEnabled, qScale * qScale),
+        expectedOutput,
+        qScale,
+        qOffset,
+        layout,
+        1,  // Padding left.
+        1,  // Padding top.
+        2,  // Padding right.
+        2,  // Padding bottom.
+        1,  // strideX
+        1);  // strideY
+}
+
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType,
+         typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 4> SimpleDepthwiseConvolution2d3x3Dilation3x3NhwcTestCommon(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    float qScale,
+    int32_t qOffset,
+    bool biasEnabled)
+{
+    auto layout = armnn::DataLayout::NHWC;
+
+    armnn::TensorInfo inputTensorInfo({ 1, 1, 9, 9}, ArmnnType);
+    auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>(
+        QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(),
+        {
+             0, 0, 0, 0, 0, 0, 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, 1, 1, 0, 0, 0,
+             0, 0, 0, 1, 1, 1, 0, 0, 0,
+             0, 0, 0, 1, 1, 1, 0, 0, 0,
+             0, 0, 0, 0, 0, 0, 0, 0, 0,
+             0, 0, 0, 0, 0, 0, 0, 0, 0,
+             0, 0, 0, 0, 0, 0, 0, 0, 0
+        })));
+
+    armnn::TensorInfo kernelTensorInfo({ 1, 1, 3, 3}, ArmnnType);
+    auto kernel = MakeTensor<T, 4>(kernelTensorInfo, std::vector<T>(
+        QuantizedVector<T>(kernelTensorInfo.GetQuantizationScale(), kernelTensorInfo.GetQuantizationOffset(),
+        {
+             1, 2, 3,
+             4, 5, 6,
+             7, 8, 9
+        })));
+
+    uint32_t padLeft = 0;
+    uint32_t padTop = 0;
+    uint32_t padRight = 0;
+    uint32_t padBottom = 0;
+    uint32_t strideX  = 1;
+    uint32_t strideY  = 1;
+    uint32_t dilationX  = 3;
+    uint32_t dilationY  = 3;
+
+    // Since the dilation rate is 3 this will reduce the size of the output from 9x9 to 3x3 of all 5s.
+    armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3}, ArmnnType);
+    boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputTensorInfo, std::vector<T>(
+        QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(),
+        {
+             5, 5, 5,
+             5, 5, 5,
+             5, 5, 5
+        })));
+
+    return DepthwiseConvolution2dTestImpl<ArmnnType, ArmnnBType>(
+        workloadFactory,
+        memoryManager,
+        input,
+        kernel,
+        GetBias2<ArmnnBType>(biasEnabled, qScale * qScale),
+        expectedOutput,
+        qScale,
+        qOffset,
+        layout,
+        padLeft,
+        padTop,
+        padRight,
+        padBottom,
+        strideX,
+        strideY,
+        dilationX,
+        dilationY);
+}
+
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 4> DepthwiseConvolution2d3x3DilationTestCommon(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+        const std::vector<float>& inputNoQuantizedValues,
+        armnn::TensorInfo& inputTensorInfo,
+        const std::vector<float>& kernelNoQuantizedValues,
+        armnn::TensorInfo& kernelTensorInfo,
+        const std::vector<float>& outputExpectedNoQuantizedValues,
+        armnn::TensorInfo& outputTensorInfo,
+        uint32_t dilationX,
+        uint32_t dilationY,
+        armnn::DataLayout layout = armnn::DataLayout::NCHW,
+        bool biasEnabled = false)
+{
+    float qScale;
+    int32_t qOffset;
+    switch (ArmnnType)
+    {
+        case armnn::DataType::QuantisedAsymm8:
+        {
+            qScale = 0.1f;
+            qOffset = 128;
+            break;
+        }
+        case armnn::DataType::QuantisedSymm16:
+        {
+            qScale = 0.1f;
+            qOffset = 0;
+            break;
+        }
+        case armnn::DataType::Float32:
+        default:
+        {
+            qScale = 0.f;
