From b454c5c65efb238c130b042ace390b2bc7f0bf75 Mon Sep 17 00:00:00 2001
From: Finn Williams <Finn.Williams@arm.com>
Date: Tue, 9 Feb 2021 15:56:23 +0000
Subject: IVGCVSW-4893 Refactor ILayerVisitor using unified interface strategy.

Signed-off-by: Jan Eilers <jan.eilers@arm.com>
Signed-off-by: Finn Williams <Finn.Williams@arm.com>
Signed-off-by: Francis Murtagh <francis.murtagh@arm.com>
Change-Id: Id7bc8255a8e3f9e5aac65d510bec8a559bf37246
---
 .../test/ActivationSerializationTests.cpp          |   19 +-
 .../test/ComparisonSerializationTests.cpp          |  123 +
 .../test/LstmSerializationTests.cpp                | 2199 +++++++++++++
 src/armnnSerializer/test/SerializerTestUtils.cpp   |  163 +
 src/armnnSerializer/test/SerializerTestUtils.hpp   |  167 +
 src/armnnSerializer/test/SerializerTests.cpp       | 3461 ++------------------
 6 files changed, 3008 insertions(+), 3124 deletions(-)
 create mode 100644 src/armnnSerializer/test/ComparisonSerializationTests.cpp
 create mode 100644 src/armnnSerializer/test/LstmSerializationTests.cpp
 create mode 100644 src/armnnSerializer/test/SerializerTestUtils.cpp
 create mode 100644 src/armnnSerializer/test/SerializerTestUtils.hpp

(limited to 'src/armnnSerializer/test')

diff --git a/src/armnnSerializer/test/ActivationSerializationTests.cpp b/src/armnnSerializer/test/ActivationSerializationTests.cpp
index 1645731413..fbe1ae0ad4 100644
--- a/src/armnnSerializer/test/ActivationSerializationTests.cpp
+++ b/src/armnnSerializer/test/ActivationSerializationTests.cpp
@@ -17,15 +17,20 @@
 
 BOOST_AUTO_TEST_SUITE(SerializerTests)
 
-class VerifyActivationName : public armnn::LayerVisitorBase<armnn::VisitorNoThrowPolicy>
+class VerifyActivationName : public armnn::IStrategy
 {
 public:
-    void VisitActivationLayer(const armnn::IConnectableLayer* layer,
-                              const armnn::ActivationDescriptor& activationDescriptor,
-                              const char* name) override
+    void ExecuteStrategy(const armnn::IConnectableLayer* layer,
+                         const armnn::BaseDescriptor& descriptor,
+                         const std::vector<armnn::ConstTensor>& constants,
+                         const char* name,
+                         const armnn::LayerBindingId id = 0) override
     {
-        IgnoreUnused(layer, activationDescriptor);
-        BOOST_TEST(name == "activation");
+        IgnoreUnused(layer, descriptor, constants, id);
+        if (layer->GetType() == armnn::LayerType::Activation)
+        {
+            BOOST_TEST(name == "activation");
+        }
     }
 };
 
@@ -67,7 +72,7 @@ BOOST_AUTO_TEST_CASE(ActivationSerialization)
     armnn::INetworkPtr deserializedNetwork = parser->CreateNetworkFromBinary(serializerVector);
 
     VerifyActivationName visitor;
-    deserializedNetwork->Accept(visitor);
+    deserializedNetwork->ExecuteStrategy(visitor);
 