+            qOffset = 0;
+            break;
+        }
+    }
+
+    inputTensorInfo.SetQuantizationScale(qScale);
+    inputTensorInfo.SetQuantizationOffset(qOffset);
+    kernelTensorInfo.SetQuantizationScale(qScale);
+    kernelTensorInfo.SetQuantizationOffset(qOffset);
+    outputTensorInfo.SetQuantizationScale(qScale);
+    outputTensorInfo.SetQuantizationOffset(qOffset);
+
+    auto input = MakeTensor<T, 4>(inputTensorInfo,
+                                  std::vector<T>(QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(),
+                                                                    inputTensorInfo.GetQuantizationOffset(),
+                                                                    inputNoQuantizedValues)));
+    auto kernel = MakeTensor<T, 4>(kernelTensorInfo,
+                                   std::vector<T>(QuantizedVector<T>(kernelTensorInfo.GetQuantizationScale(),
+                                                                     kernelTensorInfo.GetQuantizationOffset(),
+                                                                     kernelNoQuantizedValues)));
+    auto expectedOutput = MakeTensor<T, 4>(outputTensorInfo,
+                                           std::vector<T>(QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(),
+                                                                             outputTensorInfo.GetQuantizationOffset(),
+                                                                             outputExpectedNoQuantizedValues)));
+
+    uint32_t padLeft = 0;
+    uint32_t padTop = 0;
+    uint32_t padRight = 0;
+    uint32_t padBottom = 0;
+    uint32_t strideX  = 1;
+    uint32_t strideY  = 1;
+
+    return DepthwiseConvolution2dTestImpl<ArmnnType, ArmnnBType>(
+            workloadFactory,
+            memoryManager,
+            input,
+            kernel,
+            GetBias<ArmnnBType>(biasEnabled, qScale * qScale, outputTensorInfo, layout),
+            expectedOutput,
+            qScale,
+            qOffset,
+            layout,
+            padLeft,
+            padTop,
+            padRight,
+            padBottom,
+            strideX,
+            strideY,
+            dilationX,
+            dilationY);
+}
+
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T>
+LayerTestResult<T, 4> DepthwiseConvolution2d3x3Dilation3x3Test(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+        bool biasEnabled,
+        const armnn::DataLayout layout)
+{
+    armnn::TensorInfo inputTensorInfo({1, 1, 10, 10}, ArmnnType);
+    std::vector<float> inputNoQuantizedValues =
+            {
+                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                    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, 1, 1, 0, 0, 0,
+                    0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
+                    0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
+                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0
+            };
+
+    armnn::TensorInfo kernelTensorInfo({ 1, 1, 3, 3}, ArmnnType);
+    std::vector<float> kernelNoQuantizedValues =
+            {
+                    1, 2, 3,
+                    4, 5, 6,
+                    7, 8, 9
+            };
+
+    // Since the dilation rate is 3 this will dilate the kernel to be like 7x7,
+    // therefore the output will be 4x4: (I−K+2P)/S +1 => (10-7 +0)/1 +1
+    armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4}, ArmnnType);
+    std::vector<float> outputExpectedNoQuantizedValues =
+            {
+                    6., 5., 5., 5.,
+                    6., 5., 5., 5.,
+                    6., 5., 5., 5.,
+                    3., 2., 2., 2.
+            };
+
+    return DepthwiseConvolution2d3x3DilationTestCommon<ArmnnType, ArmnnBType>(
+            workloadFactory,
+            memoryManager,
+            inputNoQuantizedValues,
+            inputTensorInfo,
+            kernelNoQuantizedValues,
+            kernelTensorInfo,
+            outputExpectedNoQuantizedValues,
+            outputTensorInfo,
+            3,
+            3,
+            layout,
+            biasEnabled);
+}
+
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T>
+LayerTestResult<T, 4> DepthwiseConvolution2d2x3x3Dilation3x3Test(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+        bool biasEnabled,
+        const armnn::DataLayout layout)
+{
+    armnn::TensorInfo inputTensorInfo({1, 2, 10, 10}, ArmnnType);
+    std::vector<float> inputNoQuantizedValues =
+            {
+                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                    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, 1, 1, 0, 0, 0,
+                    0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
+                    0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
+                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+
+                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                    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, 1, 1, 0, 0, 0,
+                    0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
+                    0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
+                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                    0, 0, 0, 0, 0, 0, 0, 0, 0, 0
+            };
+
+    armnn::TensorInfo kernelTensorInfo({ 1, 2, 3, 3}, ArmnnType);
+    std::vector<float> kernelNoQuantizedValues =
+            {
+                    1, 2, 3,
+                    4, 5, 6,
+                    7, 8, 9,
+
+                    1, 2, 3,
+                    4, 5, 6,
+                    7, 8, 9
+            };
+
+    // Since the dilation rate is 3 this will dilate the kernel to be like 7x7,
+    // therefore the output will be 2x4x4: (I−K+2P)/S +1 => (10-7 +0)/1 +1
+    armnn::TensorInfo outputTensorInfo({ 1, 2, 4, 4}, ArmnnType);
+    std::vector<float> outputExpectedNoQuantizedValues =
+            {
+                    6., 5., 5., 5.,
+                    6., 5., 5., 5.,
+                    6., 5., 5., 5.,
+                    3., 2., 2., 2.,
+
+                    6., 5., 5., 5.,
+                    6., 5., 5., 5.,
+                    6., 5., 5., 5.,
+                    3., 2., 2., 2.
+            };
+
+    return DepthwiseConvolution2d3x3DilationTestCommon<ArmnnType, ArmnnBType>(
+            workloadFactory,
+            memoryManager,
+            inputNoQuantizedValues,
+            inputTensorInfo,
+            kernelNoQuantizedValues,
+            kernelTensorInfo,
+            outputExpectedNoQuantizedValues,
+            outputTensorInfo,
+            3,
+            3,
+            layout,
+            biasEnabled);
+}
+
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T>
+LayerTestResult<T, 4> DepthwiseConvolution2dMult4Test(
+            armnn::IWorkloadFactory& workloadFactory,
+            const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+            bool biasEnabled,
+            const armnn::DataLayout layout)
+{
+    armnn::TensorInfo inputTensorInfo({1, 2, 3, 3}, ArmnnType);
+    std::vector<float> inputNoQuantizedValues =
+            {
+                    10.0, 10.0, 10.0,
+                    10.0, 10.0, 10.0,
+                    10.0, 10.0, 10.0,
+
+                    21.0, 22.0, 23.0,
+                    24.0, 25.0, 26.0,
+                    27.0, 28.0, 29.0
+            };
+
+    armnn::TensorInfo kernelTensorInfo({ 4, 2, 2, 2}, ArmnnType);
+
+    std::vector<float> kernelNoQuantizedValues =
+            {
+                    0.25f, 0.25f,
+                    0.25f, 0.25f,
+
+                    0.25f, 0.25f,
+                    0.25f, 0.25f,
+
+                    0.0f , 0.0f,