     armnn::IRuntime::CreationOptions options; // default options
     armnn::IRuntimePtr run = armnn::IRuntime::Create(options);
diff --git a/src/armnnSerializer/test/ComparisonSerializationTests.cpp b/src/armnnSerializer/test/ComparisonSerializationTests.cpp
new file mode 100644
index 0000000000..3aee9a7bcb
--- /dev/null
+++ b/src/armnnSerializer/test/ComparisonSerializationTests.cpp
@@ -0,0 +1,123 @@
+//
+// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "../Serializer.hpp"
+#include "SerializerTestUtils.hpp"
+
+#include <armnn/Descriptors.hpp>
+#include <armnn/INetwork.hpp>
+#include <armnn/IRuntime.hpp>
+#include <armnnDeserializer/IDeserializer.hpp>
+#include <armnn/utility/IgnoreUnused.hpp>
+
+#include <boost/test/unit_test.hpp>
+
+
+BOOST_AUTO_TEST_SUITE(SerializerTests)
+
+struct ComparisonModel
+{
+    ComparisonModel(const std::string& layerName,
+                    const armnn::TensorInfo& inputInfo,
+                    const armnn::TensorInfo& outputInfo,
+                    armnn::ComparisonDescriptor& descriptor)
+            : m_network(armnn::INetwork::Create())
+    {
+        armnn::IConnectableLayer* const inputLayer0 = m_network->AddInputLayer(0);
+        armnn::IConnectableLayer* const inputLayer1 = m_network->AddInputLayer(1);
+        armnn::IConnectableLayer* const equalLayer = m_network->AddComparisonLayer(descriptor, layerName.c_str());
+        armnn::IConnectableLayer* const outputLayer = m_network->AddOutputLayer(0);
+
+        inputLayer0->GetOutputSlot(0).Connect(equalLayer->GetInputSlot(0));
+        inputLayer1->GetOutputSlot(0).Connect(equalLayer->GetInputSlot(1));
+        equalLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
+
+        inputLayer0->GetOutputSlot(0).SetTensorInfo(inputInfo);
+        inputLayer1->GetOutputSlot(0).SetTensorInfo(inputInfo);
+        equalLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
+    }
+
+    armnn::INetworkPtr m_network;
+};
+
+class ComparisonLayerVerifier : public LayerVerifierBase
+{
+public:
+    ComparisonLayerVerifier(const std::string& layerName,
+                            const std::vector<armnn::TensorInfo>& inputInfos,
+                            const std::vector<armnn::TensorInfo>& outputInfos,
+                            const armnn::ComparisonDescriptor& descriptor)
+            : LayerVerifierBase(layerName, inputInfos, outputInfos)
+            , m_Descriptor (descriptor) {}
+
+    void ExecuteStrategy(const armnn::IConnectableLayer* layer,
+                         const armnn::BaseDescriptor& descriptor,
+                         const std::vector<armnn::ConstTensor>& constants,
+                         const char* name,
+                         const armnn::LayerBindingId id = 0) override
+    {
+        armnn::IgnoreUnused(descriptor, constants, id);
+        switch (layer->GetType())
+        {
+            case armnn::LayerType::Input: break;
+            case armnn::LayerType::Output: break;
+            case armnn::LayerType::Comparison:
+            {
+                VerifyNameAndConnections(layer, name);
+                const armnn::ComparisonDescriptor& layerDescriptor =
+                        static_cast<const armnn::ComparisonDescriptor&>(descriptor);
+                BOOST_CHECK(layerDescriptor.m_Operation == m_Descriptor.m_Operation);
+                break;
+            }
+            default:
+            {
+                throw armnn::Exception("Unexpected layer type in Comparison test model");
+            }
+        }
+    }
+
+private:
+    armnn::ComparisonDescriptor m_Descriptor;
+};
+
+BOOST_AUTO_TEST_CASE(SerializeEqual)
+{
+    const std::string layerName("equal");
+
+    const armnn::TensorShape shape{2, 1, 2, 4};
+    const armnn::TensorInfo inputInfo  = armnn::TensorInfo(shape, armnn::DataType::Float32);
+    const armnn::TensorInfo outputInfo = armnn::TensorInfo(shape, armnn::DataType::Boolean);
+
+    armnn::ComparisonDescriptor descriptor (armnn::ComparisonOperation::Equal);
+
+    ComparisonModel model(layerName, inputInfo, outputInfo, descriptor);
+
+    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*model.m_network));
+    BOOST_CHECK(deserializedNetwork);
+
+    ComparisonLayerVerifier verifier(layerName, { inputInfo, inputInfo }, { outputInfo }, descriptor);
+    deserializedNetwork->ExecuteStrategy(verifier);
+}
+
+BOOST_AUTO_TEST_CASE(SerializeGreater)
+{
+    const std::string layerName("greater");
+
+    const armnn::TensorShape shape{2, 1, 2, 4};
+    const armnn::TensorInfo inputInfo  = armnn::TensorInfo(shape, armnn::DataType::Float32);
+    const armnn::TensorInfo outputInfo = armnn::TensorInfo(shape, armnn::DataType::Boolean);
+
+    armnn::ComparisonDescriptor descriptor (armnn::ComparisonOperation::Greater);
+
+    ComparisonModel model(layerName, inputInfo, outputInfo, descriptor);
+
+    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*model.m_network));
+    BOOST_CHECK(deserializedNetwork);
+
+    ComparisonLayerVerifier verifier(layerName, { inputInfo, inputInfo }, { outputInfo }, descriptor);
+    deserializedNetwork->ExecuteStrategy(verifier);
+}
+
+BOOST_AUTO_TEST_SUITE_END()
diff --git a/src/armnnSerializer/test/LstmSerializationTests.cpp b/src/armnnSerializer/test/LstmSerializationTests.cpp
new file mode 100644
index 0000000000..4705c0bd28
--- /dev/null
+++ b/src/armnnSerializer/test/LstmSerializationTests.cpp
@@ -0,0 +1,2199 @@
+//
+// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "../Serializer.hpp"
+#include "SerializerTestUtils.hpp"
+
+#include <armnn/Descriptors.hpp>
+#include <armnn/INetwork.hpp>
+#include <armnn/IRuntime.hpp>
+#include <armnnDeserializer/IDeserializer.hpp>
+#include <armnn/utility/IgnoreUnused.hpp>
+#include <armnn/LstmParams.hpp>
+#include <armnn/QuantizedLstmParams.hpp>
+
+#include <boost/test/unit_test.hpp>
+
+#include <fmt/format.h>
+
+
+BOOST_AUTO_TEST_SUITE(SerializerTests)
+
+template<typename Descriptor>
+armnn::LstmInputParams ConstantVector2LstmInputParams(const std::vector<armnn::ConstTensor>& constants,
+                                                      Descriptor& descriptor)
+{
+    armnn::LstmInputParams lstmInputParams;
+    size_t i = 0;
+
+    // Inserting basic paramters
+    lstmInputParams.m_InputToForgetWeights     = &constants[i++];
+    lstmInputParams.m_InputToCellWeights       = &constants[i++];
+    lstmInputParams.m_InputToOutputWeights     = &constants[i++];
+    lstmInputParams.m_RecurrentToForgetWeights = &constants[i++];
+    lstmInputParams.m_RecurrentToCellWeights   = &constants[i++];
+    lstmInputParams.m_RecurrentToOutputWeights = &constants[i++];
+    lstmInputParams.m_ForgetGateBias           = &constants[i++];
+    lstmInputParams.m_CellBias                 = &constants[i++];
+    lstmInputParams.m_OutputGateBias           = &constants[i++];
+    if (!descriptor.m_CifgEnabled)
+    {
+        lstmInputParams.m_InputToInputWeights     = &constants[i++];
+        lstmInputParams.m_RecurrentToInputWeights = &constants[i++];
+        lstmInputParams.m_InputGateBias           = &constants[i++];
+    }
+
+    if (descriptor.m_PeepholeEnabled)
+    {
+        if (!descriptor.m_CifgEnabled)
+        {
+            lstmInputParams.m_CellToInputWeights = &constants[i++];
+        }
+        lstmInputParams.m_CellToForgetWeights = &constants[i++];
+        lstmInputParams.m_CellToOutputWeights = &constants[i++];
+    }
+
+    if (descriptor.m_ProjectionEnabled)
+    {
+        lstmInputParams.m_ProjectionWeights = &constants[i++];
+        lstmInputParams.m_ProjectionBias    = &constants[i++];
+    }
+
+    if (descriptor.m_LayerNormEnabled)
+    {
+        if (!descriptor.m_CifgEnabled)
+        {
+            lstmInputParams.m_InputLayerNormWeights = &constants[i++];
+        }
+        lstmInputParams.m_ForgetLayerNormWeights = &constants[i++];
+        lstmInputParams.m_CellLayerNormWeights   = &constants[i++];
+        lstmInputParams.m_OutputLayerNormWeights = &constants[i++];
+    }
+
+    return lstmInputParams;
+}
+
+// Works for Lstm and QLstm (QuantizedLstm uses different parameters)
+template<typename Descriptor>
+class VerifyLstmLayer : public LayerVerifierBaseWithDescriptor<Descriptor>
+{
+public:
+    VerifyLstmLayer(const std::string& layerName,
+                    const std::vector<armnn::TensorInfo>& inputInfos,
+                    const std::vector<armnn::TensorInfo>& outputInfos,
+                    const Descriptor& descriptor,
+                    const armnn::LstmInputParams& inputParams)
+        : LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor)
+        , m_InputParams(inputParams) {}
+
+    void ExecuteStrategy(const armnn::IConnectableLayer* layer,
+                         const armnn::BaseDescriptor& descriptor,
+                         const std::vector<armnn::ConstTensor>& constants,
+                         const char* name,
+                         const armnn::LayerBindingId id = 0) override
+    {
+        armnn::IgnoreUnused(constants, id);
+        switch (layer->GetType())
+        {
+            case armnn::LayerType::Input: break;
+            case armnn::LayerType::Output: break;
+            case armnn::LayerType::Lstm:
+            {
+                this->VerifyNameAndConnections(layer, name);
+                const Descriptor& internalDescriptor = static_cast<const Descriptor&>(descriptor);
+                this->VerifyDescriptor(internalDescriptor);
+                armnn::LstmInputParams lstmParams = ConstantVector2LstmInputParams(constants, internalDescriptor);
+                VerifyInputParameters(lstmParams);
+                break;
+            }
+            case armnn::LayerType::QLstm:
+            {
+                this->VerifyNameAndConnections(layer, name);
+                const Descriptor& internalDescriptor = static_cast<const Descriptor&>(descriptor);
+                this->VerifyDescriptor(internalDescriptor);
+                armnn::LstmInputParams lstmParams = ConstantVector2LstmInputParams(constants, internalDescriptor);
+                VerifyInputParameters(lstmParams);
+                break;
+            }
+            default:
+            {
+                throw armnn::Exception("Unexpected layer type in Lstm test model");
+            }
+        }
+    }
+
+protected:
+    void VerifyInputParameters(const armnn::LstmInputParams& params)
+    {
+        this->VerifyConstTensors(
+            "m_InputToInputWeights", m_InputParams.m_InputToInputWeights, params.m_InputToInputWeights);
+        this->VerifyConstTensors(
+            "m_InputToForgetWeights", m_InputParams.m_InputToForgetWeights, params.m_InputToForgetWeights);
+        this->VerifyConstTensors(
+            "m_InputToCellWeights", m_InputParams.m_InputToCellWeights, params.m_InputToCellWeights);
+        this->VerifyConstTensors(
+            "m_InputToOutputWeights", m_InputParams.m_InputToOutputWeights, params.m_InputToOutputWeights);
+        this->VerifyConstTensors(
+            "m_RecurrentToInputWeights", m_InputParams.m_RecurrentToInputWeights, params.m_RecurrentToInputWeights);
+        this->VerifyConstTensors(
+            "m_RecurrentToForgetWeights", m_InputParams.m_RecurrentToForgetWeights, params.m_RecurrentToForgetWeights);
+        this->VerifyConstTensors(
+            "m_RecurrentToCellWeights", m_InputParams.m_RecurrentToCellWeights, params.m_RecurrentToCellWeights);
+        this->VerifyConstTensors(
+            "m_RecurrentToOutputWeights", m_InputParams.m_RecurrentToOutputWeights, params.m_RecurrentToOutputWeights);
+        this->VerifyConstTensors(
+            "m_CellToInputWeights", m_InputParams.m_CellToInputWeights, params.m_CellToInputWeights);
+        this->VerifyConstTensors(
+            "m_CellToForgetWeights", m_InputParams.m_CellToForgetWeights, params.m_CellToForgetWeights);
+        this->VerifyConstTensors(
+            "m_CellToOutputWeights", m_InputParams.m_CellToOutputWeights, params.m_CellToOutputWeights);
+        this->VerifyConstTensors(
+            "m_InputGateBias", m_InputParams.m_InputGateBias, params.m_InputGateBias);
+        this->VerifyConstTensors(
+            "m_ForgetGateBias", m_InputParams.m_ForgetGateBias, params.m_ForgetGateBias);
+        this->VerifyConstTensors(
+            "m_CellBias", m_InputParams.m_CellBias, params.m_CellBias);
+        this->VerifyConstTensors(
+            "m_OutputGateBias", m_InputParams.m_OutputGateBias, params.m_OutputGateBias);
+        this->VerifyConstTensors(
+            "m_ProjectionWeights", m_InputParams.m_ProjectionWeights, params.m_ProjectionWeights);
+        this->VerifyConstTensors(
+            "m_ProjectionBias", m_InputParams.m_ProjectionBias, params.m_ProjectionBias);
+        this->VerifyConstTensors(
+            "m_InputLayerNormWeights", m_InputParams.m_InputLayerNormWeights, params.m_InputLayerNormWeights);
+        this->VerifyConstTensors(
+            "m_ForgetLayerNormWeights", m_InputParams.m_ForgetLayerNormWeights, params.m_ForgetLayerNormWeights);
+        this->VerifyConstTensors(
+            "m_CellLayerNormWeights", m_InputParams.m_CellLayerNormWeights, params.m_CellLayerNormWeights);
+        this->VerifyConstTensors(
+            "m_OutputLayerNormWeights", m_InputParams.m_OutputLayerNormWeights, params.m_OutputLayerNormWeights);
+    }
+
+private:
+    armnn::LstmInputParams m_InputParams;
+};
+
+BOOST_AUTO_TEST_CASE(SerializeDeserializeLstmCifgPeepholeNoProjection)
+{
+    armnn::LstmDescriptor descriptor;
+    descriptor.m_ActivationFunc = 4;
+    descriptor.m_ClippingThresProj = 0.0f;
+    descriptor.m_ClippingThresCell = 0.0f;
+    descriptor.m_CifgEnabled = true; // if this is true then we DON'T need to set the OptCifgParams
+    descriptor.m_ProjectionEnabled = false;
+    descriptor.m_PeepholeEnabled = true;
+
+    const uint32_t batchSize = 1;
+    const uint32_t inputSize = 2;
+    const uint32_t numUnits = 4;
+    const uint32_t outputSize = numUnits;
+
+    armnn::TensorInfo inputWeightsInfo1({numUnits, inputSize}, armnn::DataType::Float32);
+    std::vector<float> inputToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements());
+    armnn::ConstTensor inputToForgetWeights(inputWeightsInfo1, inputToForgetWeightsData);
+
+    std::vector<float> inputToCellWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements());
+    armnn::ConstTensor inputToCellWeights(inputWeightsInfo1, inputToCellWeightsData);
+
+    std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements());
+    armnn::ConstTensor inputToOutputWeights(inputWeightsInfo1, inputToOutputWeightsData);
+
+    armnn::TensorInfo inputWeightsInfo2({numUnits, outputSize}, armnn::DataType::Float32);
+    std::vector<float> recurrentToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements());
+    armnn::ConstTensor recurrentToForgetWeights(inputWeightsInfo2, recurrentToForgetWeightsData);
+
+    std::vector<float> recurrentToCellWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements());
+    armnn::ConstTensor recurrentToCellWeights(inputWeightsInfo2, recurrentToCellWeightsData);
+
+    std::vector<float> recurrentToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements());
+    armnn::ConstTensor recurrentToOutputWeights(inputWeightsInfo2, recurrentToOutputWeightsData);
+
+    armnn::TensorInfo inputWeightsInfo3({numUnits}, armnn::DataType::Float32);
+    std::vector<float> cellToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo3.GetNumElements());
+    armnn::ConstTensor cellToForgetWeights(inputWeightsInfo3, cellToForgetWeightsData);
+
+    std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo3.GetNumElements());
+    armnn::ConstTensor cellToOutputWeights(inputWeightsInfo3, cellToOutputWeightsData);
+
+    std::vector<float> forgetGateBiasData(numUnits, 1.0f);
+    armnn::ConstTensor forgetGateBias(inputWeightsInfo3, forgetGateBiasData);
+
+    std::vector<float> cellBiasData(numUnits, 0.0f);
+    armnn::ConstTensor cellBias(inputWeightsInfo3, cellBiasData);
+
+    std::vector<float> outputGateBiasData(numUnits, 0.0f);
+    armnn::ConstTensor outputGateBias(inputWeightsInfo3, outputGateBiasData);
+
+    armnn::LstmInputParams params;
+    params.m_InputToForgetWeights = &inputToForgetWeights;
+    params.m_InputToCellWeights = &inputToCellWeights;
+    params.m_InputToOutputWeights = &inputToOutputWeights;
+    params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
+    params.m_RecurrentToCellWeights = &recurrentToCellWeights;
+    params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
+    params.m_ForgetGateBias = &forgetGateBias;
+    params.m_CellBias = &cellBias;
+    params.m_OutputGateBias = &outputGateBias;
+    params.m_CellToForgetWeights = &cellToForgetWeights;
+    params.m_CellToOutputWeights = &cellToOutputWeights;
+
+    armnn::INetworkPtr network = armnn::INetwork::Create();
+    armnn::IConnectableLayer* const inputLayer   = network->AddInputLayer(0);
+    armnn::IConnectableLayer* const cellStateIn = network->AddInputLayer(1);
+    armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(2);
+    const std::string layerName("lstm");
+    armnn::IConnectableLayer* const lstmLayer = network->AddLstmLayer(descriptor, params, layerName.c_str());
+    armnn::IConnectableLayer* const scratchBuffer  = network->AddOutputLayer(0);
+    armnn::IConnectableLayer* const outputStateOut  = network->AddOutputLayer(1);
+    armnn::IConnectableLayer* const cellStateOut  = network->AddOutputLayer(2);
+    armnn::IConnectableLayer* const outputLayer  = network->AddOutputLayer(3);
+
+    // connect up
+    armnn::TensorInfo inputTensorInfo({ batchSize, inputSize }, armnn::DataType::Float32);
+    armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits}, armnn::DataType::Float32);
+    armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32);
+    armnn::TensorInfo lstmTensorInfoScratchBuff({ batchSize, numUnits * 3 }, armnn::DataType::Float32);
+
+    inputLayer->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(0));
+    inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
+
+    outputStateIn->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(1));
+    outputStateIn->GetOutputSlot(0).SetTensorInfo(outputStateTensorInfo);
+
+    cellStateIn->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(2));
+    cellStateIn->GetOutputSlot(0).SetTensorInfo(cellStateTensorInfo);
+
+    lstmLayer->GetOutputSlot(0).Connect(scratchBuffer->GetInputSlot(0));
+    lstmLayer->GetOutputSlot(0).SetTensorInfo(lstmTensorInfoScratchBuff);
+
+    lstmLayer->GetOutputSlot(1).Connect(outputStateOut->GetInputSlot(0));
+    lstmLayer->GetOutputSlot(1).SetTensorInfo(outputStateTensorInfo);
+
+    lstmLayer->GetOutputSlot(2).Connect(cellStateOut->GetInputSlot(0));
+    lstmLayer->GetOutputSlot(2).SetTensorInfo(cellStateTensorInfo);
+
+    lstmLayer->GetOutputSlot(3).Connect(outputLayer->GetInputSlot(0));
+    lstmLayer->GetOutputSlot(3).SetTensorInfo(outputStateTensorInfo);
+
+    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
+    BOOST_CHECK(deserializedNetwork);
+
+    VerifyLstmLayer<armnn::LstmDescriptor> checker(
+        layerName,
+        {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
+        {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},
+        descriptor,
+        params);
+    deserializedNetwork->ExecuteStrategy(checker);
+}
+
+BOOST_AUTO_TEST_CASE(SerializeDeserializeLstmNoCifgWithPeepholeAndProjection)
+{
+    armnn::LstmDescriptor descriptor;
+    descriptor.m_ActivationFunc = 4;
+    descriptor.m_ClippingThresProj = 0.0f;
+    descriptor.m_ClippingThresCell = 0.0f;
+    descriptor.m_CifgEnabled = false; // if this is true then we DON'T need to set the OptCifgParams
+    descriptor.m_ProjectionEnabled = true;
+    descriptor.m_PeepholeEnabled = true;
+
+    const uint32_t batchSize = 2;
+    const uint32_t inputSize = 5;
+    const uint32_t numUnits = 20;
+    const uint32_t outputSize = 16;
+
+    armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::DataType::Float32);
+    std::vector<float> inputToInputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
+    armnn::ConstTensor inputToInputWeights(tensorInfo20x5, inputToInputWeightsData);
+
+    std::vector<float> inputToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
+    armnn::ConstTensor inputToForgetWeights(tensorInfo20x5, inputToForgetWeightsData);
+
+    std::vector<float> inputToCellWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
+    armnn::ConstTensor inputToCellWeights(tensorInfo20x5, inputToCellWeightsData);
+
+    std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
+    armnn::ConstTensor inputToOutputWeights(tensorInfo20x5, inputToOutputWeightsData);
+
+    armnn::TensorInfo tensorInfo20({numUnits}, armnn::DataType::Float32);
+    std::vector<float> inputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
+    armnn::ConstTensor inputGateBias(tensorInfo20, inputGateBiasData);
+
+    std::vector<float> forgetGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
+    armnn::ConstTensor forgetGateBias(tensorInfo20, forgetGateBiasData);
+
+    std::vector<float> cellBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
+    armnn::ConstTensor cellBias(tensorInfo20, cellBiasData);
+
+    std::vector<float> outputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
+    armnn::ConstTensor outputGateBias(tensorInfo20, outputGateBiasData);
+
+    armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, armnn::DataType::Float32);
+    std::vector<float> recurrentToInputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
+    armnn::ConstTensor recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData);
+
+    std::vector<float> recurrentToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
+    armnn::ConstTensor recurrentToForgetWeights(tensorInfo20x16, recurrentToForgetWeightsData);
+
+    std::vector<float> recurrentToCellWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
+    armnn::ConstTensor recurrentToCellWeights(tensorInfo20x16, recurrentToCellWeightsData);
+
+    std::vector<float> recurrentToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
+    armnn::ConstTensor recurrentToOutputWeights(tensorInfo20x16, recurrentToOutputWeightsData);
+
+    std::vector<float> cellToInputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
+    armnn::ConstTensor cellToInputWeights(tensorInfo20, cellToInputWeightsData);
+
+    std::vector<float> cellToForgetWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
+    armnn::ConstTensor cellToForgetWeights(tensorInfo20, cellToForgetWeightsData);
+
+    std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
+    armnn::ConstTensor cellToOutputWeights(tensorInfo20,  cellToOutputWeightsData);
+
+    armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, armnn::DataType::Float32);
+    std::vector<float> projectionWeightsData = GenerateRandomData<float>(tensorInfo16x20.GetNumElements());
+    armnn::ConstTensor projectionWeights(tensorInfo16x20, projectionWeightsData);
+
+    armnn::TensorInfo tensorInfo16({outputSize}, armnn::DataType::Float32);
+    std::vector<float> projectionBiasData(outputSize, 0.f);
+    armnn::ConstTensor projectionBias(tensorInfo16, projectionBiasData);
+
+    armnn::LstmInputParams params;
+    params.m_InputToForgetWeights = &inputToForgetWeights;
+    params.m_InputToCellWeights = &inputToCellWeights;
+    params.m_InputToOutputWeights = &inputToOutputWeights;
+    params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
+    params.m_RecurrentToCellWeights = &recurrentToCellWeights;
+    params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
+    params.m_ForgetGateBias = &forgetGateBias;
+    params.m_CellBias = &cellBias;
+    params.m_OutputGateBias = &outputGateBias;
+
+    // additional params because: descriptor.m_CifgEnabled = false
+    params.m_InputToInputWeights = &inputToInputWeights;
+    params.m_RecurrentToInputWeights = &recurrentToInputWeights;
+    params.m_CellToInputWeights = &cellToInputWeights;
+    params.m_InputGateBias = &inputGateBias;
+
+    // additional params because: descriptor.m_ProjectionEnabled = true
+    params.m_ProjectionWeights = &projectionWeights;
+    params.m_ProjectionBias = &projectionBias;
+
+    // additional params because: descriptor.m_PeepholeEnabled = true
+    params.m_CellToForgetWeights = &cellToForgetWeights;
+    params.m_CellToOutputWeights = &cellToOutputWeights;
+
+    armnn::INetworkPtr network = armnn::INetwork::Create();
+    armnn::IConnectableLayer* const inputLayer   = network->AddInputLayer(0);
+    armnn::IConnectableLayer* const cellStateIn = network->AddInputLayer(1);
+    armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(2);
+    const std::string layerName("lstm");
+    armnn::IConnectableLayer* const lstmLayer = network->AddLstmLayer(descriptor, params, layerName.c_str());
+    armnn::IConnectableLayer* const scratchBuffer  = network->AddOutputLayer(0);
+    armnn::IConnectableLayer* const outputStateOut  = network->AddOutputLayer(1);
+    armnn::IConnectableLayer* const cellStateOut  = network->AddOutputLayer(2);
+    armnn::IConnectableLayer* const outputLayer  = network->AddOutputLayer(3);
+
+    // connect up
+    armnn::TensorInfo inputTensorInfo({ batchSize, inputSize }, armnn::DataType::Float32);
+    armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits}, armnn::DataType::Float32);
+    armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32);
+    armnn::TensorInfo lstmTensorInfoScratchBuff({ batchSize, numUnits * 4 }, armnn::DataType::Float32);
+
+    inputLayer->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(0));
+    inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
+
+    outputStateIn->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(1));
+    outputStateIn->GetOutputSlot(0).SetTensorInfo(outputStateTensorInfo);
+
+    cellStateIn->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(2));
+    cellStateIn->GetOutputSlot(0).SetTensorInfo(cellStateTensorInfo);
+
+    lstmLayer->GetOutputSlot(0).Connect(scratchBuffer->GetInputSlot(0));
+    lstmLayer->GetOutputSlot(0).SetTensorInfo(lstmTensorInfoScratchBuff);
+
+    lstmLayer->GetOutputSlot(1).Connect(outputStateOut->GetInputSlot(0));
+    lstmLayer->GetOutputSlot(1).SetTensorInfo(outputStateTensorInfo);
+
+    lstmLayer->GetOutputSlot(2).Connect(cellStateOut->GetInputSlot(0));
+    lstmLayer->GetOutputSlot(2).SetTensorInfo(cellStateTensorInfo);
+
+    lstmLayer->GetOutputSlot(3).Connect(outputLayer->GetInputSlot(0));
+    lstmLayer->GetOutputSlot(3).SetTensorInfo(outputStateTensorInfo);
+
+    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
+    BOOST_CHECK(deserializedNetwork);
+
+    VerifyLstmLayer<armnn::LstmDescriptor> checker(
+        layerName,
+        {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
+        {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},
+        descriptor,
+        params);
+    deserializedNetwork->ExecuteStrategy(checker);
+}
+
+BOOST_AUTO_TEST_CASE(SerializeDeserializeLstmNoCifgWithPeepholeWithProjectionWithLayerNorm)
+{
+    armnn::LstmDescriptor descriptor;
+    descriptor.m_ActivationFunc = 4;
+    descriptor.m_ClippingThresProj = 0.0f;
+    descriptor.m_ClippingThresCell = 0.0f;
+    descriptor.m_CifgEnabled = false; // if this is true then we DON'T need to set the OptCifgParams
+    descriptor.m_ProjectionEnabled = true;
+    descriptor.m_PeepholeEnabled = true;
+    descriptor.m_LayerNormEnabled = true;
+
+    const uint32_t batchSize = 2;
+    const uint32_t inputSize = 5;
+    const uint32_t numUnits = 20;
+    const uint32_t outputSize = 16;
+
+    armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::DataType::Float32);
+    std::vector<float> inputToInputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
+    armnn::ConstTensor inputToInputWeights(tensorInfo20x5, inputToInputWeightsData);
+
+    std::vector<float> inputToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
+    armnn::ConstTensor inputToForgetWeights(tensorInfo20x5, inputToForgetWeightsData);
+
+    std::vector<float> inputToCellWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
+    armnn::ConstTensor inputToCellWeights(tensorInfo20x5, inputToCellWeightsData);
+
+    std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
+    armnn::ConstTensor inputToOutputWeights(tensorInfo20x5, inputToOutputWeightsData);
+
+    armnn::TensorInfo tensorInfo20({numUnits}, armnn::DataType::Float32);
+    std::vector<float> inputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
+    armnn::ConstTensor inputGateBias(tensorInfo20, inputGateBiasData);
+
+    std::vector<float> forgetGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
+    armnn::ConstTensor forgetGateBias(tensorInfo20, forgetGateBiasData);
+
+    std::vector<float> cellBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
+    armnn::ConstTensor cellBias(tensorInfo20, cellBiasData);
+
+    std::vector<float> outputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
+    armnn::ConstTensor outputGateBias(tensorInfo20, outputGateBiasData);
+
+    armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, armnn::DataType::Float32);
+    std::vector<float> recurrentToInputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
+    armnn::ConstTensor recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData);
+
+    std::vector<float> recurrentToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
+    armnn::ConstTensor recurrentToForgetWeights(tensorInfo20x16, recurrentToForgetWeightsData);
+
+    std::vector<float> recurrentToCellWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
+    armnn::ConstTensor recurrentToCellWeights(tensorInfo20x16, recurrentToCellWeightsData);
+
+    std::vector<float> recurrentToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
+    armnn::ConstTensor recurrentToOutputWeights(tensorInfo20x16, recurrentToOutputWeightsData);
+
+    std::vector<float> cellToInputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
+    armnn::ConstTensor cellToInputWeights(tensorInfo20, cellToInputWeightsData);
+
+    std::vector<float> cellToForgetWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
+    armnn::ConstTensor cellToForgetWeights(tensorInfo20, cellToForgetWeightsData);
+
+    std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
+    armnn::ConstTensor cellToOutputWeights(tensorInfo20,  cellToOutputWeightsData);
+
+    armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, armnn::DataType::Float32);
+    std::vector<float> projectionWeightsData = GenerateRandomData<float>(tensorInfo16x20.GetNumElements());
+    armnn::ConstTensor projectionWeights(tensorInfo16x20, projectionWeightsData);
+
+    armnn::TensorInfo tensorInfo16({outputSize}, armnn::DataType::Float32);
+    std::vector<float> projectionBiasData(outputSize, 0.f);
+    armnn::ConstTensor projectionBias(tensorInfo16, projectionBiasData);
+
+    std::vector<float> inputLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
+    armnn::ConstTensor inputLayerNormWeights(tensorInfo20, forgetGateBiasData);
+
+    std::vector<float> forgetLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
+    armnn::ConstTensor forgetLayerNormWeights(tensorInfo20, forgetGateBiasData);
+
+    std::vector<float> cellLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
+    armnn::ConstTensor cellLayerNormWeights(tensorInfo20, forgetGateBiasData);
+
+    std::vector<float> outLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
+    armnn::ConstTensor outLayerNormWeights(tensorInfo20, forgetGateBiasData);
+
+    armnn::LstmInputParams params;
+    params.m_InputToForgetWeights = &inputToForgetWeights;
+    params.m_InputToCellWeights = &inputToCellWeights;
+    params.m_InputToOutputWeights = &inputToOutputWeights;
+    params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
+    params.m_RecurrentToCellWeights = &recurrentToCellWeights;
+    params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
+    params.m_ForgetGateBias = &forgetGateBias;
+    params.m_CellBias = &cellBias;
+    params.m_OutputGateBias = &outputGateBias;
+
+    // additional params because: descriptor.m_CifgEnabled = false
+    params.m_InputToInputWeights = &inputToInputWeights;
+    params.m_RecurrentToInputWeights = &recurrentToInputWeights;
+    params.m_CellToInputWeights = &cellToInputWeights;
+    params.m_InputGateBias = &inputGateBias;
+
+    // additional params because: descriptor.m_ProjectionEnabled = true
+    params.m_ProjectionWeights = &projectionWeights;
+    params.m_ProjectionBias = &projectionBias;
+
+    // additional params because: descriptor.m_PeepholeEnabled = true
+    params.m_CellToForgetWeights = &cellToForgetWeights;
+    params.m_CellToOutputWeights = &cellToOutputWeights;
+
+    // additional params because: despriptor.m_LayerNormEnabled = true
+    params.m_InputLayerNormWeights = &inputLayerNormWeights;
+    params.m_ForgetLayerNormWeights = &forgetLayerNormWeights;
+    params.m_CellLayerNormWeights = &cellLayerNormWeights;
+    params.m_OutputLayerNormWeights = &outLayerNormWeights;
+
+    armnn::INetworkPtr network = armnn::INetwork::Create();
+    armnn::IConnectableLayer* const inputLayer   = network->AddInputLayer(0);
+    armnn::IConnectableLayer* const cellStateIn = network->AddInputLayer(1);
+    armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(2);
+    const std::string layerName("lstm");
+    armnn::IConnectableLayer* const lstmLayer = network->AddLstmLayer(descriptor, params, layerName.c_str());
+    armnn::IConnectableLayer* const scratchBuffer  = network->AddOutputLayer(0);
+    armnn::IConnectableLayer* const outputStateOut  = network->AddOutputLayer(1);
+    armnn::IConnectableLayer* const cellStateOut  = network->AddOutputLayer(2);
+    armnn::IConnectableLayer* const outputLayer  = network->AddOutputLayer(3);
+
+    // connect up
+    armnn::TensorInfo inputTensorInfo({ batchSize, inputSize }, armnn::DataType::Float32);
+    armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits}, armnn::DataType::Float32);
+    armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32);
+    armnn::TensorInfo lstmTensorInfoScratchBuff({ batchSize, numUnits * 4 }, armnn::DataType::Float32);
+
+    inputLayer->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(0));
+    inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
+
+    outputStateIn->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(1));
+    outputStateIn->GetOutputSlot(0).SetTensorInfo(outputStateTensorInfo);
+
+    cellStateIn->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(2));
+    cellStateIn->GetOutputSlot(0).SetTensorInfo(cellStateTensorInfo);
+
+    lstmLayer->GetOutputSlot(0).Connect(scratchBuffer->GetInputSlot(0));
+    lstmLayer->GetOutputSlot(0).SetTensorInfo(lstmTensorInfoScratchBuff);
+
+    lstmLayer->GetOutputSlot(1).Connect(outputStateOut->GetInputSlot(0));
+    lstmLayer->GetOutputSlot(1).SetTensorInfo(outputStateTensorInfo);
+
+    lstmLayer->GetOutputSlot(2).Connect(cellStateOut->GetInputSlot(0));
+    lstmLayer->GetOutputSlot(2).SetTensorInfo(cellStateTensorInfo);
+
+    lstmLayer->GetOutputSlot(3).Connect(outputLayer->GetInputSlot(0));
+    lstmLayer->GetOutputSlot(3).SetTensorInfo(outputStateTensorInfo);
+
+    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
+    BOOST_CHECK(deserializedNetwork);
+
+    VerifyLstmLayer<armnn::LstmDescriptor> checker(
+            layerName,
+            {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
+            {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},
+            descriptor,
+            params);
+    deserializedNetwork->ExecuteStrategy(checker);
+}
+
+BOOST_AUTO_TEST_CASE(EnsureLstmLayersBackwardCompatibility)
+{
+    // The hex data below is a flat buffer containing a lstm layer with no Cifg, with peephole and projection
+    // enabled. That data was obtained before additional layer normalization parameters where added to the
+    // lstm serializer. That way it can be tested if a lstm model with the old parameter configuration can
+    // still be loaded
+    const std::vector<uint8_t> lstmNoCifgWithPeepholeAndProjectionModel =
+    {
+        0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,
+        0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x2C, 0x00, 0x00, 0x00, 0x38, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00,
+        0xDC, 0x29, 0x00, 0x00, 0x38, 0x29, 0x00, 0x00, 0xB4, 0x28, 0x00, 0x00, 0x94, 0x01, 0x00, 0x00, 0x3C, 0x01,
+        0x00, 0x00, 0xE0, 0x00, 0x00, 0x00, 0x84, 0x00, 0x00, 0x00, 0x28, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
+        0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00,
+        0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x70, 0xD6, 0xFF, 0xFF,
+        0x00, 0x00, 0x00, 0x0B, 0x04, 0x00, 0x00, 0x00, 0x06, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x88, 0xD7,
+        0xFF, 0xFF, 0x08, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0xF6, 0xD6, 0xFF, 0xFF, 0x07, 0x00, 0x00, 0x00,
+        0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,
+        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+        0xE8, 0xD7, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0xC8, 0xD6, 0xFF, 0xFF, 0x00, 0x00,
+        0x00, 0x0B, 0x04, 0x00, 0x00, 0x00, 0x5E, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0xE0, 0xD7, 0xFF, 0xFF,
+        0x08, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x4E, 0xD7, 0xFF, 0xFF, 0x06, 0x00, 0x00, 0x00, 0x10, 0x00,
+        0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+        0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x40, 0xD8,
+        0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x20, 0xD7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B,
+        0x04, 0x00, 0x00, 0x00, 0xB6, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x38, 0xD8, 0xFF, 0xFF, 0x08, 0x00,
+        0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0xA6, 0xD7, 0xFF, 0xFF, 0x05, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
+        0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+        0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x98, 0xD8, 0xFF, 0xFF,
+        0x03, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x78, 0xD7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B, 0x04, 0x00,
+        0x00, 0x00, 0x0E, 0xD8, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x16, 0xD8, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00,
+        0xFA, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00,
+        0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
+        0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xD8, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00,
+        0x00, 0x00, 0x6C, 0xD8, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x23, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00,
+        0x12, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00, 0x00, 0x00, 0xE0, 0x25, 0x00, 0x00, 0xD0, 0x25,
+        0x00, 0x00, 0x2C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x26, 0x00, 0x48, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00,
+        0x10, 0x00, 0x14, 0x00, 0x18, 0x00, 0x1C, 0x00, 0x20, 0x00, 0x24, 0x00, 0x28, 0x00, 0x2C, 0x00, 0x30, 0x00,
+        0x34, 0x00, 0x38, 0x00, 0x3C, 0x00, 0x40, 0x00, 0x44, 0x00, 0x26, 0x00, 0x00, 0x00, 0xC4, 0x23, 0x00, 0x00,
+        0xF8, 0x21, 0x00, 0x00, 0x2C, 0x20, 0x00, 0x00, 0xF0, 0x1A, 0x00, 0x00, 0xB4, 0x15, 0x00, 0x00, 0x78, 0x10,
+        0x00, 0x00, 0xF0, 0x0F, 0x00, 0x00, 0x68, 0x0F, 0x00, 0x00, 0xE0, 0x0E, 0x00, 0x00, 0x14, 0x0D, 0x00, 0x00,
+        0xD8, 0x07, 0x00, 0x00, 0x50, 0x07, 0x00, 0x00, 0xC8, 0x06, 0x00, 0x00, 0x8C, 0x01, 0x00, 0x00, 0x14, 0x01,
+        0x00, 0x00, 0x8C, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xEE, 0xD7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03,
+        0x64, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xFE, 0xD8, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x14, 0x00,
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+        0x00, 0x00, 0x00, 0x00, 0x1A, 0xFD, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,
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+        0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x06, 0x00, 0x07, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00,
+        0x01, 0x01, 0x04, 0x00, 0x00, 0x00, 0x2E, 0xFE, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
+        0x22, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6C, 0x73,
+        0x74, 0x6D, 0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0xEC, 0x00, 0x00, 0x00, 0xD0, 0x00, 0x00, 0x00,
+        0xB4, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x88, 0x00, 0x00, 0x00, 0x5C, 0x00, 0x00, 0x00, 0x30, 0x00,
+        0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x14, 0xFF, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
+        0xA6, 0xFD, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00,
+        0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x3C, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00,
+        0x04, 0x00, 0x00, 0x00, 0xCE, 0xFD, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,
+        0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x64, 0xFF, 0xFF, 0xFF,
+        0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xF6, 0xFD, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00,
+        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
+        0xB4, 0xFE, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x1A, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00,
+        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x50, 0x00, 0x00, 0x00,
+        0xF0, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00,
+        0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
+        0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,
+        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE8, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x09, 0x04, 0x00, 0x00, 0x00,
+        0x7E, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00,
+        0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00,
+        0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00,
+        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
+        0x68, 0xFF, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0xCE, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00,
+        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
+        0x08, 0x00, 0x0E, 0x00, 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x0C, 0x00,
+        0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x08, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00,
+        0x08, 0x00, 0x0E, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x01, 0x00,
+        0x00, 0x00, 0x00, 0x00, 0x0E, 0x00, 0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x14, 0x00,
+        0x0E, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00,
+        0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+        0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00,
+        0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00,
+        0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x08, 0x00,
+        0x0C, 0x00, 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x04, 0x00, 0x00, 0x00,
+        0xF6, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x0A, 0x00, 0x04, 0x00, 0x06, 0x00,
+        0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x00, 0x14, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00,
+        0x0C, 0x00, 0x10, 0x00, 0x0E, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00,
+        0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+        0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00,
+        0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x08, 0x00, 0x07, 0x00, 0x0C, 0x00,
+        0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00,
+        0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x00
+    };
+
+    armnn::INetworkPtr deserializedNetwork =
+        DeserializeNetwork(std::string(lstmNoCifgWithPeepholeAndProjectionModel.begin(),
+                                       lstmNoCifgWithPeepholeAndProjectionModel.end()));
+
+    BOOST_CHECK(deserializedNetwork);
+
+    // generating the same model parameters which where used to serialize the model (Layer norm is not specified)
+    armnn::LstmDescriptor descriptor;
+    descriptor.m_ActivationFunc    = 4;
+    descriptor.m_ClippingThresProj = 0.0f;
+    descriptor.m_ClippingThresCell = 0.0f;
+    descriptor.m_CifgEnabled       = false;
+    descriptor.m_ProjectionEnabled = true;
+    descriptor.m_PeepholeEnabled   = true;
+
+    const uint32_t batchSize  = 2u;
+    const uint32_t inputSize  = 5u;
+    const uint32_t numUnits   = 20u;
+    const uint32_t outputSize = 16u;
+
+    armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::DataType::Float32);
+    std::vector<float> inputToInputWeightsData(tensorInfo20x5.GetNumElements(), 0.0f);
+    armnn::ConstTensor inputToInputWeights(tensorInfo20x5, inputToInputWeightsData);
+
+    std::vector<float> inputToForgetWeightsData(tensorInfo20x5.GetNumElements(), 0.0f);
+    armnn::ConstTensor inputToForgetWeights(tensorInfo20x5, inputToForgetWeightsData);
+
+    std::vector<float> inputToCellWeightsData(tensorInfo20x5.GetNumElements(), 0.0f);
+    armnn::ConstTensor inputToCellWeights(tensorInfo20x5, inputToCellWeightsData);
+
+    std::vector<float> inputToOutputWeightsData(tensorInfo20x5.GetNumElements(), 0.0f);
+    armnn::ConstTensor inputToOutputWeights(tensorInfo20x5, inputToOutputWeightsData);
+
+    armnn::TensorInfo tensorInfo20({numUnits}, armnn::DataType::Float32);
+    std::vector<float> inputGateBiasData(tensorInfo20.GetNumElements(), 0.0f);
+    armnn::ConstTensor inputGateBias(tensorInfo20, inputGateBiasData);
+
+    std::vector<float> forgetGateBiasData(tensorInfo20.GetNumElements(), 0.0f);
+    armnn::ConstTensor forgetGateBias(tensorInfo20, forgetGateBiasData);
+
+    std::vector<float> cellBiasData(tensorInfo20.GetNumElements(), 0.0f);
+    armnn::ConstTensor cellBias(tensorInfo20, cellBiasData);
+
+    std::vector<float> outputGateBiasData(tensorInfo20.GetNumElements(), 0.0f);
+    armnn::ConstTensor outputGateBias(tensorInfo20, outputGateBiasData);
+
+    armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, armnn::DataType::Float32);
+    std::vector<float> recurrentToInputWeightsData(tensorInfo20x16.GetNumElements(), 0.0f);
+    armnn::ConstTensor recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData);
+
+    std::vector<float> recurrentToForgetWeightsData(tensorInfo20x16.GetNumElements(), 0.0f);
+    armnn::ConstTensor recurrentToForgetWeights(tensorInfo20x16, recurrentToForgetWeightsData);
+
+    std::vector<float> recurrentToCellWeightsData(tensorInfo20x16.GetNumElements(), 0.0f);
+    armnn::ConstTensor recurrentToCellWeights(tensorInfo20x16, recurrentToCellWeightsData);
+
+    std::vector<float> recurrentToOutputWeightsData(tensorInfo20x16.GetNumElements(), 0.0f);
+    armnn::ConstTensor recurrentToOutputWeights(tensorInfo20x16, recurrentToOutputWeightsData);
+
+    std::vector<float> cellToInputWeightsData(tensorInfo20.GetNumElements(), 0.0f);
+    armnn::ConstTensor cellToInputWeights(tensorInfo20, cellToInputWeightsData);
+
+    std::vector<float> cellToForgetWeightsData(tensorInfo20.GetNumElements(), 0.0f);
+    armnn::ConstTensor cellToForgetWeights(tensorInfo20, cellToForgetWeightsData);
+
+    std::vector<float> cellToOutputWeightsData(tensorInfo20.GetNumElements(), 0.0f);
+    armnn::ConstTensor cellToOutputWeights(tensorInfo20,  cellToOutputWeightsData);
+
+    armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, armnn::DataType::Float32);
+    std::vector<float> projectionWeightsData(tensorInfo16x20.GetNumElements(), 0.0f);
+    armnn::ConstTensor projectionWeights(tensorInfo16x20, projectionWeightsData);
+
+    armnn::TensorInfo tensorInfo16({outputSize}, armnn::DataType::Float32);
+    std::vector<float> projectionBiasData(outputSize, 0.0f);
+    armnn::ConstTensor projectionBias(tensorInfo16, projectionBiasData);
+
+    armnn::LstmInputParams params;
+    params.m_InputToForgetWeights     = &inputToForgetWeights;
+    params.m_InputToCellWeights       = &inputToCellWeights;
+    params.m_InputToOutputWeights     = &inputToOutputWeights;
+    params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
+    params.m_RecurrentToCellWeights   = &recurrentToCellWeights;
+    params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
+    params.m_ForgetGateBias           = &forgetGateBias;
+    params.m_CellBias                 = &cellBias;
+    params.m_OutputGateBias           = &outputGateBias;
+
+    // additional params because: descriptor.m_CifgEnabled = false
+    params.m_InputToInputWeights      = &inputToInputWeights;
+    params.m_RecurrentToInputWeights  = &recurrentToInputWeights;
+    params.m_CellToInputWeights       = &cellToInputWeights;
+    params.m_InputGateBias            = &inputGateBias;
+
+    // additional params because: descriptor.m_ProjectionEnabled = true
+    params.m_ProjectionWeights        = &projectionWeights;
+    params.m_ProjectionBias           = &projectionBias;
+
+    // additional params because: descriptor.m_PeepholeEnabled = true
+    params.m_CellToForgetWeights      = &cellToForgetWeights;
+    params.m_CellToOutputWeights      = &cellToOutputWeights;
+
+    const std::string layerName("lstm");
+    armnn::TensorInfo inputTensorInfo({ batchSize, inputSize }, armnn::DataType::Float32);
+    armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits}, armnn::DataType::Float32);
+    armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32);
+    armnn::TensorInfo lstmTensorInfoScratchBuff({ batchSize, numUnits * 4 }, armnn::DataType::Float32);
+
+    VerifyLstmLayer<armnn::LstmDescriptor> checker(
+            layerName,
+            {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
+            {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},
+            descriptor,
+            params);
+    deserializedNetwork->ExecuteStrategy(checker);
+}
+
+armnn::QuantizedLstmInputParams ConstantsVector2QuantizedLstmInputParams(
+        const std::vector<armnn::ConstTensor>& constants)
+{
+    armnn::QuantizedLstmInputParams params;
+
+    // index for constants vector
+    size_t i = 0;
+
+    // Get input parameters
+    params.m_InputToInputWeights  = &constants[i++];
+    params.m_InputToForgetWeights = &constants[i++];
+    params.m_InputToCellWeights   = &constants[i++];
+    params.m_InputToOutputWeights = &constants[i++];
+
+    params.m_RecurrentToInputWeights  = &constants[i++];
+    params.m_RecurrentToForgetWeights = &constants[i++];
+    params.m_RecurrentToCellWeights   = &constants[i++];
+    params.m_RecurrentToOutputWeights = &constants[i++];
+
+    params.m_InputGateBias  = &constants[i++];
+    params.m_ForgetGateBias = &constants[i++];
+    params.m_CellBias       = &constants[i++];
+    params.m_OutputGateBias = &constants[i++];
+
+    return params;
+}
+
+class VerifyQuantizedLstmLayer : public LayerVerifierBase
+{
+
+public:
+    VerifyQuantizedLstmLayer(const std::string& layerName,
+                             const std::vector<armnn::TensorInfo>& inputInfos,
+                             const std::vector<armnn::TensorInfo>& outputInfos,
+                             const armnn::QuantizedLstmInputParams& inputParams)
+        : LayerVerifierBase(layerName, inputInfos, outputInfos), m_InputParams(inputParams) {}
+
+    void ExecuteStrategy(const armnn::IConnectableLayer* layer,
+                         const armnn::BaseDescriptor& descriptor,
+                         const std::vector<armnn::ConstTensor>& constants,
+                         const char* name,
+                         const armnn::LayerBindingId id = 0) override
+    {
+        armnn::IgnoreUnused(descriptor, constants, id);
+        switch (layer->GetType())
+        {
+            case armnn::LayerType::Input: break;
+            case armnn::LayerType::Output: break;
+            case armnn::LayerType::QuantizedLstm:
+            {
+                VerifyNameAndConnections(layer, name);
+                armnn::QuantizedLstmInputParams params = ConstantsVector2QuantizedLstmInputParams(constants);
+                VerifyInputParameters(params);
+                break;
+            }
+            default:
+            {
+                throw armnn::Exception(fmt::format("Unexpected layer type in QuantizedLstm test model:",
+                                                           layer->GetName()));
+            }
+        }
+    }
+
+protected:
+    void VerifyInputParameters(const armnn::QuantizedLstmInputParams& params)
+    {
+        VerifyConstTensors("m_InputToInputWeights",
+                           m_InputParams.m_InputToInputWeights, params.m_InputToInputWeights);
+        VerifyConstTensors("m_InputToForgetWeights",
+                           m_InputParams.m_InputToForgetWeights, params.m_InputToForgetWeights);
+        VerifyConstTensors("m_InputToCellWeights",
+                           m_InputParams.m_InputToCellWeights, params.m_InputToCellWeights);
+        VerifyConstTensors("m_InputToOutputWeights",
+                           m_InputParams.m_InputToOutputWeights, params.m_InputToOutputWeights);
+        VerifyConstTensors("m_RecurrentToInputWeights",
+                           m_InputParams.m_RecurrentToInputWeights, params.m_RecurrentToInputWeights);
+        VerifyConstTensors("m_RecurrentToForgetWeights",
+                           m_InputParams.m_RecurrentToForgetWeights, params.m_RecurrentToForgetWeights);
+        VerifyConstTensors("m_RecurrentToCellWeights",
+                           m_InputParams.m_RecurrentToCellWeights, params.m_RecurrentToCellWeights);
+        VerifyConstTensors("m_RecurrentToOutputWeights",
+                           m_InputParams.m_RecurrentToOutputWeights, params.m_RecurrentToOutputWeights);
+        VerifyConstTensors("m_InputGateBias",
+                           m_InputParams.m_InputGateBias, params.m_InputGateBias);
+        VerifyConstTensors("m_ForgetGateBias",
+                           m_InputParams.m_ForgetGateBias, params.m_ForgetGateBias);
+        VerifyConstTensors("m_CellBias",
+                           m_InputParams.m_CellBias, params.m_CellBias);
+        VerifyConstTensors("m_OutputGateBias",
+                           m_InputParams.m_OutputGateBias, params.m_OutputGateBias);
+    }
+
+private:
+    armnn::QuantizedLstmInputParams m_InputParams;
+};
+
+BOOST_AUTO_TEST_CASE(SerializeDeserializeQuantizedLstm)
+{
+    const uint32_t batchSize = 1;
+    const uint32_t inputSize = 2;
+    const uint32_t numUnits = 4;
+    const uint32_t outputSize = numUnits;
+
+    // Scale/Offset for input/output, cellState In/Out, weights, bias
+    float inputOutputScale = 0.0078125f;
+    int32_t inputOutputOffset = 128;
+
+    float cellStateScale = 0.00048828125f;
+    int32_t cellStateOffset = 0;
+
+    float weightsScale = 0.00408021f;
+    int32_t weightsOffset = 100;
+
+    float biasScale = 3.1876640625e-05f;
+    int32_t biasOffset = 0;
+
+    // The shape of weight data is {outputSize, inputSize} = {4, 2}
+    armnn::TensorShape inputToInputWeightsShape = {4, 2};
+    std::vector<uint8_t> inputToInputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};
+    armnn::TensorInfo inputToInputWeightsInfo(inputToInputWeightsShape,
+                                              armnn::DataType::QAsymmU8,
+                                              weightsScale,
+                                              weightsOffset);
+    armnn::ConstTensor inputToInputWeights(inputToInputWeightsInfo, inputToInputWeightsData);
+
+    armnn::TensorShape inputToForgetWeightsShape = {4, 2};
+    std::vector<uint8_t> inputToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};
+    armnn::TensorInfo inputToForgetWeightsInfo(inputToForgetWeightsShape,
+                                               armnn::DataType::QAsymmU8,
+                                               weightsScale,
+                                               weightsOffset);
+    armnn::ConstTensor inputToForgetWeights(inputToForgetWeightsInfo, inputToForgetWeightsData);
+
+    armnn::TensorShape inputToCellWeightsShape = {4, 2};
+    std::vector<uint8_t> inputToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};
+    armnn::TensorInfo inputToCellWeightsInfo(inputToCellWeightsShape,
+                                             armnn::DataType::QAsymmU8,
+                                             weightsScale,
+                                             weightsOffset);
+    armnn::ConstTensor inputToCellWeights(inputToCellWeightsInfo, inputToCellWeightsData);
+
+    armnn::TensorShape inputToOutputWeightsShape = {4, 2};
+    std::vector<uint8_t> inputToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};
+    armnn::TensorInfo inputToOutputWeightsInfo(inputToOutputWeightsShape,
+                                               armnn::DataType::QAsymmU8,
+                                               weightsScale,
+                                               weightsOffset);
+    armnn::ConstTensor inputToOutputWeights(inputToOutputWeightsInfo, inputToOutputWeightsData);
+
+    // The shape of recurrent weight data is {outputSize, outputSize} = {4, 4}
+    armnn::TensorShape recurrentToInputWeightsShape = {4, 4};
+    std::vector<uint8_t> recurrentToInputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
+    armnn::TensorInfo recurrentToInputWeightsInfo(recurrentToInputWeightsShape,
+                                                  armnn::DataType::QAsymmU8,
+                                                  weightsScale,
+                                                  weightsOffset);
+    armnn::ConstTensor recurrentToInputWeights(recurrentToInputWeightsInfo, recurrentToInputWeightsData);
+
+    armnn::TensorShape recurrentToForgetWeightsShape = {4, 4};
+    std::vector<uint8_t> recurrentToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
+    armnn::TensorInfo recurrentToForgetWeightsInfo(recurrentToForgetWeightsShape,
+                                                   armnn::DataType::QAsymmU8,
+                                                   weightsScale,
+                                                   weightsOffset);
+    armnn::ConstTensor recurrentToForgetWeights(recurrentToForgetWeightsInfo, recurrentToForgetWeightsData);
+
+    armnn::TensorShape recurrentToCellWeightsShape = {4, 4};
+    std::vector<uint8_t> recurrentToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
+    armnn::TensorInfo recurrentToCellWeightsInfo(recurrentToCellWeightsShape,
+                                                 armnn::DataType::QAsymmU8,
+                                                 weightsScale,
+                                                 weightsOffset);
+    armnn::ConstTensor recurrentToCellWeights(recurrentToCellWeightsInfo, recurrentToCellWeightsData);
+
+    armnn::TensorShape recurrentToOutputWeightsShape = {4, 4};
+    std::vector<uint8_t> recurrentToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
+    armnn::TensorInfo recurrentToOutputWeightsInfo(recurrentToOutputWeightsShape,
+                                                   armnn::DataType::QAsymmU8,
+                                                   weightsScale,
+                                                   weightsOffset);
+    armnn::ConstTensor recurrentToOutputWeights(recurrentToOutputWeightsInfo, recurrentToOutputWeightsData);
+
+    // The shape of bias data is {outputSize} = {4}
+    armnn::TensorShape inputGateBiasShape = {4};
+    std::vector<int32_t> inputGateBiasData = {1, 2, 3, 4};
+    armnn::TensorInfo inputGateBiasInfo(inputGateBiasShape,
+                                        armnn::DataType::Signed32,
+                                        biasScale,
+                                        biasOffset);
+    armnn::ConstTensor inputGateBias(inputGateBiasInfo, inputGateBiasData);
+
+    armnn::TensorShape forgetGateBiasShape = {4};
+    std::vector<int32_t> forgetGateBiasData = {1, 2, 3, 4};
+    armnn::TensorInfo forgetGateBiasInfo(forgetGateBiasShape,
+                                         armnn::DataType::Signed32,
+                                         biasScale,
+                                         biasOffset);
+    armnn::ConstTensor forgetGateBias(forgetGateBiasInfo, forgetGateBiasData);
+
+    armnn::TensorShape cellBiasShape = {4};
+    std::vector<int32_t> cellBiasData = {1, 2, 3, 4};
+    armnn::TensorInfo cellBiasInfo(cellBiasShape,
+                                   armnn::DataType::Signed32,
+                                   biasScale,
+                                   biasOffset);
+    armnn::ConstTensor cellBias(cellBiasInfo, cellBiasData);
+
+    armnn::TensorShape outputGateBiasShape = {4};
+    std::vector<int32_t> outputGateBiasData = {1, 2, 3, 4};
+    armnn::TensorInfo outputGateBiasInfo(outputGateBiasShape,
+                                         armnn::DataType::Signed32,
+                                         biasScale,
+                                         biasOffset);
+    armnn::ConstTensor outputGateBias(outputGateBiasInfo, outputGateBiasData);
+
+    armnn::QuantizedLstmInputParams params;
+    params.m_InputToInputWeights = &inputToInputWeights;
+    params.m_InputToForgetWeights = &inputToForgetWeights;
+    params.m_InputToCellWeights = &inputToCellWeights;
+    params.m_InputToOutputWeights = &inputToOutputWeights;
+    params.m_RecurrentToInputWeights = &recurrentToInputWeights;
+    params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
+    params.m_RecurrentToCellWeights = &recurrentToCellWeights;
+    params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
+    params.m_InputGateBias = &inputGateBias;
+    params.m_ForgetGateBias = &forgetGateBias;
+    params.m_CellBias = &cellBias;
+    params.m_OutputGateBias = &outputGateBias;
+
+    armnn::INetworkPtr network = armnn::INetwork::Create();
+    armnn::IConnectableLayer* const inputLayer = network->AddInputLayer(0);
+    armnn::IConnectableLayer* const cellStateIn = network->AddInputLayer(1);
+    armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(2);
+    const std::string layerName("QuantizedLstm");
+    armnn::IConnectableLayer* const quantizedLstmLayer = network->AddQuantizedLstmLayer(params, layerName.c_str());
+    armnn::IConnectableLayer* const cellStateOut = network->AddOutputLayer(0);
+    armnn::IConnectableLayer* const outputLayer = network->AddOutputLayer(1);
+
+    // Connect up
+    armnn::TensorInfo inputTensorInfo({ batchSize, inputSize },
+                                      armnn::DataType::QAsymmU8,
+                                      inputOutputScale,
+                                      inputOutputOffset);
+    armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits },
+                                          armnn::DataType::QSymmS16,
+                                          cellStateScale,
+                                          cellStateOffset);
+    armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize },
+                                            armnn::DataType::QAsymmU8,
+                                            inputOutputScale,
+                                            inputOutputOffset);
+
+    inputLayer->GetOutputSlot(0).Connect(quantizedLstmLayer->GetInputSlot(0));
+    inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
+
+    cellStateIn->GetOutputSlot(0).Connect(quantizedLstmLayer->GetInputSlot(1));
+    cellStateIn->GetOutputSlot(0).SetTensorInfo(cellStateTensorInfo);
+
+    outputStateIn->GetOutputSlot(0).Connect(quantizedLstmLayer->GetInputSlot(2));
+    outputStateIn->GetOutputSlot(0).SetTensorInfo(outputStateTensorInfo);
+
+    quantizedLstmLayer->GetOutputSlot(0).Connect(cellStateOut->GetInputSlot(0));
+    quantizedLstmLayer->GetOutputSlot(0).SetTensorInfo(cellStateTensorInfo);
+
+    quantizedLstmLayer->GetOutputSlot(1).Connect(outputLayer->GetInputSlot(0));
+    quantizedLstmLayer->GetOutputSlot(1).SetTensorInfo(outputStateTensorInfo);
+
+    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
+    BOOST_CHECK(deserializedNetwork);
+
+    VerifyQuantizedLstmLayer checker(layerName,
+                                     {inputTensorInfo, cellStateTensorInfo, outputStateTensorInfo},
+                                     {cellStateTensorInfo, outputStateTensorInfo},
+                                     params);
+
+    deserializedNetwork->ExecuteStrategy(checker);
+}
+
+BOOST_AUTO_TEST_CASE(SerializeDeserializeQLstmBasic)
+{
+    armnn::QLstmDescriptor descriptor;
+
+    descriptor.m_CifgEnabled       = true;
+    descriptor.m_ProjectionEnabled = false;
+    descriptor.m_PeepholeEnabled   = false;
+    descriptor.m_LayerNormEnabled  = false;
+
+    descriptor.m_CellClip       = 0.0f;
+    descriptor.m_ProjectionClip = 0.0f;
+
+    descriptor.m_InputIntermediateScale  = 0.00001f;
+    descriptor.m_ForgetIntermediateScale = 0.00001f;
+    descriptor.m_CellIntermediateScale   = 0.00001f;
+    descriptor.m_OutputIntermediateScale = 0.00001f;
+
+    descriptor.m_HiddenStateScale     = 0.07f;
+    descriptor.m_HiddenStateZeroPoint = 0;
+
+    const unsigned int numBatches = 2;
+    const unsigned int inputSize  = 5;
+    const unsigned int outputSize = 4;
+    const unsigned int numUnits   = 4;
+
+    // Scale/Offset quantization info
+    float inputScale    = 0.0078f;
+    int32_t inputOffset = 0;
+
+    float outputScale    = 0.0078f;
+    int32_t outputOffset = 0;
+
+    float cellStateScale    = 3.5002e-05f;
+    int32_t cellStateOffset = 0;
+
+    float weightsScale    = 0.007f;
+    int32_t weightsOffset = 0;
+
+    float biasScale    = 3.5002e-05f / 1024;
+    int32_t biasOffset = 0;
+
+    // Weights and bias tensor and quantization info
+    armnn::TensorInfo inputWeightsInfo({numUnits, inputSize},
+                                       armnn::DataType::QSymmS8,
+                                       weightsScale,
+                                       weightsOffset);
+
+    armnn::TensorInfo recurrentWeightsInfo({numUnits, outputSize},
+                                           armnn::DataType::QSymmS8,
+                                           weightsScale,
+                                           weightsOffset);
+
+    armnn::TensorInfo biasInfo({numUnits}, armnn::DataType::Signed32, biasScale, biasOffset);
+
+    std::vector<int8_t> inputToForgetWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
+    std::vector<int8_t> inputToCellWeightsData   = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
+    std::vector<int8_t> inputToOutputWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
+
+    armnn::ConstTensor inputToForgetWeights(inputWeightsInfo, inputToForgetWeightsData);
+    armnn::ConstTensor inputToCellWeights(inputWeightsInfo, inputToCellWeightsData);
+    armnn::ConstTensor inputToOutputWeights(inputWeightsInfo, inputToOutputWeightsData);
+
+    std::vector<int8_t> recurrentToForgetWeightsData =
+            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
+    std::vector<int8_t> recurrentToCellWeightsData   =
+            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
+    std::vector<int8_t> recurrentToOutputWeightsData =
+            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
+
+    armnn::ConstTensor recurrentToForgetWeights(recurrentWeightsInfo, recurrentToForgetWeightsData);
+    armnn::ConstTensor recurrentToCellWeights(recurrentWeightsInfo, recurrentToCellWeightsData);
+    armnn::ConstTensor recurrentToOutputWeights(recurrentWeightsInfo, recurrentToOutputWeightsData);
+
+    std::vector<int32_t> forgetGateBiasData(numUnits, 1);
+    std::vector<int32_t> cellBiasData(numUnits, 0);
+    std::vector<int32_t> outputGateBiasData(numUnits, 0);
+
+    armnn::ConstTensor forgetGateBias(biasInfo, forgetGateBiasData);
+    armnn::ConstTensor cellBias(biasInfo, cellBiasData);
+    armnn::ConstTensor outputGateBias(biasInfo, outputGateBiasData);
+
+    // Set up params
+    armnn::LstmInputParams params;
+    params.m_InputToForgetWeights = &inputToForgetWeights;
+    params.m_InputToCellWeights   = &inputToCellWeights;
+    params.m_InputToOutputWeights = &inputToOutputWeights;
+
+    params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
+    params.m_RecurrentToCellWeights   = &recurrentToCellWeights;
+    params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
+
+    params.m_ForgetGateBias = &forgetGateBias;
+    params.m_CellBias       = &cellBias;
+    params.m_OutputGateBias = &outputGateBias;
+
+    // Create network
+    armnn::INetworkPtr network = armnn::INetwork::Create();
+    const std::string layerName("qLstm");
+
+    armnn::IConnectableLayer* const input         = network->AddInputLayer(0);
+    armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(1);
+    armnn::IConnectableLayer* const cellStateIn   = network->AddInputLayer(2);
+
+    armnn::IConnectableLayer* const qLstmLayer = network->AddQLstmLayer(descriptor, params, layerName.c_str());
+
+    armnn::IConnectableLayer* const outputStateOut = network->AddOutputLayer(0);
+    armnn::IConnectableLayer* const cellStateOut   = network->AddOutputLayer(1);
+    armnn::IConnectableLayer* const outputLayer    = network->AddOutputLayer(2);
+
+    // Input/Output tensor info
+    armnn::TensorInfo inputInfo({numBatches , inputSize},
+                                armnn::DataType::QAsymmS8,
+                                inputScale,
+                                inputOffset);
+
+    armnn::TensorInfo cellStateInfo({numBatches , numUnits},
+                                    armnn::DataType::QSymmS16,