+                    0.0f , 0.1f,
+
+                    0.0f , 0.0f,
+                    0.0f , 0.1f,
+
+                    0.2f , 0.0f,
+                    0.0f , 0.0f,
+
+                    0.2f , 0.0f,
+                    0.0f , 0.0f,
+
+                    0.0f , 0.3f,
+                    0.0f , 0.0f,
+
+                    0.0f , 0.3f,
+                    0.0f , 0.0f
+            };
+
+    armnn::TensorInfo outputTensorInfo({ 1, 8, 2, 2}, ArmnnType);
+    std::vector<float> outputExpectedNoQuantizedValues =
+            {
+                    10.f, 10.f,
+                    10.f, 10.f,
+
+                    1.f, 1.f,
+                    1.f, 1.f,
+
+                    2.f, 2.f,
+                    2.f, 2.f,
+
+                    3.f, 3.f,
+                    3.f, 3.f,
+
+                    23.f, 24.f,
+                    26.f, 27.f,
+
+                    2.5f, 2.6000001f,
+                    2.8f, 2.9f,
+
+                    4.2000003f, 4.4f,
+                    4.8f, 5.f,
+
+                    6.6000004f, 6.9f,
+                    7.5000005f, 7.8f
+            };
+
+
+    return DepthwiseConvolution2d3x3DilationTestCommon<ArmnnType, ArmnnBType>(
+            workloadFactory,
+            memoryManager,
+            inputNoQuantizedValues,
+            inputTensorInfo,
+            kernelNoQuantizedValues,
+            kernelTensorInfo,
+            outputExpectedNoQuantizedValues,
+            outputTensorInfo,
+            1,
+            1,
+            layout,
+            biasEnabled);
+}
+
+template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T>
+LayerTestResult<T, 4> DepthwiseConvolution2dMult2Test(
+            armnn::IWorkloadFactory& workloadFactory,
+            const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+            bool biasEnabled,
+            const armnn::DataLayout layout)
+{
+    armnn::TensorInfo inputTensorInfo({1, 2, 3, 3}, ArmnnType);
+    std::vector<float> inputNoQuantizedValues =
+            {
+                    10.0, 10.0, 10.0,
+                    10.0, 10.0, 10.0,
+                    10.0, 10.0, 10.0,
+
+                    21.0, 22.0, 23.0,
+                    24.0, 25.0, 26.0,
+                    27.0, 28.0, 29.0
+            };
+
+    armnn::TensorInfo kernelTensorInfo({ 2, 2, 2, 2}, ArmnnType);
+
+    std::vector<float> kernelNoQuantizedValues =
+            {
+                    0.25f, 0.25f,
+                    0.25f, 0.25f,
+
+                    0.2f , 0.0f,
+                    0.0f , 0.0f,
+
+                    0.0f , 0.0f,
+                    0.0f , 0.1f,
+
+                    0.0f , 0.3f,
+                    0.0f , 0.0f
+
+            };
+
+    armnn::TensorInfo outputTensorInfo({ 1, 4, 2, 2}, ArmnnType);
+    std::vector<float> outputExpectedNoQuantizedValues =
+            {
+                    10.f, 10.f,
+                    10.f, 10.f,
+
+                    1.f, 1.f,
+                    1.f, 1.f,
+
+                    4.2000003f, 4.4f,
+                    4.8f, 5.f,
+
+                    6.6000004f, 6.9f,
+                    7.5000005f, 7.8f
+            };
+
+
+    return DepthwiseConvolution2d3x3DilationTestCommon<ArmnnType, ArmnnBType>(
+            workloadFactory,
+            memoryManager,
+            inputNoQuantizedValues,
+            inputTensorInfo,
+            kernelNoQuantizedValues,
+            kernelTensorInfo,
+            outputExpectedNoQuantizedValues,
+            outputTensorInfo,
+            1,
+            1,
+            layout,
+            biasEnabled);
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 4> CompareDepthwiseConvolution2dTestImpl(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    armnn::IWorkloadFactory& refWorkloadFactory,
+    const armnnUtils::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{ channelMultiplier, inputChannels, kernelHeight, kernelWidth };
+    std::vector<unsigned int> biasShape{ outputChannels };
+    switch (layout.GetDataLayout())
+    {
+        case armnn::DataLayout::NCHW:
+            inputShape =  { inputNum, inputChannels, inputHeight, inputWidth };
+            outputShape = { outputNum, outputChannels, outputHeight, outputWidth };
+            break;
+        case armnn::DataLayout ::NHWC:
+            inputShape =  { inputNum, inputHeight, inputWidth, inputChannels };
+            outputShape = { outputNum, outputHeight, outputWidth, outputChannels };
+            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(), ArmnnType, inputsQScale, qOffset);
+    outputTensorInfo = armnn::TensorInfo(4, outputShape.data(), ArmnnType, outputQScale, qOffset);
+    kernelDesc = armnn::TensorInfo(4, kernelShape.data(), ArmnnType, inputsQScale, qOffset);
+    biasDesc = armnn::TensorInfo(
+        1, biasShape.data(), armnn::GetBiasDataType(ArmnnType), 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]);
+
+    ExecuteWorkload(*workload, memoryManager);
+
+    workloadRef->PostAllocationConfigure();
+    workloadRef->Execute();
+
+    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
+    CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get());
+
+    return ret;
+}
+
+//
+// Explicit template specializations
+//
+
+template LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4>
+Convolution2d3x3Dilation3x3Test<armnn::DataType::Float32, armnn::DataType::Float32>(
+    armnn::IWorkloadFactory&,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr&,
+    bool,
+    armnn::DataLayout);
+
+template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedAsymm8>, 4>
+Convolution2d3x3Dilation3x3Test<armnn::DataType::QuantisedAsymm8, armnn::DataType::Signed32>(
+    armnn::IWorkloadFactory&,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr&,
+    bool,
+    armnn::DataLayout);
+
+template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedSymm16>, 4>
+Convolution2d3x3Dilation3x3Test<armnn::DataType::QuantisedSymm16, armnn::DataType::Signed32>(
+    armnn::IWorkloadFactory&,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr&,
+    bool,
+    armnn::DataLayout);
+
+template LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4>
+Convolution2d2x3x3Dilation3x3Test<armnn::DataType::Float32, armnn::DataType::Float32>(
+    armnn::IWorkloadFactory&,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr&,
+    bool,
+    armnn::DataLayout);
+
+template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedAsymm8>, 4>
+Convolution2d2x3x3Dilation3x3Test<armnn::DataType::QuantisedAsymm8, armnn::DataType::Signed32>(
+    armnn::IWorkloadFactory&,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr&,
+    bool,
+    armnn::DataLayout);
+
+template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedSymm16>, 4>
+Convolution2d2x3x3Dilation3x3Test<armnn::DataType::QuantisedSymm16, armnn::DataType::Signed32>(
+    armnn::IWorkloadFactory&,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr&,
+    bool,
+    armnn::DataLayout);
+
+template LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4>
+Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test<armnn::DataType::Float32, armnn::DataType::Float32>(
+    armnn::IWorkloadFactory &workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager,
+    bool biasEnabled,
+    const armnn::DataLayout layout);
+
+template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedAsymm8>, 4>
+Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test<armnn::DataType::QuantisedAsymm8, armnn::DataType::Signed32>(
+    armnn::IWorkloadFactory &workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager,
+    bool biasEnabled,
+    const armnn::DataLayout layout);
+
+template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedSymm16>, 4>
+Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test<armnn::DataType::QuantisedSymm16, armnn::DataType::Signed32>(
+    armnn::IWorkloadFactory &workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager,
+    bool biasEnabled,
+    const armnn::DataLayout layout);
+
+template LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4>