+                                    cellStateScale,
+                                    cellStateOffset);
+
+    armnn::TensorInfo outputStateInfo({numBatches , outputSize},
+                                      armnn::DataType::QAsymmS8,
+                                      outputScale,
+                                      outputOffset);
+
+    // Connect input/output slots
+    input->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(0));
+    input->GetOutputSlot(0).SetTensorInfo(inputInfo);
+
+    outputStateIn->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(1));
+    outputStateIn->GetOutputSlot(0).SetTensorInfo(cellStateInfo);
+
+    cellStateIn->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(2));
+    cellStateIn->GetOutputSlot(0).SetTensorInfo(outputStateInfo);
+
+    qLstmLayer->GetOutputSlot(0).Connect(outputStateOut->GetInputSlot(0));
+    qLstmLayer->GetOutputSlot(0).SetTensorInfo(outputStateInfo);
+
+    qLstmLayer->GetOutputSlot(1).Connect(cellStateOut->GetInputSlot(0));
+    qLstmLayer->GetOutputSlot(1).SetTensorInfo(cellStateInfo);
+
+    qLstmLayer->GetOutputSlot(2).Connect(outputLayer->GetInputSlot(0));
+    qLstmLayer->GetOutputSlot(2).SetTensorInfo(outputStateInfo);
+
+    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
+    BOOST_CHECK(deserializedNetwork);
+
+    VerifyLstmLayer<armnn::QLstmDescriptor> checker(
+            layerName,
+            {inputInfo, cellStateInfo, outputStateInfo},
+            {outputStateInfo, cellStateInfo, outputStateInfo},
+            descriptor,
+            params);
+
+    deserializedNetwork->ExecuteStrategy(checker);
+}
+
+BOOST_AUTO_TEST_CASE(SerializeDeserializeQLstmCifgLayerNorm)
+{
+    armnn::QLstmDescriptor descriptor;
+
+    // CIFG params are used when CIFG is disabled
+    descriptor.m_CifgEnabled       = true;
+    descriptor.m_ProjectionEnabled = false;
+    descriptor.m_PeepholeEnabled   = false;
+    descriptor.m_LayerNormEnabled  = true;
+
+    descriptor.m_CellClip       = 0.0f;
+    descriptor.m_ProjectionClip = 0.0f;
+
+    descriptor.m_InputIntermediateScale  = 0.00001f;
+    descriptor.m_ForgetIntermediateScale = 0.00001f;
+    descriptor.m_CellIntermediateScale   = 0.00001f;
+    descriptor.m_OutputIntermediateScale = 0.00001f;
+
+    descriptor.m_HiddenStateScale     = 0.07f;
+    descriptor.m_HiddenStateZeroPoint = 0;
+
+    const unsigned int numBatches = 2;
+    const unsigned int inputSize  = 5;
+    const unsigned int outputSize = 4;
+    const unsigned int numUnits   = 4;
+
+    // Scale/Offset quantization info
+    float inputScale    = 0.0078f;
+    int32_t inputOffset = 0;
+
+    float outputScale    = 0.0078f;
+    int32_t outputOffset = 0;
+
+    float cellStateScale    = 3.5002e-05f;
+    int32_t cellStateOffset = 0;
+
+    float weightsScale    = 0.007f;
+    int32_t weightsOffset = 0;
+
+    float layerNormScale    = 3.5002e-05f;
+    int32_t layerNormOffset = 0;
+
+    float biasScale    = layerNormScale / 1024;
+    int32_t biasOffset = 0;
+
+    // Weights and bias tensor and quantization info
+    armnn::TensorInfo inputWeightsInfo({numUnits, inputSize},
+                                       armnn::DataType::QSymmS8,
+                                       weightsScale,
+                                       weightsOffset);
+
+    armnn::TensorInfo recurrentWeightsInfo({numUnits, outputSize},
+                                           armnn::DataType::QSymmS8,
+                                           weightsScale,
+                                           weightsOffset);
+
+    armnn::TensorInfo biasInfo({numUnits},
+                               armnn::DataType::Signed32,
+                               biasScale,
+                               biasOffset);
+
+    armnn::TensorInfo layerNormWeightsInfo({numUnits},
+                                           armnn::DataType::QSymmS16,
+                                           layerNormScale,
+                                           layerNormOffset);
+
+    // Mandatory params
+    std::vector<int8_t> inputToForgetWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
+    std::vector<int8_t> inputToCellWeightsData   = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
+    std::vector<int8_t> inputToOutputWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
+
+    armnn::ConstTensor inputToForgetWeights(inputWeightsInfo, inputToForgetWeightsData);
+    armnn::ConstTensor inputToCellWeights(inputWeightsInfo, inputToCellWeightsData);
+    armnn::ConstTensor inputToOutputWeights(inputWeightsInfo, inputToOutputWeightsData);
+
+    std::vector<int8_t> recurrentToForgetWeightsData =
+            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
+    std::vector<int8_t> recurrentToCellWeightsData   =
+            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
+    std::vector<int8_t> recurrentToOutputWeightsData =
+            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
+
+    armnn::ConstTensor recurrentToForgetWeights(recurrentWeightsInfo, recurrentToForgetWeightsData);
+    armnn::ConstTensor recurrentToCellWeights(recurrentWeightsInfo, recurrentToCellWeightsData);
+    armnn::ConstTensor recurrentToOutputWeights(recurrentWeightsInfo, recurrentToOutputWeightsData);
+
+    std::vector<int32_t> forgetGateBiasData(numUnits, 1);
+    std::vector<int32_t> cellBiasData(numUnits, 0);
+    std::vector<int32_t> outputGateBiasData(numUnits, 0);
+
+    armnn::ConstTensor forgetGateBias(biasInfo, forgetGateBiasData);
+    armnn::ConstTensor cellBias(biasInfo, cellBiasData);
+    armnn::ConstTensor outputGateBias(biasInfo, outputGateBiasData);
+
+    // Layer Norm
+    std::vector<int16_t> forgetLayerNormWeightsData =
+            GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
+    std::vector<int16_t> cellLayerNormWeightsData =
+            GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
+    std::vector<int16_t> outputLayerNormWeightsData =
+            GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
+
+    armnn::ConstTensor forgetLayerNormWeights(layerNormWeightsInfo, forgetLayerNormWeightsData);
+    armnn::ConstTensor cellLayerNormWeights(layerNormWeightsInfo, cellLayerNormWeightsData);
+    armnn::ConstTensor outputLayerNormWeights(layerNormWeightsInfo, outputLayerNormWeightsData);
+
+    // Set up params
+    armnn::LstmInputParams params;
+
+    // Mandatory params
+    params.m_InputToForgetWeights = &inputToForgetWeights;
+    params.m_InputToCellWeights   = &inputToCellWeights;
+    params.m_InputToOutputWeights = &inputToOutputWeights;
+
+    params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
+    params.m_RecurrentToCellWeights   = &recurrentToCellWeights;
+    params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
+
+    params.m_ForgetGateBias = &forgetGateBias;
+    params.m_CellBias       = &cellBias;
+    params.m_OutputGateBias = &outputGateBias;
+
+    // Layer Norm
+    params.m_ForgetLayerNormWeights = &forgetLayerNormWeights;
+    params.m_CellLayerNormWeights   = &cellLayerNormWeights;
+    params.m_OutputLayerNormWeights = &outputLayerNormWeights;
+
+    // Create network
+    armnn::INetworkPtr network = armnn::INetwork::Create();
+    const std::string layerName("qLstm");
+
+    armnn::IConnectableLayer* const input         = network->AddInputLayer(0);
+    armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(1);
+    armnn::IConnectableLayer* const cellStateIn   = network->AddInputLayer(2);
+
+    armnn::IConnectableLayer* const qLstmLayer = network->AddQLstmLayer(descriptor, params, layerName.c_str());
+
+    armnn::IConnectableLayer* const outputStateOut  = network->AddOutputLayer(0);
+    armnn::IConnectableLayer* const cellStateOut  = network->AddOutputLayer(1);
+    armnn::IConnectableLayer* const outputLayer  = network->AddOutputLayer(2);
+
+    // Input/Output tensor info
+    armnn::TensorInfo inputInfo({numBatches , inputSize},
+                                armnn::DataType::QAsymmS8,
+                                inputScale,
+                                inputOffset);
+
+    armnn::TensorInfo cellStateInfo({numBatches , numUnits},
+                                    armnn::DataType::QSymmS16,
+                                    cellStateScale,
+                                    cellStateOffset);
+
+    armnn::TensorInfo outputStateInfo({numBatches , outputSize},
+                                      armnn::DataType::QAsymmS8,
+                                      outputScale,
+                                      outputOffset);
+
+    // Connect input/output slots
+    input->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(0));
+    input->GetOutputSlot(0).SetTensorInfo(inputInfo);
+
+    outputStateIn->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(1));
+    outputStateIn->GetOutputSlot(0).SetTensorInfo(cellStateInfo);
+
+    cellStateIn->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(2));
+    cellStateIn->GetOutputSlot(0).SetTensorInfo(outputStateInfo);
+
+    qLstmLayer->GetOutputSlot(0).Connect(outputStateOut->GetInputSlot(0));
+    qLstmLayer->GetOutputSlot(0).SetTensorInfo(outputStateInfo);
+
+    qLstmLayer->GetOutputSlot(1).Connect(cellStateOut->GetInputSlot(0));
+    qLstmLayer->GetOutputSlot(1).SetTensorInfo(cellStateInfo);
+
+    qLstmLayer->GetOutputSlot(2).Connect(outputLayer->GetInputSlot(0));
+    qLstmLayer->GetOutputSlot(2).SetTensorInfo(outputStateInfo);
+
+    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
+    BOOST_CHECK(deserializedNetwork);
+
+    VerifyLstmLayer<armnn::QLstmDescriptor> checker(layerName,
+                                                    {inputInfo, cellStateInfo, outputStateInfo},
+                                                    {outputStateInfo, cellStateInfo, outputStateInfo},
+                                                    descriptor,
+                                                    params);
+
+    deserializedNetwork->ExecuteStrategy(checker);
+}
+
+BOOST_AUTO_TEST_CASE(SerializeDeserializeQLstmAdvanced)
+{
+    armnn::QLstmDescriptor descriptor;
+
+    descriptor.m_CifgEnabled       = false;
+    descriptor.m_ProjectionEnabled = true;
+    descriptor.m_PeepholeEnabled   = true;
+    descriptor.m_LayerNormEnabled  = true;
+
+    descriptor.m_CellClip       = 0.1f;
+    descriptor.m_ProjectionClip = 0.1f;
+
+    descriptor.m_InputIntermediateScale  = 0.00001f;
+    descriptor.m_ForgetIntermediateScale = 0.00001f;
+    descriptor.m_CellIntermediateScale   = 0.00001f;
+    descriptor.m_OutputIntermediateScale = 0.00001f;
+
+    descriptor.m_HiddenStateScale     = 0.07f;
+    descriptor.m_HiddenStateZeroPoint = 0;
+
+    const unsigned int numBatches = 2;
+    const unsigned int inputSize  = 5;
+    const unsigned int outputSize = 4;
+    const unsigned int numUnits   = 4;
+
+    // Scale/Offset quantization info
+    float inputScale    = 0.0078f;
+    int32_t inputOffset = 0;
+
+    float outputScale    = 0.0078f;
+    int32_t outputOffset = 0;
+
+    float cellStateScale    = 3.5002e-05f;
+    int32_t cellStateOffset = 0;
+
+    float weightsScale    = 0.007f;
+    int32_t weightsOffset = 0;
+
+    float layerNormScale    = 3.5002e-05f;
+    int32_t layerNormOffset = 0;
+
+    float biasScale    = layerNormScale / 1024;
+    int32_t biasOffset = 0;
+
+    // Weights and bias tensor and quantization info
+    armnn::TensorInfo inputWeightsInfo({numUnits, inputSize},
+                                       armnn::DataType::QSymmS8,
+                                       weightsScale,
+                                       weightsOffset);
+
+    armnn::TensorInfo recurrentWeightsInfo({numUnits, outputSize},
+                                           armnn::DataType::QSymmS8,
+                                           weightsScale,
+                                           weightsOffset);
+
+    armnn::TensorInfo biasInfo({numUnits},
+                               armnn::DataType::Signed32,
+                               biasScale,
+                               biasOffset);
+
+    armnn::TensorInfo peepholeWeightsInfo({numUnits},
+                                          armnn::DataType::QSymmS16,
+                                          weightsScale,
+                                          weightsOffset);
+
+    armnn::TensorInfo layerNormWeightsInfo({numUnits},
+                                           armnn::DataType::QSymmS16,
+                                           layerNormScale,
+                                           layerNormOffset);
+
+    armnn::TensorInfo projectionWeightsInfo({outputSize, numUnits},
+                                             armnn::DataType::QSymmS8,
+                                             weightsScale,
+                                             weightsOffset);
+
+    // Mandatory params
+    std::vector<int8_t> inputToForgetWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
+    std::vector<int8_t> inputToCellWeightsData   = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
+    std::vector<int8_t> inputToOutputWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
+
+    armnn::ConstTensor inputToForgetWeights(inputWeightsInfo, inputToForgetWeightsData);
+    armnn::ConstTensor inputToCellWeights(inputWeightsInfo, inputToCellWeightsData);
+    armnn::ConstTensor inputToOutputWeights(inputWeightsInfo, inputToOutputWeightsData);
+
+    std::vector<int8_t> recurrentToForgetWeightsData =
+            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
+    std::vector<int8_t> recurrentToCellWeightsData   =
+            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
+    std::vector<int8_t> recurrentToOutputWeightsData =
+            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
+
+    armnn::ConstTensor recurrentToForgetWeights(recurrentWeightsInfo, recurrentToForgetWeightsData);
+    armnn::ConstTensor recurrentToCellWeights(recurrentWeightsInfo, recurrentToCellWeightsData);
+    armnn::ConstTensor recurrentToOutputWeights(recurrentWeightsInfo, recurrentToOutputWeightsData);
+
+    std::vector<int32_t> forgetGateBiasData(numUnits, 1);
+    std::vector<int32_t> cellBiasData(numUnits, 0);
+    std::vector<int32_t> outputGateBiasData(numUnits, 0);
+
+    armnn::ConstTensor forgetGateBias(biasInfo, forgetGateBiasData);
+    armnn::ConstTensor cellBias(biasInfo, cellBiasData);
+    armnn::ConstTensor outputGateBias(biasInfo, outputGateBiasData);
+
+    // CIFG
+    std::vector<int8_t> inputToInputWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
+    std::vector<int8_t> recurrentToInputWeightsData =
+            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
+    std::vector<int32_t> inputGateBiasData(numUnits, 1);
+
+    armnn::ConstTensor inputToInputWeights(inputWeightsInfo, inputToInputWeightsData);
+    armnn::ConstTensor recurrentToInputWeights(recurrentWeightsInfo, recurrentToInputWeightsData);
+    armnn::ConstTensor inputGateBias(biasInfo, inputGateBiasData);
+
+    // Peephole
+    std::vector<int16_t> cellToInputWeightsData  = GenerateRandomData<int16_t>(peepholeWeightsInfo.GetNumElements());
+    std::vector<int16_t> cellToForgetWeightsData = GenerateRandomData<int16_t>(peepholeWeightsInfo.GetNumElements());
+    std::vector<int16_t> cellToOutputWeightsData = GenerateRandomData<int16_t>(peepholeWeightsInfo.GetNumElements());
+
+    armnn::ConstTensor cellToInputWeights(peepholeWeightsInfo, cellToInputWeightsData);
+    armnn::ConstTensor cellToForgetWeights(peepholeWeightsInfo, cellToForgetWeightsData);
+    armnn::ConstTensor cellToOutputWeights(peepholeWeightsInfo, cellToOutputWeightsData);
+
+    // Projection
+    std::vector<int8_t> projectionWeightsData = GenerateRandomData<int8_t>(projectionWeightsInfo.GetNumElements());
+    std::vector<int32_t> projectionBiasData(outputSize, 1);
+
+    armnn::ConstTensor projectionWeights(projectionWeightsInfo, projectionWeightsData);
+    armnn::ConstTensor projectionBias(biasInfo, projectionBiasData);
+
+    // Layer Norm
+    std::vector<int16_t> inputLayerNormWeightsData =
+            GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
+    std::vector<int16_t> forgetLayerNormWeightsData =
+            GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
+    std::vector<int16_t> cellLayerNormWeightsData =
+            GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
+    std::vector<int16_t> outputLayerNormWeightsData =
+            GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
+
+    armnn::ConstTensor inputLayerNormWeights(layerNormWeightsInfo, inputLayerNormWeightsData);
+    armnn::ConstTensor forgetLayerNormWeights(layerNormWeightsInfo, forgetLayerNormWeightsData);
+    armnn::ConstTensor cellLayerNormWeights(layerNormWeightsInfo, cellLayerNormWeightsData);
+    armnn::ConstTensor outputLayerNormWeights(layerNormWeightsInfo, outputLayerNormWeightsData);
+
+    // Set up params
+    armnn::LstmInputParams params;
+
+    // Mandatory params
+    params.m_InputToForgetWeights = &inputToForgetWeights;
+    params.m_InputToCellWeights   = &inputToCellWeights;
+    params.m_InputToOutputWeights = &inputToOutputWeights;
+
+    params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
+    params.m_RecurrentToCellWeights   = &recurrentToCellWeights;
+    params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
+
+    params.m_ForgetGateBias = &forgetGateBias;
+    params.m_CellBias       = &cellBias;
+    params.m_OutputGateBias = &outputGateBias;
+
+    // CIFG
+    params.m_InputToInputWeights     = &inputToInputWeights;
+    params.m_RecurrentToInputWeights = &recurrentToInputWeights;
+    params.m_InputGateBias           = &inputGateBias;
+
+    // Peephole
+    params.m_CellToInputWeights  = &cellToInputWeights;
+    params.m_CellToForgetWeights = &cellToForgetWeights;
+    params.m_CellToOutputWeights = &cellToOutputWeights;
+
+    // Projection
+    params.m_ProjectionWeights = &projectionWeights;
+    params.m_ProjectionBias    = &projectionBias;
+
+    // Layer Norm
+    params.m_InputLayerNormWeights  = &inputLayerNormWeights;
+    params.m_ForgetLayerNormWeights = &forgetLayerNormWeights;
+    params.m_CellLayerNormWeights   = &cellLayerNormWeights;
+    params.m_OutputLayerNormWeights = &outputLayerNormWeights;
+
+    // Create network
+    armnn::INetworkPtr network = armnn::INetwork::Create();
+    const std::string layerName("qLstm");
+
+    armnn::IConnectableLayer* const input         = network->AddInputLayer(0);
+    armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(1);
+    armnn::IConnectableLayer* const cellStateIn   = network->AddInputLayer(2);
+
+    armnn::IConnectableLayer* const qLstmLayer = network->AddQLstmLayer(descriptor, params, layerName.c_str());
+
+    armnn::IConnectableLayer* const outputStateOut = network->AddOutputLayer(0);
+    armnn::IConnectableLayer* const cellStateOut   = network->AddOutputLayer(1);
+    armnn::IConnectableLayer* const outputLayer    = network->AddOutputLayer(2);
+
+    // Input/Output tensor info
+    armnn::TensorInfo inputInfo({numBatches , inputSize},
+                                armnn::DataType::QAsymmS8,
+                                inputScale,
+                                inputOffset);
+
+    armnn::TensorInfo cellStateInfo({numBatches , numUnits},
+                                    armnn::DataType::QSymmS16,
+                                    cellStateScale,
+                                    cellStateOffset);
+
+    armnn::TensorInfo outputStateInfo({numBatches , outputSize},
+                                      armnn::DataType::QAsymmS8,
+                                      outputScale,
+                                      outputOffset);
+
+    // Connect input/output slots
+    input->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(0));
+    input->GetOutputSlot(0).SetTensorInfo(inputInfo);
+
+    outputStateIn->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(1));
+    outputStateIn->GetOutputSlot(0).SetTensorInfo(cellStateInfo);
+
+    cellStateIn->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(2));
+    cellStateIn->GetOutputSlot(0).SetTensorInfo(outputStateInfo);
+
+    qLstmLayer->GetOutputSlot(0).Connect(outputStateOut->GetInputSlot(0));
+    qLstmLayer->GetOutputSlot(0).SetTensorInfo(outputStateInfo);
+
+    qLstmLayer->GetOutputSlot(1).Connect(cellStateOut->GetInputSlot(0));
+    qLstmLayer->GetOutputSlot(1).SetTensorInfo(cellStateInfo);
+
+    qLstmLayer->GetOutputSlot(2).Connect(outputLayer->GetInputSlot(0));
+    qLstmLayer->GetOutputSlot(2).SetTensorInfo(outputStateInfo);
+
+    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
+    BOOST_CHECK(deserializedNetwork);
+
+    VerifyLstmLayer<armnn::QLstmDescriptor> checker(layerName,
+                                                    {inputInfo, cellStateInfo, outputStateInfo},
+                                                    {outputStateInfo, cellStateInfo, outputStateInfo},
+                                                    descriptor,
+                                                    params);
+
+    deserializedNetwork->ExecuteStrategy(checker);
+}
+
+BOOST_AUTO_TEST_SUITE_END()
diff --git a/src/armnnSerializer/test/SerializerTestUtils.cpp b/src/armnnSerializer/test/SerializerTestUtils.cpp
new file mode 100644
index 0000000000..586d2a05a5
--- /dev/null
+++ b/src/armnnSerializer/test/SerializerTestUtils.cpp
@@ -0,0 +1,163 @@
+//
+// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "SerializerTestUtils.hpp"
+#include "../Serializer.hpp"
+
+using armnnDeserializer::IDeserializer;
+
+LayerVerifierBase::LayerVerifierBase(const std::string& layerName,
+                                     const std::vector<armnn::TensorInfo>& inputInfos,
+                                     const std::vector<armnn::TensorInfo>& outputInfos)
+                                     : m_LayerName(layerName)
+                                     , m_InputTensorInfos(inputInfos)
+                                     , m_OutputTensorInfos(outputInfos)
+{}
+
+void LayerVerifierBase::ExecuteStrategy(const armnn::IConnectableLayer* layer,
+                     const armnn::BaseDescriptor& descriptor,
+                     const std::vector<armnn::ConstTensor>& constants,
+                     const char* name,
+                     const armnn::LayerBindingId id)
+{
+    armnn::IgnoreUnused(descriptor, constants, id);
+    switch (layer->GetType())
+    {
+        case armnn::LayerType::Input: break;
+        case armnn::LayerType::Output: break;
+        default:
+        {
+            VerifyNameAndConnections(layer, name);
+        }
+    }
+}
+
+
+void LayerVerifierBase::VerifyNameAndConnections(const armnn::IConnectableLayer* layer, const char* name)
+{
+    BOOST_TEST(name == m_LayerName.c_str());
+
+    BOOST_TEST(layer->GetNumInputSlots() == m_InputTensorInfos.size());
+    BOOST_TEST(layer->GetNumOutputSlots() == m_OutputTensorInfos.size());
+
+    for (unsigned int i = 0; i < m_InputTensorInfos.size(); i++)
+    {
+        const armnn::IOutputSlot* connectedOutput = layer->GetInputSlot(i).GetConnection();
+        BOOST_CHECK(connectedOutput);
+
+        const armnn::TensorInfo& connectedInfo = connectedOutput->GetTensorInfo();
+        BOOST_TEST(connectedInfo.GetShape() == m_InputTensorInfos[i].GetShape());
+        BOOST_TEST(
+            GetDataTypeName(connectedInfo.GetDataType()) == GetDataTypeName(m_InputTensorInfos[i].GetDataType()));
+
+        BOOST_TEST(connectedInfo.GetQuantizationScale() == m_InputTensorInfos[i].GetQuantizationScale());
+        BOOST_TEST(connectedInfo.GetQuantizationOffset() == m_InputTensorInfos[i].GetQuantizationOffset());
+    }
+
+    for (unsigned int i = 0; i < m_OutputTensorInfos.size(); i++)
+    {
+        const armnn::TensorInfo& outputInfo = layer->GetOutputSlot(i).GetTensorInfo();
+        BOOST_TEST(outputInfo.GetShape() == m_OutputTensorInfos[i].GetShape());
+        BOOST_TEST(
+            GetDataTypeName(outputInfo.GetDataType()) == GetDataTypeName(m_OutputTensorInfos[i].GetDataType()));
+
+        BOOST_TEST(outputInfo.GetQuantizationScale() == m_OutputTensorInfos[i].GetQuantizationScale());
+        BOOST_TEST(outputInfo.GetQuantizationOffset() == m_OutputTensorInfos[i].GetQuantizationOffset());
+    }
+}
+
+void LayerVerifierBase::VerifyConstTensors(const std::string& tensorName,
+                                           const armnn::ConstTensor* expectedPtr,
+                                           const armnn::ConstTensor* actualPtr)
+{
+    if (expectedPtr == nullptr)
+    {
+        BOOST_CHECK_MESSAGE(actualPtr == nullptr, tensorName + " should not exist");
+    }
+    else
+    {
+        BOOST_CHECK_MESSAGE(actualPtr != nullptr, tensorName + " should have been set");
+        if (actualPtr != nullptr)
+        {
+            const armnn::TensorInfo& expectedInfo = expectedPtr->GetInfo();
+            const armnn::TensorInfo& actualInfo = actualPtr->GetInfo();
+
+            BOOST_CHECK_MESSAGE(expectedInfo.GetShape() == actualInfo.GetShape(),
+                                tensorName + " shapes don't match");
+            BOOST_CHECK_MESSAGE(
+                    GetDataTypeName(expectedInfo.GetDataType()) == GetDataTypeName(actualInfo.GetDataType()),
+                    tensorName + " data types don't match");
+
+            BOOST_CHECK_MESSAGE(expectedPtr->GetNumBytes() == actualPtr->GetNumBytes(),
+                                tensorName + " (GetNumBytes) data sizes do not match");
+            if (expectedPtr->GetNumBytes() == actualPtr->GetNumBytes())
+            {
+                //check the data is identical
+                const char* expectedData = static_cast<const char*>(expectedPtr->GetMemoryArea());
+                const char* actualData = static_cast<const char*>(actualPtr->GetMemoryArea());
+                bool same = true;
+                for (unsigned int i = 0; i < expectedPtr->GetNumBytes(); ++i)
+                {
+                    same = expectedData[i] == actualData[i];
+                    if (!same)
+                    {
+                        break;
+                    }
+                }
+                BOOST_CHECK_MESSAGE(same, tensorName + " data does not match");
+            }
+        }
+    }
+}
+
+void CompareConstTensor(const armnn::ConstTensor& tensor1, const armnn::ConstTensor& tensor2)
+{
+    BOOST_TEST(tensor1.GetShape() == tensor2.GetShape());
+    BOOST_TEST(GetDataTypeName(tensor1.GetDataType()) == GetDataTypeName(tensor2.GetDataType()));
+
+    switch (tensor1.GetDataType())
+    {
+        case armnn::DataType::Float32:
+            CompareConstTensorData<const float*>(
+                tensor1.GetMemoryArea(), tensor2.GetMemoryArea(), tensor1.GetNumElements());
+            break;
+        case armnn::DataType::QAsymmU8:
+        case armnn::DataType::Boolean:
+            CompareConstTensorData<const uint8_t*>(
+                tensor1.GetMemoryArea(), tensor2.GetMemoryArea(), tensor1.GetNumElements());
+            break;
+        case armnn::DataType::QSymmS8:
+            CompareConstTensorData<const int8_t*>(
+                tensor1.GetMemoryArea(), tensor2.GetMemoryArea(), tensor1.GetNumElements());
+            break;
+        case armnn::DataType::Signed32:
+            CompareConstTensorData<const int32_t*>(
+                tensor1.GetMemoryArea(), tensor2.GetMemoryArea(), tensor1.GetNumElements());
+            break;
+        default:
+            // Note that Float16 is not yet implemented
+            BOOST_TEST_MESSAGE("Unexpected datatype");
+            BOOST_TEST(false);
+    }
+}
+
+armnn::INetworkPtr DeserializeNetwork(const std::string& serializerString)
+{
+    std::vector<std::uint8_t> const serializerVector{serializerString.begin(), serializerString.end()};
+    return IDeserializer::Create()->CreateNetworkFromBinary(serializerVector);
+}
+
+std::string SerializeNetwork(const armnn::INetwork& network)
+{
+    armnnSerializer::ISerializerPtr serializer = armnnSerializer::ISerializer::Create();
+
+    serializer->Serialize(network);
+
+    std::stringstream stream;
+    serializer->SaveSerializedToStream(stream);
+
+    std::string serializerString{stream.str()};
+    return serializerString;
+}
diff --git a/src/armnnSerializer/test/SerializerTestUtils.hpp b/src/armnnSerializer/test/SerializerTestUtils.hpp
new file mode 100644
index 0000000000..e085d2ef15
--- /dev/null
+++ b/src/armnnSerializer/test/SerializerTestUtils.hpp
@@ -0,0 +1,167 @@
+//
+// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include <armnn/Descriptors.hpp>
+#include <armnn/INetwork.hpp>
+#include <armnn/TypesUtils.hpp>
+#include <armnnDeserializer/IDeserializer.hpp>
+#include <armnn/utility/IgnoreUnused.hpp>
+
+#include <random>
+#include <vector>
+
+#include <boost/test/unit_test.hpp>
+
+
+armnn::INetworkPtr DeserializeNetwork(const std::string& serializerString);
+
+std::string SerializeNetwork(const armnn::INetwork& network);
+
+void CompareConstTensor(const armnn::ConstTensor& tensor1, const armnn::ConstTensor& tensor2);
+
+class LayerVerifierBase : public armnn::IStrategy
+{
+public:
+    LayerVerifierBase(const std::string& layerName,
+                      const std::vector<armnn::TensorInfo>& inputInfos,
+                      const std::vector<armnn::TensorInfo>& outputInfos);
+
+    void ExecuteStrategy(const armnn::IConnectableLayer* layer,
+                         const armnn::BaseDescriptor& descriptor,
+                         const std::vector<armnn::ConstTensor>& constants,
+                         const char* name,
+                         const armnn::LayerBindingId id = 0) override;
+
+protected:
+    void VerifyNameAndConnections(const armnn::IConnectableLayer* layer, const char* name);
+
+    void VerifyConstTensors(const std::string& tensorName,
+                            const armnn::ConstTensor* expectedPtr,
+                            const armnn::ConstTensor* actualPtr);
+
+private:
+    std::string m_LayerName;
+    std::vector<armnn::TensorInfo> m_InputTensorInfos;
+    std::vector<armnn::TensorInfo> m_OutputTensorInfos;
+};
+
+template<typename Descriptor>
+class LayerVerifierBaseWithDescriptor : public LayerVerifierBase
+{
+public:
+    LayerVerifierBaseWithDescriptor(const std::string& layerName,
+                                    const std::vector<armnn::TensorInfo>& inputInfos,
+                                    const std::vector<armnn::TensorInfo>& outputInfos,
+                                    const Descriptor& descriptor)
+        : LayerVerifierBase(layerName, inputInfos, outputInfos)
+        , m_Descriptor(descriptor) {}
+
+    void ExecuteStrategy(const armnn::IConnectableLayer* layer,
+                         const armnn::BaseDescriptor& descriptor,
+                         const std::vector<armnn::ConstTensor>& constants,
+                         const char* name,
+                         const armnn::LayerBindingId id = 0) override
+    {
+        armnn::IgnoreUnused(constants, id);
+        switch (layer->GetType())
+        {
+            case armnn::LayerType::Input: break;
+            case armnn::LayerType::Output: break;
+            default:
+            {
+                VerifyNameAndConnections(layer, name);
+                const Descriptor& internalDescriptor = static_cast<const Descriptor&>(descriptor);
+                VerifyDescriptor(internalDescriptor);
+                break;
+            }
+        }
+    }
+
+protected:
+    void VerifyDescriptor(const Descriptor& descriptor)
+    {
+        BOOST_CHECK(descriptor == m_Descriptor);
+    }
+
+    Descriptor m_Descriptor;
+};
+
+template<typename T>
+void CompareConstTensorData(const void* data1, const void* data2, unsigned int numElements)
+{
+    T typedData1 = static_cast<T>(data1);
+    T typedData2 = static_cast<T>(data2);
+    BOOST_CHECK(typedData1);
+    BOOST_CHECK(typedData2);
+
+    for (unsigned int i = 0; i < numElements; i++)
+    {
+        BOOST_TEST(typedData1[i] == typedData2[i]);
+    }
+}
+
+
+template <typename Descriptor>
+class LayerVerifierBaseWithDescriptorAndConstants : public LayerVerifierBaseWithDescriptor<Descriptor>
+{
+public:
+    LayerVerifierBaseWithDescriptorAndConstants(const std::string& layerName,
+                                                const std::vector<armnn::TensorInfo>& inputInfos,
+                                                const std::vector<armnn::TensorInfo>& outputInfos,
+                                                const Descriptor& descriptor,
+                                                const std::vector<armnn::ConstTensor>& constants)
+            : LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor)
+            , m_Constants(constants) {}
+
+    void ExecuteStrategy(const armnn::IConnectableLayer* layer,
+                         const armnn::BaseDescriptor& descriptor,
+                         const std::vector<armnn::ConstTensor>& constants,
+                         const char* name,
+                         const armnn::LayerBindingId id = 0) override
+    {
+        armnn::IgnoreUnused(id);
+
+        switch (layer->GetType())
+        {
+            case armnn::LayerType::Input: break;
+            case armnn::LayerType::Output: break;
+            default:
+            {
+                this->VerifyNameAndConnections(layer, name);
+                const Descriptor& internalDescriptor = static_cast<const Descriptor&>(descriptor);
+                this->VerifyDescriptor(internalDescriptor);
+
+                for(std::size_t i = 0; i < constants.size(); i++)
+                {
+                    CompareConstTensor(constants[i], m_Constants[i]);
+                }
+            }
+        }
+    }
+
+private:
+    std::vector<armnn::ConstTensor> m_Constants;
+};
+
+template<typename DataType>
+static std::vector<DataType> GenerateRandomData(size_t size)
+{
+    constexpr bool isIntegerType = std::is_integral<DataType>::value;
+    using Distribution =
+        typename std::conditional<isIntegerType,
+                                  std::uniform_int_distribution<DataType>,
+                                  std::uniform_real_distribution<DataType>>::type;
+
+    static constexpr DataType lowerLimit = std::numeric_limits<DataType>::min();
+    static constexpr DataType upperLimit = std::numeric_limits<DataType>::max();
+
+    static Distribution distribution(lowerLimit, upperLimit);
+    static std::default_random_engine generator;
+
+    std::vector<DataType> randomData(size);
+    std::generate(randomData.begin(), randomData.end(), []() { return distribution(generator); });
+
+    return randomData;
+}
\ No newline at end of file
diff --git a/src/armnnSerializer/test/SerializerTests.cpp b/src/armnnSerializer/test/SerializerTests.cpp
index 44e8a3898e..f261731a75 100644
--- a/src/armnnSerializer/test/SerializerTests.cpp
+++ b/src/armnnSerializer/test/SerializerTests.cpp
@@ -4,6 +4,7 @@
 //
 
 #include "../Serializer.hpp"
+#include "SerializerTestUtils.hpp"
 
 #include <armnn/Descriptors.hpp>
 #include <armnn/INetwork.hpp>
@@ -11,6 +12,7 @@
 #include <armnn/LstmParams.hpp>
 #include <armnn/QuantizedLstmParams.hpp>
 #include <armnnDeserializer/IDeserializer.hpp>
+#include <armnn/utility/IgnoreUnused.hpp>
 
 #include <random>
 #include <vector>
@@ -19,264 +21,36 @@
 
 using armnnDeserializer::IDeserializer;
 
-namespace
-{
-
-#define DECLARE_LAYER_VERIFIER_CLASS(name) \
-class name##LayerVerifier : public LayerVerifierBase \
-{ \
-public: \
-    name##LayerVerifier(const std::string& layerName, \
-                        const std::vector<armnn::TensorInfo>& inputInfos, \
-                        const std::vector<armnn::TensorInfo>& outputInfos) \
-        : LayerVerifierBase(layerName, inputInfos, outputInfos) {} \
-\
-    void Visit##name##Layer(const armnn::IConnectableLayer* layer, const char* name) override \
-    { \
-        VerifyNameAndConnections(layer, name); \
-    } \
-};
-
-#define DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(name) \
-class name##LayerVerifier : public LayerVerifierBaseWithDescriptor<armnn::name##Descriptor> \
-{ \
-public: \
-    name##LayerVerifier(const std::string& layerName, \
-                        const std::vector<armnn::TensorInfo>& inputInfos, \
-                        const std::vector<armnn::TensorInfo>& outputInfos, \
-                        const armnn::name##Descriptor& descriptor) \
-        : LayerVerifierBaseWithDescriptor<armnn::name##Descriptor>( \
-            layerName, inputInfos, outputInfos, descriptor) {} \
-\
-    void Visit##name##Layer(const armnn::IConnectableLayer* layer, \
-                            const armnn::name##Descriptor& descriptor, \
-                            const char* name) override \
-    { \
-        VerifyNameAndConnections(layer, name); \
-        VerifyDescriptor(descriptor); \
-    } \
-};
-
-struct DefaultLayerVerifierPolicy
-{
-    static void Apply(const std::string)
-    {
-        BOOST_TEST_MESSAGE("Unexpected layer found in network");
-        BOOST_TEST(false);
-    }
-};
-
-class LayerVerifierBase : public armnn::LayerVisitorBase<DefaultLayerVerifierPolicy>
-{
-public:
-    LayerVerifierBase(const std::string& layerName,
-                      const std::vector<armnn::TensorInfo>& inputInfos,
-                      const std::vector<armnn::TensorInfo>& outputInfos)
-    : m_LayerName(layerName)
-    , m_InputTensorInfos(inputInfos)
-    , m_OutputTensorInfos(outputInfos) {}
-
-    void VisitInputLayer(const armnn::IConnectableLayer*, armnn::LayerBindingId, const char*) override {}
-
-    void VisitOutputLayer(const armnn::IConnectableLayer*, armnn::LayerBindingId, const char*) override {}
-
-protected:
-    void VerifyNameAndConnections(const armnn::IConnectableLayer* layer, const char* name)
-    {
-        BOOST_TEST(name == m_LayerName.c_str());
-
-        BOOST_TEST(layer->GetNumInputSlots() == m_InputTensorInfos.size());
-        BOOST_TEST(layer->GetNumOutputSlots() == m_OutputTensorInfos.size());
-
-        for (unsigned int i = 0; i < m_InputTensorInfos.size(); i++)
-        {
-            const armnn::IOutputSlot* connectedOutput = layer->GetInputSlot(i).GetConnection();
-            BOOST_CHECK(connectedOutput);
-
-            const armnn::TensorInfo& connectedInfo = connectedOutput->GetTensorInfo();
-            BOOST_TEST(connectedInfo.GetShape() == m_InputTensorInfos[i].GetShape());
-            BOOST_TEST(
-                GetDataTypeName(connectedInfo.GetDataType()) == GetDataTypeName(m_InputTensorInfos[i].GetDataType()));
-
-            BOOST_TEST(connectedInfo.GetQuantizationScale() == m_InputTensorInfos[i].GetQuantizationScale());
-            BOOST_TEST(connectedInfo.GetQuantizationOffset() == m_InputTensorInfos[i].GetQuantizationOffset());
-        }
-
-        for (unsigned int i = 0; i < m_OutputTensorInfos.size(); i++)
-        {
-            const armnn::TensorInfo& outputInfo = layer->GetOutputSlot(i).GetTensorInfo();
-            BOOST_TEST(outputInfo.GetShape() == m_OutputTensorInfos[i].GetShape());
-            BOOST_TEST(
-                GetDataTypeName(outputInfo.GetDataType()) == GetDataTypeName(m_OutputTensorInfos[i].GetDataType()));
-
-            BOOST_TEST(outputInfo.GetQuantizationScale() == m_OutputTensorInfos[i].GetQuantizationScale());
-            BOOST_TEST(outputInfo.GetQuantizationOffset() == m_OutputTensorInfos[i].GetQuantizationOffset());
-        }
-    }
-
-    void VerifyConstTensors(const std::string& tensorName,
-                            const armnn::ConstTensor* expectedPtr,
-                            const armnn::ConstTensor* actualPtr)
-    {
-        if (expectedPtr == nullptr)
-        {
-            BOOST_CHECK_MESSAGE(actualPtr == nullptr, tensorName + " should not exist");
-        }
-        else
-        {
-            BOOST_CHECK_MESSAGE(actualPtr != nullptr, tensorName + " should have been set");
-            if (actualPtr != nullptr)
-            {
-                const armnn::TensorInfo& expectedInfo = expectedPtr->GetInfo();
-                const armnn::TensorInfo& actualInfo = actualPtr->GetInfo();
-
-                BOOST_CHECK_MESSAGE(expectedInfo.GetShape() == actualInfo.GetShape(),
-                                    tensorName + " shapes don't match");
-                BOOST_CHECK_MESSAGE(
-                        GetDataTypeName(expectedInfo.GetDataType()) == GetDataTypeName(actualInfo.GetDataType()),
-                        tensorName + " data types don't match");
-
-                BOOST_CHECK_MESSAGE(expectedPtr->GetNumBytes() == actualPtr->GetNumBytes(),
-                                    tensorName + " (GetNumBytes) data sizes do not match");
-                if (expectedPtr->GetNumBytes() == actualPtr->GetNumBytes())
-                {
-                    //check the data is identical
-                    const char* expectedData = static_cast<const char*>(expectedPtr->GetMemoryArea());
-                    const char* actualData = static_cast<const char*>(actualPtr->GetMemoryArea());
-                    bool same = true;
-                    for (unsigned int i = 0; i < expectedPtr->GetNumBytes(); ++i)
-                    {
-                        same = expectedData[i] == actualData[i];
-                        if (!same)
-                        {
-                            break;
-                        }
-                    }
-                    BOOST_CHECK_MESSAGE(same, tensorName + " data does not match");
-                }
-            }
-        }
-    }
-
-private:
-    std::string m_LayerName;
-    std::vector<armnn::TensorInfo> m_InputTensorInfos;
-    std::vector<armnn::TensorInfo> m_OutputTensorInfos;
-};
-
-template<typename Descriptor>
-class LayerVerifierBaseWithDescriptor : public LayerVerifierBase
-{
-public:
-    LayerVerifierBaseWithDescriptor(const std::string& layerName,
-                                    const std::vector<armnn::TensorInfo>& inputInfos,
-                                    const std::vector<armnn::TensorInfo>& outputInfos,
-                                    const Descriptor& descriptor)
-        : LayerVerifierBase(layerName, inputInfos, outputInfos)
-        , m_Descriptor(descriptor) {}
-
-protected:
-    void VerifyDescriptor(const Descriptor& descriptor)
-    {
-        BOOST_CHECK(descriptor == m_Descriptor);
-    }
-
-    Descriptor m_Descriptor;
-};
-
-template<typename T>
-void CompareConstTensorData(const void* data1, const void* data2, unsigned int numElements)
-{
-    T typedData1 = static_cast<T>(data1);
-    T typedData2 = static_cast<T>(data2);
-    BOOST_CHECK(typedData1);
-    BOOST_CHECK(typedData2);
-
-    for (unsigned int i = 0; i < numElements; i++)
-    {
-        BOOST_TEST(typedData1[i] == typedData2[i]);
-    }
-}
-
-void CompareConstTensor(const armnn::ConstTensor& tensor1, const armnn::ConstTensor& tensor2)
-{
-    BOOST_TEST(tensor1.GetShape() == tensor2.GetShape());
-    BOOST_TEST(GetDataTypeName(tensor1.GetDataType()) == GetDataTypeName(tensor2.GetDataType()));
-
-    switch (tensor1.GetDataType())
-    {
-        case armnn::DataType::Float32:
-            CompareConstTensorData<const float*>(
-                tensor1.GetMemoryArea(), tensor2.GetMemoryArea(), tensor1.GetNumElements());
-            break;
-        case armnn::DataType::QAsymmU8:
-        case armnn::DataType::Boolean:
-            CompareConstTensorData<const uint8_t*>(
-                tensor1.GetMemoryArea(), tensor2.GetMemoryArea(), tensor1.GetNumElements());
-            break;
-        case armnn::DataType::QSymmS8:
-            CompareConstTensorData<const int8_t*>(
-                tensor1.GetMemoryArea(), tensor2.GetMemoryArea(), tensor1.GetNumElements());
-            break;
-        case armnn::DataType::Signed32:
-            CompareConstTensorData<const int32_t*>(
-                tensor1.GetMemoryArea(), tensor2.GetMemoryArea(), tensor1.GetNumElements());
-            break;
-        default:
-            // Note that Float16 is not yet implemented
-            BOOST_TEST_MESSAGE("Unexpected datatype");
-            BOOST_TEST(false);
-    }
-}
-
-armnn::INetworkPtr DeserializeNetwork(const std::string& serializerString)
-{
-    std::vector<std::uint8_t> const serializerVector{serializerString.begin(), serializerString.end()};
-    return IDeserializer::Create()->CreateNetworkFromBinary(serializerVector);
-}
+BOOST_AUTO_TEST_SUITE(SerializerTests)
 
-std::string SerializeNetwork(const armnn::INetwork& network)
+BOOST_AUTO_TEST_CASE(SerializeAbs)
 {
-    armnnSerializer::ISerializerPtr serializer = armnnSerializer::ISerializer::Create();
-
-    serializer->Serialize(network);
-
-    std::stringstream stream;
-    serializer->SaveSerializedToStream(stream);
+    const std::string layerName("abs");
+    const armnn::TensorInfo tensorInfo({1, 2, 3}, armnn::DataType::Float32);
 
-    std::string serializerString{stream.str()};
-    return serializerString;
-}
+    armnn::INetworkPtr network = armnn::INetwork::Create();
+    armnn::IConnectableLayer* const inputLayer = network->AddInputLayer(0);
 
-template<typename DataType>
-static std::vector<DataType> GenerateRandomData(size_t size)
-{
-    constexpr bool isIntegerType = std::is_integral<DataType>::value;
-    using Distribution =
-        typename std::conditional<isIntegerType,
-                                  std::uniform_int_distribution<DataType>,
-                                  std::uniform_real_distribution<DataType>>::type;
+    ARMNN_NO_DEPRECATE_WARN_BEGIN
+    armnn::IConnectableLayer* const absLayer = network->AddAbsLayer(layerName.c_str());
+    ARMNN_NO_DEPRECATE_WARN_END
+    armnn::IConnectableLayer* const outputLayer = network->AddOutputLayer(0);
 
-    static constexpr DataType lowerLimit = std::numeric_limits<DataType>::min();
-    static constexpr DataType upperLimit = std::numeric_limits<DataType>::max();
+    inputLayer->GetOutputSlot(0).Connect(absLayer->GetInputSlot(0));
+    absLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
 
-    static Distribution distribution(lowerLimit, upperLimit);
-    static std::default_random_engine generator;
+    inputLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo);
+    absLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo);
 
-    std::vector<DataType> randomData(size);
-    std::generate(randomData.begin(), randomData.end(), []() { return distribution(generator); });
+    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
+    BOOST_CHECK(deserializedNetwork);
 