+DepthwiseConvolution2d3x3Dilation3x3Test<armnn::DataType::Float32, armnn::DataType::Float32>(
+        armnn::IWorkloadFactory&,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr&,
+        bool,
+        armnn::DataLayout);
+
+template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedAsymm8>, 4>
+DepthwiseConvolution2d3x3Dilation3x3Test<armnn::DataType::QuantisedAsymm8, armnn::DataType::Signed32>(
+        armnn::IWorkloadFactory&,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr&,
+        bool,
+        armnn::DataLayout);
+
+template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedSymm16>, 4>
+DepthwiseConvolution2d3x3Dilation3x3Test<armnn::DataType::QuantisedSymm16, armnn::DataType::Signed32>(
+        armnn::IWorkloadFactory&,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr&,
+        bool,
+        armnn::DataLayout);
+
+template LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4>
+DepthwiseConvolution2d2x3x3Dilation3x3Test<armnn::DataType::Float32, armnn::DataType::Float32>(
+        armnn::IWorkloadFactory&,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr&,
+        bool,
+        armnn::DataLayout);
+
+template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedAsymm8>, 4>
+DepthwiseConvolution2d2x3x3Dilation3x3Test<armnn::DataType::QuantisedAsymm8, armnn::DataType::Signed32>(
+        armnn::IWorkloadFactory&,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr&,
+        bool,
+        armnn::DataLayout);
+
+template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedSymm16>, 4>
+DepthwiseConvolution2d2x3x3Dilation3x3Test<armnn::DataType::QuantisedSymm16, armnn::DataType::Signed32>(
+        armnn::IWorkloadFactory&,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr&,
+        bool,
+        armnn::DataLayout);
+
+template LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4>
+DepthwiseConvolution2dMult4Test<armnn::DataType::Float32, armnn::DataType::Float32>(
+        armnn::IWorkloadFactory &workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager,
+        bool biasEnabled,
+        const armnn::DataLayout layout);
+
+template LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4>
+DepthwiseConvolution2dMult2Test<armnn::DataType::Float32, armnn::DataType::Float32>(
+        armnn::IWorkloadFactory &workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager,
+        bool biasEnabled,
+        const armnn::DataLayout layout);
+
+//
+// Implementation functions
+//
+
+LayerTestResult<float, 4> SimpleConvolution2d3x5Test(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    bool biasEnabled,
+    const armnn::DataLayout layout)
+{
+    return SimpleConvolution2d3x5TestCommon<armnn::DataType::Float32, armnn::DataType::Float32>(
+        workloadFactory, memoryManager, 0.f, 0, biasEnabled, layout);
+}
+
+LayerTestResult<uint8_t, 4> SimpleConvolution2d3x5Uint8Test(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    bool biasEnabled,
+    const armnn::DataLayout layout)
+{
+    return SimpleConvolution2d3x5TestCommon<armnn::DataType::QuantisedAsymm8, armnn::DataType::Signed32>(
+        workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);
+}
+
+LayerTestResult<float, 4> SimpleConvolution2d3x3Test(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    bool biasEnabled,
+    const armnn::DataLayout layout)
+{
+    return SimpleConvolution2d3x3TestCommon<armnn::DataType::Float32, armnn::DataType::Float32>(
+        workloadFactory, memoryManager, 0.f, 0, biasEnabled, layout);
+}
+
+LayerTestResult<float, 4> SimpleConvolution2d3x3NhwcTest(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    bool biasEnabled)
+{
+    return SimpleConvolution2d3x3NhwcTestCommon<armnn::DataType::Float32>(
+        workloadFactory,
+        memoryManager,
+        0.f,
+        0,
+        biasEnabled,
+        armnn::DataLayout::NHWC);
+}
+
+LayerTestResult<float, 4> SimpleConvolution2d3x3Stride2x2Test(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+        bool biasEnabled,
+        const armnn::DataLayout layout)
+{