-    return randomData;
+    LayerVerifierBase verifier(layerName, {tensorInfo}, {tensorInfo});
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
-} // anonymous namespace
-
-BOOST_AUTO_TEST_SUITE(SerializerTests)
-
 BOOST_AUTO_TEST_CASE(SerializeAddition)
 {
-    DECLARE_LAYER_VERIFIER_CLASS(Addition)
-
     const std::string layerName("addition");
     const armnn::TensorInfo tensorInfo({1, 2, 3}, armnn::DataType::Float32);
 
@@ -294,17 +68,16 @@ BOOST_AUTO_TEST_CASE(SerializeAddition)
     inputLayer1->GetOutputSlot(0).SetTensorInfo(tensorInfo);
     additionLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo);
 
-    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
+    std::string serializedNetwork = SerializeNetwork(*network);
+    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(serializedNetwork);
     BOOST_CHECK(deserializedNetwork);
 
-    AdditionLayerVerifier verifier(layerName, {tensorInfo, tensorInfo}, {tensorInfo});
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBase verifier(layerName, {tensorInfo, tensorInfo}, {tensorInfo});
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeArgMinMax)
 {
-    DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(ArgMinMax)
-
     const std::string layerName("argminmax");
     const armnn::TensorInfo inputInfo({1, 2, 3}, armnn::DataType::Float32);
     const armnn::TensorInfo outputInfo({1, 3}, armnn::DataType::Signed32);
@@ -327,54 +100,15 @@ BOOST_AUTO_TEST_CASE(SerializeArgMinMax)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    ArgMinMaxLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor);
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBaseWithDescriptor<armnn::ArgMinMaxDescriptor> verifier(layerName,
+                                                                         {inputInfo},
+                                                                         {outputInfo},
+                                                                         descriptor);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeBatchNormalization)
 {
-    using Descriptor = armnn::BatchNormalizationDescriptor;
-    class BatchNormalizationLayerVerifier : public LayerVerifierBaseWithDescriptor<Descriptor>
-    {
-    public:
-        BatchNormalizationLayerVerifier(const std::string& layerName,
-                                        const std::vector<armnn::TensorInfo>& inputInfos,
-                                        const std::vector<armnn::TensorInfo>& outputInfos,
-                                        const Descriptor& descriptor,
-                                        const armnn::ConstTensor& mean,
-                                        const armnn::ConstTensor& variance,
-                                        const armnn::ConstTensor& beta,
-                                        const armnn::ConstTensor& gamma)
-            : LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor)
-            , m_Mean(mean)
-            , m_Variance(variance)
-            , m_Beta(beta)
-            , m_Gamma(gamma) {}
-
-        void VisitBatchNormalizationLayer(const armnn::IConnectableLayer* layer,
-                                          const Descriptor& descriptor,
-                                          const armnn::ConstTensor& mean,
-                                          const armnn::ConstTensor& variance,
-                                          const armnn::ConstTensor& beta,
-                                          const armnn::ConstTensor& gamma,
-                                          const char* name) override
-        {
-            VerifyNameAndConnections(layer, name);
-            VerifyDescriptor(descriptor);
-
-            CompareConstTensor(mean, m_Mean);
-            CompareConstTensor(variance, m_Variance);
-            CompareConstTensor(beta, m_Beta);
-            CompareConstTensor(gamma, m_Gamma);
-        }
-
-    private:
-        armnn::ConstTensor m_Mean;
-        armnn::ConstTensor m_Variance;
-        armnn::ConstTensor m_Beta;
-        armnn::ConstTensor m_Gamma;
-    };
-
     const std::string layerName("batchNormalization");
     const armnn::TensorInfo inputInfo ({ 1, 3, 3, 1 }, armnn::DataType::Float32);
     const armnn::TensorInfo outputInfo({ 1, 3, 3, 1 }, armnn::DataType::Float32);
@@ -393,15 +127,21 @@ BOOST_AUTO_TEST_CASE(SerializeBatchNormalization)
     std::vector<float> betaData({1.0});
     std::vector<float> gammaData({0.0});
 
-    armnn::ConstTensor mean(meanInfo, meanData);
-    armnn::ConstTensor variance(varianceInfo, varianceData);
-    armnn::ConstTensor beta(betaInfo, betaData);
-    armnn::ConstTensor gamma(gammaInfo, gammaData);
+    std::vector<armnn::ConstTensor> constants;
+    constants.emplace_back(armnn::ConstTensor(meanInfo, meanData));
+    constants.emplace_back(armnn::ConstTensor(varianceInfo, varianceData));
+    constants.emplace_back(armnn::ConstTensor(betaInfo, betaData));
+    constants.emplace_back(armnn::ConstTensor(gammaInfo, gammaData));
 
     armnn::INetworkPtr network = armnn::INetwork::Create();
     armnn::IConnectableLayer* const inputLayer = network->AddInputLayer(0);
     armnn::IConnectableLayer* const batchNormalizationLayer =
-        network->AddBatchNormalizationLayer(descriptor, mean, variance, beta, gamma, layerName.c_str());
+        network->AddBatchNormalizationLayer(descriptor,
+                                            constants[0],
+                                            constants[1],
+                                            constants[2],
+                                            constants[3],
+                                            layerName.c_str());
     armnn::IConnectableLayer* const outputLayer = network->AddOutputLayer(0);
 
     inputLayer->GetOutputSlot(0).Connect(batchNormalizationLayer->GetInputSlot(0));
@@ -413,15 +153,13 @@ BOOST_AUTO_TEST_CASE(SerializeBatchNormalization)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    BatchNormalizationLayerVerifier verifier(
-        layerName, {inputInfo}, {outputInfo}, descriptor, mean, variance, beta, gamma);
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBaseWithDescriptorAndConstants<armnn::BatchNormalizationDescriptor> verifier(
+        layerName, {inputInfo}, {outputInfo}, descriptor, constants);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeBatchToSpaceNd)
 {
-    DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(BatchToSpaceNd)
-
     const std::string layerName("spaceToBatchNd");
     const armnn::TensorInfo inputInfo({4, 1, 2, 2}, armnn::DataType::Float32);
     const armnn::TensorInfo outputInfo({1, 1, 4, 4}, armnn::DataType::Float32);
@@ -445,14 +183,15 @@ BOOST_AUTO_TEST_CASE(SerializeBatchToSpaceNd)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    BatchToSpaceNdLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBaseWithDescriptor<armnn::BatchToSpaceNdDescriptor> verifier(layerName,
+                                                                              {inputInfo},
+                                                                              {outputInfo},
+                                                                              desc);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeComparison)
 {
-    DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Comparison)
-
     const std::string layerName("comparison");
 
     const armnn::TensorShape shape{2, 1, 2, 4};
@@ -479,8 +218,11 @@ BOOST_AUTO_TEST_CASE(SerializeComparison)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    ComparisonLayerVerifier verifier(layerName, { inputInfo, inputInfo }, { outputInfo }, descriptor);
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBaseWithDescriptor<armnn::ComparisonDescriptor> verifier(layerName,
+                                                                          { inputInfo, inputInfo },
+                                                                          { outputInfo },
+                                                                          descriptor);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeConstant)
@@ -491,22 +233,37 @@ BOOST_AUTO_TEST_CASE(SerializeConstant)
         ConstantLayerVerifier(const std::string& layerName,
                               const std::vector<armnn::TensorInfo>& inputInfos,
                               const std::vector<armnn::TensorInfo>& outputInfos,
-                              const armnn::ConstTensor& layerInput)
+                              const std::vector<armnn::ConstTensor>& constants)
             : LayerVerifierBase(layerName, inputInfos, outputInfos)
-            , m_LayerInput(layerInput) {}
+            , m_Constants(constants) {}
 
-        void VisitConstantLayer(const armnn::IConnectableLayer* layer,
-                                const armnn::ConstTensor& input,
-                                const char* name) override
+        void ExecuteStrategy(const armnn::IConnectableLayer* layer,
+                             const armnn::BaseDescriptor& descriptor,
+                             const std::vector<armnn::ConstTensor>& constants,
+                             const char* name,
+                             const armnn::LayerBindingId id = 0) override
         {
-            VerifyNameAndConnections(layer, name);
-            CompareConstTensor(input, m_LayerInput);
-        }
+            armnn::IgnoreUnused(descriptor, id);
+
+            switch (layer->GetType())
+            {
+                case armnn::LayerType::Input: break;
+                case armnn::LayerType::Output: break;
+                case armnn::LayerType::Addition: break;
+                default:
+                {
+                    this->VerifyNameAndConnections(layer, name);
 
-        void VisitAdditionLayer(const armnn::IConnectableLayer*, const char*) override {}
+                    for (std::size_t i = 0; i < constants.size(); i++)
+                    {
+                        CompareConstTensor(constants[i], m_Constants[i]);
+                    }
+                }
+            }
+        }
 
     private:
-        armnn::ConstTensor m_LayerInput;
+        const std::vector<armnn::ConstTensor> m_Constants;
     };
 
     const std::string layerName("constant");
@@ -532,53 +289,12 @@ BOOST_AUTO_TEST_CASE(SerializeConstant)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    ConstantLayerVerifier verifier(layerName, {}, {info}, constTensor);
-    deserializedNetwork->Accept(verifier);
+    ConstantLayerVerifier verifier(layerName, {}, {info}, {constTensor});
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeConvolution2d)
 {
-    using Descriptor = armnn::Convolution2dDescriptor;
-    class Convolution2dLayerVerifier : public LayerVerifierBaseWithDescriptor<Descriptor>
-    {
-    public:
-        Convolution2dLayerVerifier(const std::string& layerName,
-                                   const std::vector<armnn::TensorInfo>& inputInfos,
-                                   const std::vector<armnn::TensorInfo>& outputInfos,
-                                   const Descriptor& descriptor,
-                                   const armnn::ConstTensor& weights,
-                                   const armnn::Optional<armnn::ConstTensor>& biases)
-            : LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor)
-            , m_Weights(weights)
-            , m_Biases(biases) {}
-
-        void VisitConvolution2dLayer(const armnn::IConnectableLayer* layer,
-                                     const Descriptor& descriptor,
-                                     const armnn::ConstTensor& weights,
-                                     const armnn::Optional<armnn::ConstTensor>& biases,
-                                     const char* name) override
-        {
-            VerifyNameAndConnections(layer, name);
-            VerifyDescriptor(descriptor);
-
-            // check weights
-            CompareConstTensor(weights, m_Weights);
-
-            // check biases
-            BOOST_CHECK(biases.has_value() == descriptor.m_BiasEnabled);
-            BOOST_CHECK(biases.has_value() == m_Biases.has_value());
-
-            if (biases.has_value() && m_Biases.has_value())
-            {
-                CompareConstTensor(biases.value(), m_Biases.value());
-            }
-        }
-
-    private:
-        armnn::ConstTensor                  m_Weights;
-        armnn::Optional<armnn::ConstTensor> m_Biases;
-    };
-
     const std::string layerName("convolution2d");
     const armnn::TensorInfo inputInfo ({ 1, 5, 5, 1 }, armnn::DataType::Float32);
     const armnn::TensorInfo outputInfo({ 1, 3, 3, 1 }, armnn::DataType::Float32);
@@ -622,53 +338,14 @@ BOOST_AUTO_TEST_CASE(SerializeConvolution2d)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    Convolution2dLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor, weights, biases);
-    deserializedNetwork->Accept(verifier);
+    const std::vector<armnn::ConstTensor>& constants {weights, biases};
+    LayerVerifierBaseWithDescriptorAndConstants<armnn::Convolution2dDescriptor> verifier(
+            layerName, {inputInfo}, {outputInfo}, descriptor, constants);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeConvolution2dWithPerAxisParams)
 {
-    using Descriptor = armnn::Convolution2dDescriptor;
-    class Convolution2dLayerVerifier : public LayerVerifierBaseWithDescriptor<Descriptor>
-    {
-    public:
-        Convolution2dLayerVerifier(const std::string& layerName,
-                                   const std::vector<armnn::TensorInfo>& inputInfos,
-                                   const std::vector<armnn::TensorInfo>& outputInfos,
-                                   const Descriptor& descriptor,
-                                   const armnn::ConstTensor& weights,
-                                   const armnn::Optional<armnn::ConstTensor>& biases)
-            : LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor)
-            , m_Weights(weights)
-            , m_Biases(biases) {}
-
-        void VisitConvolution2dLayer(const armnn::IConnectableLayer* layer,
-                                     const Descriptor& descriptor,
-                                     const armnn::ConstTensor& weights,
-                                     const armnn::Optional<armnn::ConstTensor>& biases,
-                                     const char* name) override
-        {
-            VerifyNameAndConnections(layer, name);
-            VerifyDescriptor(descriptor);
-
-            // check weights
-            CompareConstTensor(weights, m_Weights);
-
-            // check biases
-            BOOST_CHECK(biases.has_value() == descriptor.m_BiasEnabled);
-            BOOST_CHECK(biases.has_value() == m_Biases.has_value());
-
-            if (biases.has_value() && m_Biases.has_value())
-            {
-                CompareConstTensor(biases.value(), m_Biases.value());
-            }
-        }
-
-    private:
-        armnn::ConstTensor                  m_Weights;
-        armnn::Optional<armnn::ConstTensor> m_Biases;
-    };
-
     using namespace armnn;
 
     const std::string layerName("convolution2dWithPerAxis");
@@ -716,14 +393,14 @@ BOOST_AUTO_TEST_CASE(SerializeConvolution2dWithPerAxisParams)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    Convolution2dLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor, weights, biases);
-    deserializedNetwork->Accept(verifier);
+    const std::vector<armnn::ConstTensor>& constants {weights, biases};
+    LayerVerifierBaseWithDescriptorAndConstants<Convolution2dDescriptor> verifier(
+            layerName, {inputInfo}, {outputInfo}, descriptor, constants);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeDepthToSpace)
 {
-    DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(DepthToSpace)
-
     const std::string layerName("depthToSpace");
 
     const armnn::TensorInfo inputInfo ({ 1,  8, 4, 12 }, armnn::DataType::Float32);
@@ -747,53 +424,12 @@ BOOST_AUTO_TEST_CASE(SerializeDepthToSpace)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    DepthToSpaceLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBaseWithDescriptor<armnn::DepthToSpaceDescriptor> verifier(layerName, {inputInfo}, {outputInfo}, desc);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeDepthwiseConvolution2d)
 {
-    using Descriptor = armnn::DepthwiseConvolution2dDescriptor;
-    class DepthwiseConvolution2dLayerVerifier : public LayerVerifierBaseWithDescriptor<Descriptor>
-    {
-    public:
-        DepthwiseConvolution2dLayerVerifier(const std::string& layerName,
-                                            const std::vector<armnn::TensorInfo>& inputInfos,
-                                            const std::vector<armnn::TensorInfo>& outputInfos,
-                                            const Descriptor& descriptor,
-                                            const armnn::ConstTensor& weights,
-                                            const armnn::Optional<armnn::ConstTensor>& biases) :
-            LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor),
-            m_Weights(weights),
-            m_Biases(biases) {}
-
-        void VisitDepthwiseConvolution2dLayer(const armnn::IConnectableLayer* layer,
-                                              const Descriptor& descriptor,
-                                              const armnn::ConstTensor& weights,
-                                              const armnn::Optional<armnn::ConstTensor>& biases,
-                                              const char* name) override
-        {
-            VerifyNameAndConnections(layer, name);
-            VerifyDescriptor(descriptor);
-
-            // check weights
-            CompareConstTensor(weights, m_Weights);
-
-            // check biases
-            BOOST_CHECK(biases.has_value() == descriptor.m_BiasEnabled);
-            BOOST_CHECK(biases.has_value() == m_Biases.has_value());
-
-            if (biases.has_value() && m_Biases.has_value())
-            {
-                CompareConstTensor(biases.value(), m_Biases.value());
-            }
-        }
-
-    private:
-        armnn::ConstTensor                      m_Weights;
-        armnn::Optional<armnn::ConstTensor>     m_Biases;
-    };
-
     const std::string layerName("depwiseConvolution2d");
     const armnn::TensorInfo inputInfo ({ 1, 5, 5, 3 }, armnn::DataType::Float32);
     const armnn::TensorInfo outputInfo({ 1, 3, 3, 3 }, armnn::DataType::Float32);
@@ -837,53 +473,14 @@ BOOST_AUTO_TEST_CASE(SerializeDepthwiseConvolution2d)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    DepthwiseConvolution2dLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor, weights, biases);
-    deserializedNetwork->Accept(verifier);
+    const std::vector<armnn::ConstTensor>& constants {weights, biases};
+    LayerVerifierBaseWithDescriptorAndConstants<armnn::DepthwiseConvolution2dDescriptor> verifier(
+            layerName, {inputInfo}, {outputInfo}, descriptor, constants);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeDepthwiseConvolution2dWithPerAxisParams)
 {
-    using Descriptor = armnn::DepthwiseConvolution2dDescriptor;
-    class DepthwiseConvolution2dLayerVerifier : public LayerVerifierBaseWithDescriptor<Descriptor>
-    {
-    public:
-        DepthwiseConvolution2dLayerVerifier(const std::string& layerName,
-                                            const std::vector<armnn::TensorInfo>& inputInfos,
-                                            const std::vector<armnn::TensorInfo>& outputInfos,
-                                            const Descriptor& descriptor,
-                                            const armnn::ConstTensor& weights,
-                                            const armnn::Optional<armnn::ConstTensor>& biases) :
-            LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor),
-            m_Weights(weights),
-            m_Biases(biases) {}
-
-        void VisitDepthwiseConvolution2dLayer(const armnn::IConnectableLayer* layer,
-                                              const Descriptor& descriptor,
-                                              const armnn::ConstTensor& weights,
-                                              const armnn::Optional<armnn::ConstTensor>& biases,
-                                              const char* name) override
-        {
-            VerifyNameAndConnections(layer, name);
-            VerifyDescriptor(descriptor);
-
-            // check weights
-            CompareConstTensor(weights, m_Weights);
-
-            // check biases
-            BOOST_CHECK(biases.has_value() == descriptor.m_BiasEnabled);
-            BOOST_CHECK(biases.has_value() == m_Biases.has_value());
-
-            if (biases.has_value() && m_Biases.has_value())
-            {
-                CompareConstTensor(biases.value(), m_Biases.value());
-            }
-        }
-
-    private:
-        armnn::ConstTensor                      m_Weights;
-        armnn::Optional<armnn::ConstTensor>     m_Biases;
-    };
-
     using namespace armnn;
 
     const std::string layerName("depwiseConvolution2dWithPerAxis");
@@ -933,14 +530,14 @@ BOOST_AUTO_TEST_CASE(SerializeDepthwiseConvolution2dWithPerAxisParams)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    DepthwiseConvolution2dLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor, weights, biases);
-    deserializedNetwork->Accept(verifier);
+    const std::vector<armnn::ConstTensor>& constants {weights, biases};
+    LayerVerifierBaseWithDescriptorAndConstants<armnn::DepthwiseConvolution2dDescriptor> verifier(
+            layerName, {inputInfo}, {outputInfo}, descriptor, constants);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeDequantize)
 {
-    DECLARE_LAYER_VERIFIER_CLASS(Dequantize)
-
     const std::string layerName("dequantize");
     const armnn::TensorInfo inputInfo({ 1, 5, 2, 3 }, armnn::DataType::QAsymmU8, 0.5f, 1);
     const armnn::TensorInfo outputInfo({ 1, 5, 2, 3 }, armnn::DataType::Float32);
@@ -959,39 +556,12 @@ BOOST_AUTO_TEST_CASE(SerializeDequantize)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    DequantizeLayerVerifier verifier(layerName, {inputInfo}, {outputInfo});
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBase verifier(layerName, {inputInfo}, {outputInfo});
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeDeserializeDetectionPostProcess)
 {
-    using Descriptor = armnn::DetectionPostProcessDescriptor;
-    class DetectionPostProcessLayerVerifier : public LayerVerifierBaseWithDescriptor<Descriptor>
-    {
-    public:
-        DetectionPostProcessLayerVerifier(const std::string& layerName,
-                                          const std::vector<armnn::TensorInfo>& inputInfos,
-                                          const std::vector<armnn::TensorInfo>& outputInfos,
-                                          const Descriptor& descriptor,
-                                          const armnn::ConstTensor& anchors)
-            : LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor)
-            , m_Anchors(anchors) {}
-
-        void VisitDetectionPostProcessLayer(const armnn::IConnectableLayer* layer,
-                                            const Descriptor& descriptor,
-                                            const armnn::ConstTensor& anchors,
-                                            const char* name) override
-        {
-            VerifyNameAndConnections(layer, name);
-            VerifyDescriptor(descriptor);
-
-            CompareConstTensor(anchors, m_Anchors);
-        }
-
-    private:
-        armnn::ConstTensor m_Anchors;
-    };
-
     const std::string layerName("detectionPostProcess");
 
     const std::vector<armnn::TensorInfo> inputInfos({
@@ -1051,14 +621,14 @@ BOOST_AUTO_TEST_CASE(SerializeDeserializeDetectionPostProcess)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    DetectionPostProcessLayerVerifier verifier(layerName, inputInfos, outputInfos, descriptor, anchors);
-    deserializedNetwork->Accept(verifier);
+    const std::vector<armnn::ConstTensor>& constants {anchors};
+    LayerVerifierBaseWithDescriptorAndConstants<armnn::DetectionPostProcessDescriptor> verifier(
+            layerName, inputInfos, outputInfos, descriptor, constants);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeDivision)
 {
-    DECLARE_LAYER_VERIFIER_CLASS(Division)
-
     const std::string layerName("division");
     const armnn::TensorInfo info({ 1, 5, 2, 3 }, armnn::DataType::Float32);
 
@@ -1079,131 +649,41 @@ BOOST_AUTO_TEST_CASE(SerializeDivision)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    DivisionLayerVerifier verifier(layerName, {info, info}, {info});
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBase verifier(layerName, {info, info}, {info});
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
-class EqualLayerVerifier : public LayerVerifierBase
-{
-public:
-    EqualLayerVerifier(const std::string& layerName,
-                       const std::vector<armnn::TensorInfo>& inputInfos,
-                       const std::vector<armnn::TensorInfo>& outputInfos)
-        : LayerVerifierBase(layerName, inputInfos, outputInfos) {}
-
-    void VisitComparisonLayer(const armnn::IConnectableLayer* layer,
-                              const armnn::ComparisonDescriptor& descriptor,
-                              const char* name) override
-    {
-        VerifyNameAndConnections(layer, name);
-        BOOST_CHECK(descriptor.m_Operation == armnn::ComparisonOperation::Equal);
-    }
-
-    void VisitEqualLayer(const armnn::IConnectableLayer*, const char*) override
-    {
-        throw armnn::Exception("EqualLayer should have translated to ComparisonLayer");
-    }
-};
-
-// NOTE: Until the deprecated AddEqualLayer disappears this test checks that calling
-//       AddEqualLayer places a ComparisonLayer into the serialized format and that
-//       when this deserialises we have a ComparisonLayer
-BOOST_AUTO_TEST_CASE(SerializeEqual)
+BOOST_AUTO_TEST_CASE(SerializeDeserializeEqual)
 {
-    const std::string layerName("equal");
-
-    const armnn::TensorShape shape{2, 1, 2, 4};
-
-    const armnn::TensorInfo inputInfo  = armnn::TensorInfo(shape, armnn::DataType::Float32);
-    const armnn::TensorInfo outputInfo = armnn::TensorInfo(shape, armnn::DataType::Boolean);
+    const std::string layerName("EqualLayer");
+    const armnn::TensorInfo inputTensorInfo1 = armnn::TensorInfo({2, 1, 2, 4}, armnn::DataType::Float32);
+    const armnn::TensorInfo inputTensorInfo2 = armnn::TensorInfo({2, 1, 2, 4}, armnn::DataType::Float32);
+    const armnn::TensorInfo outputTensorInfo = armnn::TensorInfo({2, 1, 2, 4}, armnn::DataType::Boolean);
 
     armnn::INetworkPtr network = armnn::INetwork::Create();
-    armnn::IConnectableLayer* const inputLayer0 = network->AddInputLayer(0);
-    armnn::IConnectableLayer* const inputLayer1 = network->AddInputLayer(1);
+    armnn::IConnectableLayer* const inputLayer1 = network->AddInputLayer(0);
+    armnn::IConnectableLayer* const inputLayer2 = network->AddInputLayer(1);
     ARMNN_NO_DEPRECATE_WARN_BEGIN
     armnn::IConnectableLayer* const equalLayer = network->AddEqualLayer(layerName.c_str());
     ARMNN_NO_DEPRECATE_WARN_END
     armnn::IConnectableLayer* const outputLayer = network->AddOutputLayer(0);
 
-    inputLayer0->GetOutputSlot(0).Connect(equalLayer->GetInputSlot(0));
-    inputLayer1->GetOutputSlot(0).Connect(equalLayer->GetInputSlot(1));
+    inputLayer1->GetOutputSlot(0).Connect(equalLayer->GetInputSlot(0));
+    inputLayer1->GetOutputSlot(0).SetTensorInfo(inputTensorInfo1);
+    inputLayer2->GetOutputSlot(0).Connect(equalLayer->GetInputSlot(1));
+    inputLayer2->GetOutputSlot(0).SetTensorInfo(inputTensorInfo2);
     equalLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
-
-    inputLayer0->GetOutputSlot(0).SetTensorInfo(inputInfo);
-    inputLayer1->GetOutputSlot(0).SetTensorInfo(inputInfo);
-    equalLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
+    equalLayer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
 
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    EqualLayerVerifier verifier(layerName, { inputInfo, inputInfo }, { outputInfo });
-    deserializedNetwork->Accept(verifier);
-}
-
-BOOST_AUTO_TEST_CASE(EnsureEqualBackwardCompatibility)
-{
-    // The hex data below is a flat buffer containing a simple network with two inputs,
-    // an EqualLayer (now deprecated) and an output
-    //
-    // This test verifies that we can still deserialize this old-style model by replacing
-    // the EqualLayer with an equivalent ComparisonLayer
-    const std::vector<uint8_t> equalModel =
-    {
-        0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,
-        0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x1C, 0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
-        0xCC, 0x01, 0x00, 0x00, 0x20, 0x01, 0x00, 0x00, 0x70, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x02, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
-        0x60, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B, 0x04, 0x00, 0x00, 0x00, 0xFE, 0xFE, 0xFF, 0xFF, 0x04, 0x00,
-        0x00, 0x00, 0x06, 0xFF, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0xEA, 0xFE, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00,
-        0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x64, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB4, 0xFE, 0xFF, 0xFF, 0x00, 0x00,
-        0x00, 0x13, 0x04, 0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x36, 0xFF, 0xFF, 0xFF,
-        0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x1C, 0x00,
-        0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x65, 0x71, 0x75, 0x61, 0x6C, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
-        0x5C, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x34, 0xFF,
-        0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x92, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x04, 0x08, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00,
-        0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x08, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00,
-        0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x00, 0x00,
-        0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0E, 0x00,
-        0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x06, 0x00, 0x08, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0E, 0x00,
-        0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x0E, 0x00, 0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x14, 0x00, 0x0E, 0x00, 0x00, 0x00,
-        0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
-        0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00,
-        0x00, 0x00, 0x66, 0xFF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x04, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x04, 0x00,
-        0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09,
-        0x04, 0x00, 0x00, 0x00, 0xF6, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x0A, 0x00,
-        0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x00, 0x14, 0x00, 0x00, 0x00,
-        0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x0E, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00,
-        0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0A, 0x00, 0x00, 0x00,
-        0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x08, 0x00,
-        0x07, 0x00, 0x0C, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
-        0x04, 0x00, 0x00, 0x00
-    };
-
-    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(std::string(equalModel.begin(), equalModel.end()));
-    BOOST_CHECK(deserializedNetwork);
-
-    const armnn::TensorShape shape{ 2, 1, 2, 4 };
-
-    const armnn::TensorInfo inputInfo  = armnn::TensorInfo(shape, armnn::DataType::Float32);
-    const armnn::TensorInfo outputInfo = armnn::TensorInfo(shape, armnn::DataType::Boolean);
-
-    EqualLayerVerifier verifier("equal", { inputInfo, inputInfo }, { outputInfo });
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBase verifier(layerName, {inputTensorInfo1, inputTensorInfo2}, {outputTensorInfo});
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeFill)
 {
-    DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Fill)
-
     const std::string layerName("fill");
     const armnn::TensorInfo inputInfo({4}, armnn::DataType::Signed32);
     const armnn::TensorInfo outputInfo({1, 3, 3, 1}, armnn::DataType::Float32);
@@ -1224,15 +704,13 @@ BOOST_AUTO_TEST_CASE(SerializeFill)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    FillLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor);
+    LayerVerifierBaseWithDescriptor<armnn::FillDescriptor> verifier(layerName, {inputInfo}, {outputInfo}, descriptor);
 
-    deserializedNetwork->Accept(verifier);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeFloor)
 {
-    DECLARE_LAYER_VERIFIER_CLASS(Floor)
-
     const std::string layerName("floor");
     const armnn::TensorInfo info({4,4}, armnn::DataType::Float32);
 
@@ -1250,51 +728,12 @@ BOOST_AUTO_TEST_CASE(SerializeFloor)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    FloorLayerVerifier verifier(layerName, {info}, {info});
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBase verifier(layerName, {info}, {info});
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeFullyConnected)
 {
-    using Descriptor = armnn::FullyConnectedDescriptor;
-    class FullyConnectedLayerVerifier : public LayerVerifierBaseWithDescriptor<Descriptor>
-    {
-    public:
-        FullyConnectedLayerVerifier(const std::string& layerName,
-                                    const std::vector<armnn::TensorInfo>& inputInfos,
-                                    const std::vector<armnn::TensorInfo>& outputInfos,
-                                    const Descriptor& descriptor,
-                                    const armnn::ConstTensor& weight,
-                                    const armnn::Optional<armnn::ConstTensor>& bias)
-            : LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor)
-            , m_Weight(weight)
-            , m_Bias(bias) {}
-
-        void VisitFullyConnectedLayer(const armnn::IConnectableLayer* layer,
-                                      const Descriptor& descriptor,
-                                      const armnn::ConstTensor& weight,
-                                      const armnn::Optional<armnn::ConstTensor>& bias,
-                                      const char* name) override
-        {
-            VerifyNameAndConnections(layer, name);
-            VerifyDescriptor(descriptor);
-
-            CompareConstTensor(weight, m_Weight);
-
-            BOOST_TEST(bias.has_value() == descriptor.m_BiasEnabled);
-            BOOST_TEST(bias.has_value() == m_Bias.has_value());
-
-            if (bias.has_value() && m_Bias.has_value())
-            {
-                CompareConstTensor(bias.value(), m_Bias.value());
-            }
-        }
-
-    private:
-        armnn::ConstTensor m_Weight;
-        armnn::Optional<armnn::ConstTensor> m_Bias;
-    };
-
     const std::string layerName("fullyConnected");
     const armnn::TensorInfo inputInfo ({ 2, 5, 1, 1 }, armnn::DataType::Float32);
     const armnn::TensorInfo outputInfo({ 2, 3 }, armnn::DataType::Float32);
@@ -1328,8 +767,10 @@ BOOST_AUTO_TEST_CASE(SerializeFullyConnected)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    FullyConnectedLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor, weights, biases);
-    deserializedNetwork->Accept(verifier);
+    const std::vector<armnn::ConstTensor> constants {weights, biases};
+    LayerVerifierBaseWithDescriptorAndConstants<armnn::FullyConnectedDescriptor> verifier(
+            layerName, {inputInfo}, {outputInfo}, descriptor, constants);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeGather)
@@ -1344,17 +785,26 @@ BOOST_AUTO_TEST_CASE(SerializeGather)
                             const GatherDescriptor& descriptor)
             : LayerVerifierBaseWithDescriptor<GatherDescriptor>(layerName, inputInfos, outputInfos, descriptor) {}
 
-        void VisitGatherLayer(const armnn::IConnectableLayer* layer,
-                              const GatherDescriptor& descriptor,
-                              const char *name) override
+        void ExecuteStrategy(const armnn::IConnectableLayer* layer,
+                             const armnn::BaseDescriptor& descriptor,
+                             const std::vector<armnn::ConstTensor>& constants,
+                             const char* name,
+                             const armnn::LayerBindingId id = 0) override
         {
-            VerifyNameAndConnections(layer, name);
-            BOOST_CHECK(descriptor.m_Axis == m_Descriptor.m_Axis);
+            armnn::IgnoreUnused(constants, id);
+            switch (layer->GetType())
+            {
+                case armnn::LayerType::Input: break;
+                case armnn::LayerType::Output: break;
+                case armnn::LayerType::Constant: break;
+                default:
+                {
+                    VerifyNameAndConnections(layer, name);
+                    const GatherDescriptor& layerDescriptor = static_cast<const GatherDescriptor&>(descriptor);
+                    BOOST_CHECK(layerDescriptor.m_Axis == m_Descriptor.m_Axis);
+                }
+            }
         }
-
-        void VisitConstantLayer(const armnn::IConnectableLayer*,
-                                const armnn::ConstTensor&,
-                                const char*) override {}
     };
 
     const std::string layerName("gather");
@@ -1390,35 +840,14 @@ BOOST_AUTO_TEST_CASE(SerializeGather)
     BOOST_CHECK(deserializedNetwork);
 
     GatherLayerVerifier verifier(layerName, {paramsInfo, indicesInfo}, {outputInfo}, descriptor);
-    deserializedNetwork->Accept(verifier);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
-class GreaterLayerVerifier : public LayerVerifierBase
-{
-public:
-    GreaterLayerVerifier(const std::string& layerName,
-                         const std::vector<armnn::TensorInfo>& inputInfos,
-                         const std::vector<armnn::TensorInfo>& outputInfos)
-        : LayerVerifierBase(layerName, inputInfos, outputInfos) {}
-
-    void VisitComparisonLayer(const armnn::IConnectableLayer* layer,
-                              const armnn::ComparisonDescriptor& descriptor,
-                              const char* name) override
-    {
-        VerifyNameAndConnections(layer, name);
-        BOOST_CHECK(descriptor.m_Operation == armnn::ComparisonOperation::Greater);
-    }
-
-    void VisitGreaterLayer(const armnn::IConnectableLayer*, const char*) override
-    {
-        throw armnn::Exception("GreaterLayer should have translated to ComparisonLayer");
-    }
-};
 
 // NOTE: Until the deprecated AddGreaterLayer disappears this test checks that calling
 //       AddGreaterLayer places a ComparisonLayer into the serialized format and that
 //       when this deserialises we have a ComparisonLayer
-BOOST_AUTO_TEST_CASE(SerializeGreater)
+BOOST_AUTO_TEST_CASE(SerializeGreaterDeprecated)
 {
     const std::string layerName("greater");
 
@@ -1446,74 +875,13 @@ BOOST_AUTO_TEST_CASE(SerializeGreater)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    GreaterLayerVerifier verifier(layerName, { inputInfo, inputInfo }, { outputInfo });
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBase verifier(layerName, { inputInfo, inputInfo }, { outputInfo });
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
-BOOST_AUTO_TEST_CASE(EnsureGreaterBackwardCompatibility)
-{
-    // The hex data below is a flat buffer containing a simple network with two inputs,
-    // an GreaterLayer (now deprecated) and an output
-    //
-    // This test verifies that we can still deserialize this old-style model by replacing
-    // the GreaterLayer with an equivalent ComparisonLayer
-    const std::vector<uint8_t> greaterModel =
-    {
-        0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,
-        0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x1C, 0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
-        0xCC, 0x01, 0x00, 0x00, 0x20, 0x01, 0x00, 0x00, 0x70, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x02, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
-        0x60, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B, 0x04, 0x00, 0x00, 0x00, 0xFE, 0xFE, 0xFF, 0xFF, 0x04, 0x00,
-        0x00, 0x00, 0x06, 0xFF, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0xEA, 0xFE, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00,
-        0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x64, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB4, 0xFE, 0xFF, 0xFF, 0x00, 0x00,
-        0x00, 0x19, 0x04, 0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x36, 0xFF, 0xFF, 0xFF,
-        0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x1C, 0x00,
-        0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x67, 0x72, 0x65, 0x61, 0x74, 0x65, 0x72, 0x00, 0x02, 0x00, 0x00, 0x00,
-        0x5C, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x34, 0xFF,
-        0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x92, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x04, 0x08, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00,
-        0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x08, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00,
-        0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x00, 0x00,
-        0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0E, 0x00,
-        0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x06, 0x00, 0x08, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0E, 0x00,
-        0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x0E, 0x00, 0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x14, 0x00, 0x0E, 0x00, 0x00, 0x00,
-        0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
-        0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00,
-        0x00, 0x00, 0x66, 0xFF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00,
-        0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09,
-        0x04, 0x00, 0x00, 0x00, 0xF6, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x0A, 0x00,
-        0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x00, 0x14, 0x00, 0x00, 0x00,
-        0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x0E, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00,
-        0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0A, 0x00, 0x00, 0x00,
-        0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x08, 0x00,
-        0x07, 0x00, 0x0C, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
-        0x02, 0x00, 0x00, 0x00
-    };
-
-    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(std::string(greaterModel.begin(), greaterModel.end()));
-    BOOST_CHECK(deserializedNetwork);
-
-    const armnn::TensorShape shape{ 1, 2, 2, 2 };
-
-    const armnn::TensorInfo inputInfo  = armnn::TensorInfo(shape, armnn::DataType::Float32);
-    const armnn::TensorInfo outputInfo = armnn::TensorInfo(shape, armnn::DataType::Boolean);
-
-    GreaterLayerVerifier verifier("greater", { inputInfo, inputInfo }, { outputInfo });
-    deserializedNetwork->Accept(verifier);
-}
 
 BOOST_AUTO_TEST_CASE(SerializeInstanceNormalization)
 {
-    DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(InstanceNormalization)
-
     const std::string layerName("instanceNormalization");
     const armnn::TensorInfo info({ 1, 2, 1, 5 }, armnn::DataType::Float32);
 
@@ -1538,12 +906,11 @@ BOOST_AUTO_TEST_CASE(SerializeInstanceNormalization)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    InstanceNormalizationLayerVerifier verifier(layerName, {info}, {info}, descriptor);
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBaseWithDescriptor<armnn::InstanceNormalizationDescriptor> verifier(
+            layerName, {info}, {info}, descriptor);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
-DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(L2Normalization)
-
 BOOST_AUTO_TEST_CASE(SerializeL2Normalization)
 {
     const std::string l2NormLayerName("l2Normalization");
@@ -1567,8 +934,9 @@ BOOST_AUTO_TEST_CASE(SerializeL2Normalization)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    L2NormalizationLayerVerifier verifier(l2NormLayerName, {info}, {info}, desc);
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBaseWithDescriptor<armnn::L2NormalizationDescriptor> verifier(
+            l2NormLayerName, {info}, {info}, desc);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(EnsureL2NormalizationBackwardCompatibility)
@@ -1623,14 +991,13 @@ BOOST_AUTO_TEST_CASE(EnsureL2NormalizationBackwardCompatibility)
     // Since this variable does not exist in the l2NormalizationModel dump, the default value will be loaded
     desc.m_Eps = 1e-12f;
 
-    L2NormalizationLayerVerifier verifier(layerName, {inputInfo}, {inputInfo}, desc);
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBaseWithDescriptor<armnn::L2NormalizationDescriptor> verifier(
+            layerName, {inputInfo}, {inputInfo}, desc);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeLogicalBinary)
 {
-    DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(LogicalBinary)
-
     const std::string layerName("logicalBinaryAnd");
 
     const armnn::TensorShape shape{2, 1, 2, 2};
@@ -1657,14 +1024,13 @@ BOOST_AUTO_TEST_CASE(SerializeLogicalBinary)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    LogicalBinaryLayerVerifier verifier(layerName, { inputInfo, inputInfo }, { outputInfo }, descriptor);
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBaseWithDescriptor<armnn::LogicalBinaryDescriptor> verifier(
+            layerName, { inputInfo, inputInfo }, { outputInfo }, descriptor);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeLogicalUnary)
 {
-    DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(ElementwiseUnary)
-
     const std::string layerName("elementwiseUnaryLogicalNot");
 
     const armnn::TensorShape shape{2, 1, 2, 2};
@@ -1690,15 +1056,14 @@ BOOST_AUTO_TEST_CASE(SerializeLogicalUnary)
 
     BOOST_CHECK(deserializedNetwork);
 
-    ElementwiseUnaryLayerVerifier verifier(layerName, { inputInfo }, { outputInfo }, descriptor);
+    LayerVerifierBaseWithDescriptor<armnn::ElementwiseUnaryDescriptor> verifier(
+            layerName, { inputInfo }, { outputInfo }, descriptor);
 
-    deserializedNetwork->Accept(verifier);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeLogSoftmax)
 {
-    DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(LogSoftmax)
-
     const std::string layerName("log_softmax");
     const armnn::TensorInfo info({1, 10}, armnn::DataType::Float32);
 
@@ -1720,14 +1085,12 @@ BOOST_AUTO_TEST_CASE(SerializeLogSoftmax)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    LogSoftmaxLayerVerifier verifier(layerName, {info}, {info}, descriptor);
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBaseWithDescriptor<armnn::LogSoftmaxDescriptor> verifier(layerName, {info}, {info}, descriptor);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeMaximum)
 {
-    DECLARE_LAYER_VERIFIER_CLASS(Maximum)
-
     const std::string layerName("maximum");
     const armnn::TensorInfo info({ 1, 2, 2, 3 }, armnn::DataType::Float32);
 
@@ -1748,14 +1111,12 @@ BOOST_AUTO_TEST_CASE(SerializeMaximum)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    MaximumLayerVerifier verifier(layerName, {info, info}, {info});
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBase verifier(layerName, {info, info}, {info});
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeMean)
 {
-    DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Mean)
-
     const std::string layerName("mean");
     const armnn::TensorInfo inputInfo({1, 1, 3, 2}, armnn::DataType::Float32);
     const armnn::TensorInfo outputInfo({1, 1, 1, 2}, armnn::DataType::Float32);
@@ -1778,14 +1139,12 @@ BOOST_AUTO_TEST_CASE(SerializeMean)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    MeanLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor);
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBaseWithDescriptor<armnn::MeanDescriptor> verifier(layerName, {inputInfo}, {outputInfo}, descriptor);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeMerge)
 {
-    DECLARE_LAYER_VERIFIER_CLASS(Merge)
-
     const std::string layerName("merge");
     const armnn::TensorInfo info({ 1, 2, 2, 3 }, armnn::DataType::Float32);
 
@@ -1806,8 +1165,8 @@ BOOST_AUTO_TEST_CASE(SerializeMerge)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    MergeLayerVerifier verifier(layerName, {info, info}, {info});
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBase verifier(layerName, {info, info}, {info});
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 class MergerLayerVerifier : public LayerVerifierBaseWithDescriptor<armnn::OriginsDescriptor>
@@ -1819,19 +1178,35 @@ public:
                         const armnn::OriginsDescriptor& descriptor)
         : LayerVerifierBaseWithDescriptor<armnn::OriginsDescriptor>(layerName, inputInfos, outputInfos, descriptor) {}
 
-    void VisitMergerLayer(const armnn::IConnectableLayer*,
-                          const armnn::OriginsDescriptor&,
-                          const char*) override
-    {
-        throw armnn::Exception("MergerLayer should have translated to ConcatLayer");
-    }
-
-    void VisitConcatLayer(const armnn::IConnectableLayer* layer,
-                          const armnn::OriginsDescriptor& descriptor,
-                          const char* name) override
+    void ExecuteStrategy(const armnn::IConnectableLayer* layer,
+                         const armnn::BaseDescriptor& descriptor,
+                         const std::vector<armnn::ConstTensor>& constants,
+                         const char* name,
+                         const armnn::LayerBindingId id = 0) override
     {
-        VerifyNameAndConnections(layer, name);
-        VerifyDescriptor(descriptor);
+        armnn::IgnoreUnused(descriptor, constants, id);
+        switch (layer->GetType())
+        {
+            case armnn::LayerType::Input: break;
+            case armnn::LayerType::Output: break;
+            case armnn::LayerType::Merge:
+            {
+                throw armnn::Exception("MergerLayer should have translated to ConcatLayer");
+                break;
+            }
+            case armnn::LayerType::Concat:
+            {
+                VerifyNameAndConnections(layer, name);
+                const armnn::MergerDescriptor& layerDescriptor =
+                        static_cast<const armnn::MergerDescriptor&>(descriptor);
+                VerifyDescriptor(layerDescriptor);
+                break;
+            }
+            default:
+            {
+                throw armnn::Exception("Unexpected layer type in Merge test model");
+            }
+        }
     }
 };
 
@@ -1870,7 +1245,7 @@ BOOST_AUTO_TEST_CASE(SerializeMerger)
     BOOST_CHECK(deserializedNetwork);
 
     MergerLayerVerifier verifier(layerName, {inputInfo, inputInfo}, {outputInfo}, descriptor);
-    deserializedNetwork->Accept(verifier);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(EnsureMergerLayerBackwardCompatibility)
@@ -1939,7 +1314,7 @@ BOOST_AUTO_TEST_CASE(EnsureMergerLayerBackwardCompatibility)
             armnn::CreateDescriptorForConcatenation(shapes.begin(), shapes.end(), 0);
 
     MergerLayerVerifier verifier("merger", { inputInfo, inputInfo }, { outputInfo }, descriptor);
-    deserializedNetwork->Accept(verifier);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeConcat)
@@ -1974,13 +1349,11 @@ BOOST_AUTO_TEST_CASE(SerializeConcat)
     // NOTE: using the MergerLayerVerifier to ensure that it is a concat layer and not a
     //       merger layer that gets placed into the graph.
     MergerLayerVerifier verifier(layerName, {inputInfo, inputInfo}, {outputInfo}, descriptor);
-    deserializedNetwork->Accept(verifier);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeMinimum)
 {
-    DECLARE_LAYER_VERIFIER_CLASS(Minimum)
-
     const std::string layerName("minimum");
     const armnn::TensorInfo info({ 1, 2, 2, 3 }, armnn::DataType::Float32);
 
@@ -2001,14 +1374,12 @@ BOOST_AUTO_TEST_CASE(SerializeMinimum)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    MinimumLayerVerifier verifier(layerName, {info, info}, {info});
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBase verifier(layerName, {info, info}, {info});
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeMultiplication)
 {
-    DECLARE_LAYER_VERIFIER_CLASS(Multiplication)
-
     const std::string layerName("multiplication");
     const armnn::TensorInfo info({ 1, 5, 2, 3 }, armnn::DataType::Float32);
 
@@ -2029,14 +1400,12 @@ BOOST_AUTO_TEST_CASE(SerializeMultiplication)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    MultiplicationLayerVerifier verifier(layerName, {info, info}, {info});
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBase verifier(layerName, {info, info}, {info});
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializePrelu)
 {
-    DECLARE_LAYER_VERIFIER_CLASS(Prelu)
-
     const std::string layerName("prelu");
 
     armnn::TensorInfo inputTensorInfo ({ 4, 1, 2 }, armnn::DataType::Float32);
@@ -2060,14 +1429,12 @@ BOOST_AUTO_TEST_CASE(SerializePrelu)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    PreluLayerVerifier verifier(layerName, {inputTensorInfo, alphaTensorInfo}, {outputTensorInfo});
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBase verifier(layerName, {inputTensorInfo, alphaTensorInfo}, {outputTensorInfo});
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeNormalization)
 {
-    DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Normalization)
-
     const std::string layerName("normalization");
     const armnn::TensorInfo info({2, 1, 2, 2}, armnn::DataType::Float32);
 