+    return SimpleConvolution2d3x3Stride2x2TestCommon<armnn::DataType::Float32>(
+        workloadFactory,
+        memoryManager,
+        0.f,
+        0,
+        biasEnabled,
+        layout);
+}
+
+LayerTestResult<uint8_t, 4> SimpleConvolution2d3x3Uint8Test(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    bool biasEnabled,
+    const armnn::DataLayout layout)
+{
+    return SimpleConvolution2d3x3TestCommon<armnn::DataType::QuantisedAsymm8, armnn::DataType::Signed32>(
+        workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);
+}
+
+LayerTestResult<int16_t, 4> SimpleConvolution2d3x5QSymm16Test(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    bool biasEnabled,
+    const armnn::DataLayout layout)
+{
+    return SimpleConvolution2d3x5TestCommon<armnn::DataType::QuantisedSymm16, armnn::DataType::Signed32>(
+        workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);
+}
+
+LayerTestResult<int16_t, 4> SimpleConvolution2d3x3QSymm16Test(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    bool biasEnabled,
+    const armnn::DataLayout layout)
+{
+    return SimpleConvolution2d3x3TestCommon<armnn::DataType::QuantisedSymm16, armnn::DataType::Signed32>(
+            workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);
+}
+
+LayerTestResult<float, 4> Convolution2dAsymmetricPaddingTest(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    armnn::DataLayout layout)
+{
+    return SimpleConvolution2dAsymmetricPaddingTestCommon<armnn::DataType::Float32, armnn::DataType::Float32>(
+            workloadFactory, memoryManager, layout, 0.0f, 0);
+}
+
+LayerTestResult<float, 4> Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    armnn::DataLayout layout)
+{
+    return Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon
+            <armnn::DataType::Float32, armnn::DataType::Float32>(
+            workloadFactory, memoryManager, layout, 0.0f, 0);
+}
+
+LayerTestResult<float, 4> Convolution1dTest(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    bool biasEnabled)
+{
+    return Convolution1dTestImpl<armnn::DataType::Float32, armnn::DataType::Float32>(
+            workloadFactory, memoryManager, 0.0f, 0, biasEnabled);
+}
+
+LayerTestResult<uint8_t, 4> Convolution1dUint8Test(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    bool biasEnabled)
+{
+    return Convolution1dTestImpl<armnn::DataType::QuantisedAsymm8, armnn::DataType::Signed32>(
+            workloadFactory, memoryManager, 0.1f, 128, biasEnabled);
+}
+
+LayerTestResult<float,4> CompareConvolution2dTest(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    armnn::IWorkloadFactory& refWorkloadFactory)
+{
+    return CompareConvolution2dTestImpl<armnn::DataType::Float32>(
+            workloadFactory, memoryManager, refWorkloadFactory);
+}
+
+LayerTestResult<float, 4> DepthwiseConvolution2dTest(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    bool biasEnabled,
+    const armnn::DataLayout layout)
+{
+    return DepthwiseConvolution2dTestImpl<armnn::DataType::Float32, armnn::DataType::Float32>(
+        workloadFactory, memoryManager, 0.0f, 0, biasEnabled, layout);
+}
+
+LayerTestResult<float, 4> DepthwiseConvolution2dDepthNhwcTest(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    bool biasEnabled)
+{
+    return DepthwiseConvolution2dNhwcTestCommon<armnn::DataType::Float32, armnn::DataType::Float32>(
+        workloadFactory, memoryManager, 0.0f, 0, biasEnabled);
+}
+
+LayerTestResult<float, 4> DepthwiseConvolution2dDepthMul1Test(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    bool biasEnabled,
+    const armnn::DataLayout layout)
+{
+    return DepthwiseConvolution2dDepthMul1TestImpl<armnn::DataType::Float32, armnn::DataType::Float32>(
+        workloadFactory, memoryManager, 0.0f, 0, biasEnabled, layout);
+}
+
+LayerTestResult<float, 4> DepthwiseConvolution2dDepthMul64Test(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    armnn::TensorInfo inputTensorInfo({ 1, 1, 2, 2 }, armnn::DataType::Float32);
+    auto input = MakeTensor<float, 4>(inputTensorInfo, { 1.f, 2.f, 3.f, 4.f });
+
+    std::vector<float> kernelData;
+    std::vector<float> singleDepthKernel{ 1.f, -1.f, -1.f, 1.f };
+    for (unsigned int i = 0; i < 64; ++i)
+    {
+        kernelData.insert(kernelData.end(), singleDepthKernel.begin(), singleDepthKernel.end());
+    }
+    armnn::TensorInfo kernelTensorInfo({ 64, 1, 2, 2 }, armnn::DataType::Float32);
+    auto kernel = MakeTensor<float, 4>(kernelTensorInfo, kernelData);
+
+    std::vector<float> expectedOutputData(64, 0.f);
+    armnn::TensorInfo outputTensorInfo({ 1, 64, 1, 1 }, armnn::DataType::Float32);
+    auto expectedOutput = MakeTensor<float, 4>(outputTensorInfo, expectedOutputData);
+
+    return DepthwiseConvolution2dTestImpl<armnn::DataType::Float32, armnn::DataType::Float32>(
+            workloadFactory,
+            memoryManager,
+            input,
+            kernel,
+            boost::multi_array<float, 1>(),
+            expectedOutput,
+            0.f,
+            0,
+            armnn::DataLayout::NCHW);
+}
+
+LayerTestResult<float, 4> DepthwiseConvolution2dAsymmetricTest(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    bool biasEnabled,
+    const armnn::DataLayout layout)
+{
+    return DepthwiseConvolution2dAsymmetricTestCommon<armnn::DataType::Float32, armnn::DataType::Float32>(
+        workloadFactory, memoryManager, 0.0f, 0, biasEnabled, layout);
+}
+
+LayerTestResult<uint8_t, 4> DepthwiseConvolution2dUint8Test(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    bool biasEnabled,
+    const armnn::DataLayout layout)
+{
+    return DepthwiseConvolution2dTestImpl<armnn::DataType::QuantisedAsymm8, armnn::DataType::Signed32>(
+        workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);
+}
+
+LayerTestResult<uint8_t, 4> DepthwiseConvolution2dDepthMul1Uint8Test(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    bool biasEnabled,
+    const armnn::DataLayout layout)
+{
+    return DepthwiseConvolution2dDepthMul1TestImpl<armnn::DataType::QuantisedAsymm8, armnn::DataType::Signed32>(
+        workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);
+}
+
+LayerTestResult<float, 4> SimpleDepthwiseConvolution2d3x3Dilation3x3NhwcTest(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return SimpleDepthwiseConvolution2d3x3Dilation3x3NhwcTestCommon<armnn::DataType::Float32, armnn::DataType::Float32>(
+            workloadFactory,
+            memoryManager,
+            0.f,
+            0,
+            false);
+}
+
+LayerTestResult<int16_t, 4> DepthwiseConvolution2dInt16Test(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+        bool biasEnabled,
+        const armnn::DataLayout layout)
+{
+    return DepthwiseConvolution2dTestImpl<armnn::DataType::QuantisedSymm16, armnn::DataType::Signed32>(
+        workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);
+}
+
+LayerTestResult<int16_t, 4> DepthwiseConvolution2dDepthMul1Int16Test(
+                armnn::IWorkloadFactory& workloadFactory,
+                const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+                bool biasEnabled,
+                const armnn::DataLayout layout)
+{
+    return DepthwiseConvolution2dDepthMul1TestImpl<armnn::DataType::QuantisedSymm16, armnn::DataType::Signed32>(
+        workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);
+}
+
+LayerTestResult<float, 4> CompareDepthwiseConvolution2dFloatTest(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    armnn::IWorkloadFactory& refWorkloadFactory,
+    const armnn::DataLayout layout)
+{
+    return CompareDepthwiseConvolution2dTestImpl<armnn::DataType::Float32>(
+        workloadFactory, memoryManager, refWorkloadFactory, layout);
+}
+
+LayerTestResult<uint8_t, 4> CompareDepthwiseConvolution2dUint8Test(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    armnn::IWorkloadFactory& refWorkloadFactory,
+    const armnn::DataLayout layout)
+{
+    return CompareDepthwiseConvolution2dTestImpl<armnn::DataType::QuantisedAsymm8>(
+        workloadFactory, memoryManager, refWorkloadFactory, layout);
+}