@@ -2092,12 +1459,10 @@ BOOST_AUTO_TEST_CASE(SerializeNormalization)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    NormalizationLayerVerifier verifier(layerName, {info}, {info}, desc);
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBaseWithDescriptor<armnn::NormalizationDescriptor> verifier(layerName, {info}, {info}, desc);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
-DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Pad)
-
 BOOST_AUTO_TEST_CASE(SerializePad)
 {
     const std::string layerName("pad");
@@ -2120,8 +1485,11 @@ BOOST_AUTO_TEST_CASE(SerializePad)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    PadLayerVerifier verifier(layerName, {inputTensorInfo}, {outputTensorInfo}, desc);
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBaseWithDescriptor<armnn::PadDescriptor> verifier(layerName,
+                                                                   {inputTensorInfo},
+                                                                   {outputTensorInfo},
+                                                                   desc);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(EnsurePadBackwardCompatibility)
@@ -2174,14 +1542,12 @@ BOOST_AUTO_TEST_CASE(EnsurePadBackwardCompatibility)
 
     armnn::PadDescriptor descriptor({{ 0, 0 }, { 1, 0 }, { 1, 1 }, { 1, 2 }});
 
-    PadLayerVerifier verifier("pad", { inputInfo }, { outputInfo }, descriptor);
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBaseWithDescriptor<armnn::PadDescriptor> verifier("pad", { inputInfo }, { outputInfo }, descriptor);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializePermute)
 {
-    DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Permute)
-
     const std::string layerName("permute");
     const armnn::TensorInfo inputTensorInfo({4, 3, 2, 1}, armnn::DataType::Float32);
     const armnn::TensorInfo outputTensorInfo({1, 2, 3, 4}, armnn::DataType::Float32);
@@ -2202,14 +1568,13 @@ BOOST_AUTO_TEST_CASE(SerializePermute)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    PermuteLayerVerifier verifier(layerName, {inputTensorInfo}, {outputTensorInfo}, descriptor);
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBaseWithDescriptor<armnn::PermuteDescriptor> verifier(
+            layerName, {inputTensorInfo}, {outputTensorInfo}, descriptor);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializePooling2d)
 {
-    DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Pooling2d)
-
     const std::string layerName("pooling2d");
     const armnn::TensorInfo inputInfo({1, 2, 2, 1}, armnn::DataType::Float32);
     const armnn::TensorInfo outputInfo({1, 1, 1, 1}, armnn::DataType::Float32);
@@ -2242,14 +1607,13 @@ BOOST_AUTO_TEST_CASE(SerializePooling2d)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    Pooling2dLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBaseWithDescriptor<armnn::Pooling2dDescriptor> verifier(
+            layerName, {inputInfo}, {outputInfo}, desc);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeQuantize)
 {
-    DECLARE_LAYER_VERIFIER_CLASS(Quantize)
-
     const std::string layerName("quantize");
     const armnn::TensorInfo info({ 1, 2, 2, 3 }, armnn::DataType::Float32);
 
@@ -2267,14 +1631,12 @@ BOOST_AUTO_TEST_CASE(SerializeQuantize)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    QuantizeLayerVerifier verifier(layerName, {info}, {info});
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBase verifier(layerName, {info}, {info});
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeRank)
 {
-    DECLARE_LAYER_VERIFIER_CLASS(Rank)
-
     const std::string layerName("rank");
     const armnn::TensorInfo inputInfo({1, 9}, armnn::DataType::Float32);
     const armnn::TensorInfo outputInfo({1}, armnn::DataType::Signed32);
@@ -2293,14 +1655,12 @@ BOOST_AUTO_TEST_CASE(SerializeRank)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    RankLayerVerifier verifier(layerName, {inputInfo}, {outputInfo});
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBase verifier(layerName, {inputInfo}, {outputInfo});
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeReduceSum)
 {
-    DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Reduce)
-
     const std::string layerName("Reduce_Sum");
     const armnn::TensorInfo inputInfo({1, 1, 3, 2}, armnn::DataType::Float32);
     const armnn::TensorInfo outputInfo({1, 1, 1, 2}, armnn::DataType::Float32);
@@ -2323,14 +1683,12 @@ BOOST_AUTO_TEST_CASE(SerializeReduceSum)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    ReduceLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor);
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBaseWithDescriptor<armnn::ReduceDescriptor> verifier(layerName, {inputInfo}, {outputInfo}, descriptor);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeReshape)
 {
-    DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Reshape)
-
     const std::string layerName("reshape");
     const armnn::TensorInfo inputInfo({1, 9}, armnn::DataType::Float32);
     const armnn::TensorInfo outputInfo({3, 3}, armnn::DataType::Float32);
@@ -2351,14 +1709,13 @@ BOOST_AUTO_TEST_CASE(SerializeReshape)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    ReshapeLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor);
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBaseWithDescriptor<armnn::ReshapeDescriptor> verifier(
+            layerName, {inputInfo}, {outputInfo}, descriptor);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeResize)
 {
-    DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Resize)
-
     const std::string layerName("resize");
     const armnn::TensorInfo inputInfo  = armnn::TensorInfo({1, 3, 5, 5}, armnn::DataType::Float32);
     const armnn::TensorInfo outputInfo = armnn::TensorInfo({1, 3, 2, 4}, armnn::DataType::Float32);
@@ -2384,8 +1741,8 @@ BOOST_AUTO_TEST_CASE(SerializeResize)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    ResizeLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBaseWithDescriptor<armnn::ResizeDescriptor> verifier(layerName, {inputInfo}, {outputInfo}, desc);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 class ResizeBilinearLayerVerifier : public LayerVerifierBaseWithDescriptor<armnn::ResizeBilinearDescriptor>
@@ -2398,25 +1755,36 @@ public:
         : LayerVerifierBaseWithDescriptor<armnn::ResizeBilinearDescriptor>(
             layerName, inputInfos, outputInfos, descriptor) {}
 
-    void VisitResizeLayer(const armnn::IConnectableLayer* layer,
-                          const armnn::ResizeDescriptor& descriptor,
-                          const char* name) override
-    {
-        VerifyNameAndConnections(layer, name);
-
-        BOOST_CHECK(descriptor.m_Method             == armnn::ResizeMethod::Bilinear);
-        BOOST_CHECK(descriptor.m_TargetWidth        == m_Descriptor.m_TargetWidth);
-        BOOST_CHECK(descriptor.m_TargetHeight       == m_Descriptor.m_TargetHeight);
-        BOOST_CHECK(descriptor.m_DataLayout         == m_Descriptor.m_DataLayout);
-        BOOST_CHECK(descriptor.m_AlignCorners       == m_Descriptor.m_AlignCorners);
-        BOOST_CHECK(descriptor.m_HalfPixelCenters   == m_Descriptor.m_HalfPixelCenters);
-    }
-
-    void VisitResizeBilinearLayer(const armnn::IConnectableLayer*,
-                                  const armnn::ResizeBilinearDescriptor&,
-                                  const char*) override
+    void ExecuteStrategy(const armnn::IConnectableLayer* layer,
+                         const armnn::BaseDescriptor& descriptor,
+                         const std::vector<armnn::ConstTensor>& constants,
+                         const char* name,
+                         const armnn::LayerBindingId id = 0) override
     {
-        throw armnn::Exception("ResizeBilinearLayer should have translated to ResizeLayer");
+        armnn::IgnoreUnused(descriptor, constants, id);
+        switch (layer->GetType())
+        {
+            case armnn::LayerType::Input: break;
+            case armnn::LayerType::Output: break;
+            case armnn::LayerType::Resize:
+            {
+                VerifyNameAndConnections(layer, name);
+                const armnn::ResizeDescriptor& layerDescriptor =
+                        static_cast<const armnn::ResizeDescriptor&>(descriptor);
+                BOOST_CHECK(layerDescriptor.m_Method             == armnn::ResizeMethod::Bilinear);
+                BOOST_CHECK(layerDescriptor.m_TargetWidth        == m_Descriptor.m_TargetWidth);
+                BOOST_CHECK(layerDescriptor.m_TargetHeight       == m_Descriptor.m_TargetHeight);
+                BOOST_CHECK(layerDescriptor.m_DataLayout         == m_Descriptor.m_DataLayout);
+                BOOST_CHECK(layerDescriptor.m_AlignCorners       == m_Descriptor.m_AlignCorners);
+                BOOST_CHECK(layerDescriptor.m_HalfPixelCenters   == m_Descriptor.m_HalfPixelCenters);
+                break;
+            }
+            default:
+            {
+                throw armnn::Exception("Unexpected layer type in test model. ResizeBiliniar "
+                                       "should have translated to Resize");
+            }
+        }
     }
 };
 
@@ -2452,7 +1820,7 @@ BOOST_AUTO_TEST_CASE(SerializeResizeBilinear)
     BOOST_CHECK(deserializedNetwork);
 
     ResizeBilinearLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);
-    deserializedNetwork->Accept(verifier);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(EnsureResizeBilinearBackwardCompatibility)
@@ -2508,13 +1876,11 @@ BOOST_AUTO_TEST_CASE(EnsureResizeBilinearBackwardCompatibility)
     descriptor.m_TargetHeight = 2u;
 
     ResizeBilinearLayerVerifier verifier("resizeBilinear", { inputInfo }, { outputInfo }, descriptor);
-    deserializedNetwork->Accept(verifier);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeSlice)
 {
-    DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Slice)
-
     const std::string layerName{"slice"};
 
     const armnn::TensorInfo inputInfo  = armnn::TensorInfo({3, 2, 3, 1}, armnn::DataType::Float32);
@@ -2537,14 +1903,12 @@ BOOST_AUTO_TEST_CASE(SerializeSlice)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    SliceLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor);
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBaseWithDescriptor<armnn::SliceDescriptor> verifier(layerName, {inputInfo}, {outputInfo}, descriptor);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeSoftmax)
 {
-    DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Softmax)
-
     const std::string layerName("softmax");
     const armnn::TensorInfo info({1, 10}, armnn::DataType::Float32);
 
@@ -2565,14 +1929,12 @@ BOOST_AUTO_TEST_CASE(SerializeSoftmax)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    SoftmaxLayerVerifier verifier(layerName, {info}, {info}, descriptor);
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBaseWithDescriptor<armnn::SoftmaxDescriptor> verifier(layerName, {info}, {info}, descriptor);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeSpaceToBatchNd)
 {
-    DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(SpaceToBatchNd)
-
     const std::string layerName("spaceToBatchNd");
     const armnn::TensorInfo inputInfo({2, 1, 2, 4}, armnn::DataType::Float32);
     const armnn::TensorInfo outputInfo({8, 1, 1, 3}, armnn::DataType::Float32);
@@ -2596,14 +1958,13 @@ BOOST_AUTO_TEST_CASE(SerializeSpaceToBatchNd)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    SpaceToBatchNdLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBaseWithDescriptor<armnn::SpaceToBatchNdDescriptor> verifier(
+            layerName, {inputInfo}, {outputInfo}, desc);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeSpaceToDepth)
 {
-    DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(SpaceToDepth)
-
     const std::string layerName("spaceToDepth");
 
     const armnn::TensorInfo inputInfo ({ 1, 16, 8,  3 }, armnn::DataType::Float32);
@@ -2627,14 +1988,13 @@ BOOST_AUTO_TEST_CASE(SerializeSpaceToDepth)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    SpaceToDepthLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBaseWithDescriptor<armnn::SpaceToDepthDescriptor> verifier(
+            layerName, {inputInfo}, {outputInfo}, desc);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeSplitter)
 {
-    DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Splitter)
-
     const unsigned int numViews = 3;
     const unsigned int numDimensions = 4;
     const unsigned int inputShape[] = {1, 18, 4, 4};
@@ -2682,14 +2042,13 @@ BOOST_AUTO_TEST_CASE(SerializeSplitter)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    SplitterLayerVerifier verifier(layerName, {inputInfo}, {outputInfo, outputInfo, outputInfo}, desc);
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBaseWithDescriptor<armnn::ViewsDescriptor> verifier(
+            layerName, {inputInfo}, {outputInfo, outputInfo, outputInfo}, desc);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeStack)
 {
-    DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Stack)
-
     const std::string layerName("stack");
 
     armnn::TensorInfo inputTensorInfo ({4, 3, 5}, armnn::DataType::Float32);
@@ -2714,14 +2073,13 @@ BOOST_AUTO_TEST_CASE(SerializeStack)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    StackLayerVerifier verifier(layerName, {inputTensorInfo, inputTensorInfo}, {outputTensorInfo}, descriptor);
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBaseWithDescriptor<armnn::StackDescriptor> verifier(
+            layerName, {inputTensorInfo, inputTensorInfo}, {outputTensorInfo}, descriptor);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeStandIn)
 {
-    DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(StandIn)
-
     const std::string layerName("standIn");
 
     armnn::TensorInfo tensorInfo({ 1u }, armnn::DataType::Float32);
@@ -2749,14 +2107,13 @@ BOOST_AUTO_TEST_CASE(SerializeStandIn)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    StandInLayerVerifier verifier(layerName, { tensorInfo, tensorInfo }, { tensorInfo, tensorInfo }, descriptor);
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBaseWithDescriptor<armnn::StandInDescriptor> verifier(
+            layerName, { tensorInfo, tensorInfo }, { tensorInfo, tensorInfo }, descriptor);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeStridedSlice)
 {
-    DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(StridedSlice)
-
     const std::string layerName("stridedSlice");
     const armnn::TensorInfo inputInfo = armnn::TensorInfo({3, 2, 3, 1}, armnn::DataType::Float32);
     const armnn::TensorInfo outputInfo = armnn::TensorInfo({3, 1}, armnn::DataType::Float32);
@@ -2780,14 +2137,13 @@ BOOST_AUTO_TEST_CASE(SerializeStridedSlice)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    StridedSliceLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBaseWithDescriptor<armnn::StridedSliceDescriptor> verifier(
+            layerName, {inputInfo}, {outputInfo}, desc);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeSubtraction)
 {
-    DECLARE_LAYER_VERIFIER_CLASS(Subtraction)
-
     const std::string layerName("subtraction");
     const armnn::TensorInfo info({ 1, 4 }, armnn::DataType::Float32);
 
@@ -2808,8 +2164,8 @@ BOOST_AUTO_TEST_CASE(SerializeSubtraction)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    SubtractionLayerVerifier verifier(layerName, {info, info}, {info});
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBase verifier(layerName, {info, info}, {info});
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeSwitch)
@@ -2820,16 +2176,31 @@ BOOST_AUTO_TEST_CASE(SerializeSwitch)
         SwitchLayerVerifier(const std::string& layerName,
                             const std::vector<armnn::TensorInfo>& inputInfos,
                             const std::vector<armnn::TensorInfo>& outputInfos)
-            : LayerVerifierBase(layerName, inputInfos, outputInfos) {}
+                : LayerVerifierBase(layerName, inputInfos, outputInfos) {}
 
-        void VisitSwitchLayer(const armnn::IConnectableLayer* layer, const char* name) override
+        void ExecuteStrategy(const armnn::IConnectableLayer* layer,
+                             const armnn::BaseDescriptor& descriptor,
+                             const std::vector<armnn::ConstTensor>& constants,
+                             const char* name,
+                             const armnn::LayerBindingId id = 0) override
         {
-            VerifyNameAndConnections(layer, name);
+            armnn::IgnoreUnused(descriptor, constants, id);
+            switch (layer->GetType())
+            {
+                case armnn::LayerType::Input: break;
+                case armnn::LayerType::Output: break;
+                case armnn::LayerType::Constant: break;
+                case armnn::LayerType::Switch:
+                {
+                    VerifyNameAndConnections(layer, name);
+                    break;
+                }
+                default:
+                {
+                    throw armnn::Exception("Unexpected layer type in Switch test model");
+                }
+            }
         }
-
-        void VisitConstantLayer(const armnn::IConnectableLayer*,
-                                const armnn::ConstTensor&,
-                                const char*) override {}
     };
 
     const std::string layerName("switch");
@@ -2859,13 +2230,11 @@ BOOST_AUTO_TEST_CASE(SerializeSwitch)
     BOOST_CHECK(deserializedNetwork);
 
     SwitchLayerVerifier verifier(layerName, {info, info}, {info, info});
-    deserializedNetwork->Accept(verifier);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeTranspose)
 {
-    DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Transpose)
-
     const std::string layerName("transpose");
     const armnn::TensorInfo inputTensorInfo({4, 3, 2, 1}, armnn::DataType::Float32);
     const armnn::TensorInfo outputTensorInfo({1, 2, 3, 4}, armnn::DataType::Float32);
@@ -2886,54 +2255,13 @@ BOOST_AUTO_TEST_CASE(SerializeTranspose)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    TransposeLayerVerifier verifier(layerName, {inputTensorInfo}, {outputTensorInfo}, descriptor);
-    deserializedNetwork->Accept(verifier);
+    LayerVerifierBaseWithDescriptor<armnn::TransposeDescriptor> verifier(
+            layerName, {inputTensorInfo}, {outputTensorInfo}, descriptor);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeTransposeConvolution2d)
 {
-    using Descriptor = armnn::TransposeConvolution2dDescriptor;
-    class TransposeConvolution2dLayerVerifier : public LayerVerifierBaseWithDescriptor<Descriptor>
-    {
-    public:
-        TransposeConvolution2dLayerVerifier(const std::string& layerName,
-                                            const std::vector<armnn::TensorInfo>& inputInfos,
-                                            const std::vector<armnn::TensorInfo>& outputInfos,
-                                            const Descriptor& descriptor,
-                                            const armnn::ConstTensor& weights,
-                                            const armnn::Optional<armnn::ConstTensor>& biases)
-            : LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor)
-            , m_Weights(weights)
-            , m_Biases(biases)
-        {}
-
-        void VisitTransposeConvolution2dLayer(const armnn::IConnectableLayer* layer,
-                                              const Descriptor& descriptor,
-                                              const armnn::ConstTensor& weights,
-                                              const armnn::Optional<armnn::ConstTensor>& biases,
-                                              const char* name) override
-        {
-            VerifyNameAndConnections(layer, name);
-            VerifyDescriptor(descriptor);
-
-            // check weights
-            CompareConstTensor(weights, m_Weights);
-
-            // check biases
-            BOOST_CHECK(biases.has_value() == descriptor.m_BiasEnabled);
-            BOOST_CHECK(biases.has_value() == m_Biases.has_value());
-
-            if (biases.has_value() && m_Biases.has_value())
-            {
-                CompareConstTensor(biases.value(), m_Biases.value());
-            }
-        }
-
-    private:
-        armnn::ConstTensor                      m_Weights;
-        armnn::Optional<armnn::ConstTensor>     m_Biases;
-    };
-
     const std::string layerName("transposeConvolution2d");
     const armnn::TensorInfo inputInfo ({ 1, 7, 7, 1 }, armnn::DataType::Float32);
     const armnn::TensorInfo outputInfo({ 1, 9, 9, 1 }, armnn::DataType::Float32);
@@ -2975,8 +2303,10 @@ BOOST_AUTO_TEST_CASE(SerializeTransposeConvolution2d)
     armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
     BOOST_CHECK(deserializedNetwork);
 
-    TransposeConvolution2dLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor, weights, biases);
-    deserializedNetwork->Accept(verifier);
+    const std::vector<armnn::ConstTensor> constants {weights, biases};
+    LayerVerifierBaseWithDescriptorAndConstants<armnn::TransposeConvolution2dDescriptor> verifier(
+            layerName, {inputInfo}, {outputInfo}, descriptor, constants);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_CASE(SerializeDeserializeNonLinearNetwork)
@@ -2991,16 +2321,31 @@ BOOST_AUTO_TEST_CASE(SerializeDeserializeNonLinearNetwork)
             : LayerVerifierBase(layerName, inputInfos, outputInfos)
             , m_LayerInput(layerInput) {}
 
-        void VisitConstantLayer(const armnn::IConnectableLayer* layer,
-                                const armnn::ConstTensor& input,
-                                const char* name) override
+        void ExecuteStrategy(const armnn::IConnectableLayer* layer,
+                             const armnn::BaseDescriptor& descriptor,
+                             const std::vector<armnn::ConstTensor>& constants,
+                             const char* name,
+                             const armnn::LayerBindingId id = 0) override
         {
-            VerifyNameAndConnections(layer, name);
-            CompareConstTensor(input, m_LayerInput);
+            armnn::IgnoreUnused(descriptor, constants, id);
+            switch (layer->GetType())
+            {
+                case armnn::LayerType::Input: break;
+                case armnn::LayerType::Output: break;
+                case armnn::LayerType::Addition: break;
+                case armnn::LayerType::Constant:
+                {
+                    VerifyNameAndConnections(layer, name);
+                    CompareConstTensor(constants.at(0), m_LayerInput);
+                    break;
+                }
+                default:
+                {
+                    throw armnn::Exception("Unexpected layer type in test model");
+                }
+            }
         }
 
-        void VisitAdditionLayer(const armnn::IConnectableLayer*, const char*) override {}
-
     private:
         armnn::ConstTensor m_LayerInput;
     };
@@ -3029,2125 +2374,7 @@ BOOST_AUTO_TEST_CASE(SerializeDeserializeNonLinearNetwork)
     BOOST_CHECK(deserializedNetwork);
 
     ConstantLayerVerifier verifier(layerName, {}, {info}, constTensor);
-    deserializedNetwork->Accept(verifier);
-}
-
-class VerifyLstmLayer : public LayerVerifierBaseWithDescriptor<armnn::LstmDescriptor>
-{
-public:
-    VerifyLstmLayer(const std::string& layerName,
-                    const std::vector<armnn::TensorInfo>& inputInfos,
-                    const std::vector<armnn::TensorInfo>& outputInfos,
-                    const armnn::LstmDescriptor& descriptor,
-                    const armnn::LstmInputParams& inputParams)
-        : LayerVerifierBaseWithDescriptor<armnn::LstmDescriptor>(layerName, inputInfos, outputInfos, descriptor)
-        , m_InputParams(inputParams) {}
-
-    void VisitLstmLayer(const armnn::IConnectableLayer* layer,
-                        const armnn::LstmDescriptor& descriptor,
-                        const armnn::LstmInputParams& params,
-                        const char* name)
-    {
-        VerifyNameAndConnections(layer, name);
-        VerifyDescriptor(descriptor);
-        VerifyInputParameters(params);
-    }
-
-protected:
-    void VerifyInputParameters(const armnn::LstmInputParams& params)
-    {
-        VerifyConstTensors(
-            "m_InputToInputWeights", m_InputParams.m_InputToInputWeights, params.m_InputToInputWeights);
-        VerifyConstTensors(
-            "m_InputToForgetWeights", m_InputParams.m_InputToForgetWeights, params.m_InputToForgetWeights);
-        VerifyConstTensors(
-            "m_InputToCellWeights", m_InputParams.m_InputToCellWeights, params.m_InputToCellWeights);
-        VerifyConstTensors(
-            "m_InputToOutputWeights", m_InputParams.m_InputToOutputWeights, params.m_InputToOutputWeights);
-        VerifyConstTensors(
-            "m_RecurrentToInputWeights", m_InputParams.m_RecurrentToInputWeights, params.m_RecurrentToInputWeights);
-        VerifyConstTensors(
-            "m_RecurrentToForgetWeights", m_InputParams.m_RecurrentToForgetWeights, params.m_RecurrentToForgetWeights);
-        VerifyConstTensors(
-            "m_RecurrentToCellWeights", m_InputParams.m_RecurrentToCellWeights, params.m_RecurrentToCellWeights);
-        VerifyConstTensors(
-            "m_RecurrentToOutputWeights", m_InputParams.m_RecurrentToOutputWeights, params.m_RecurrentToOutputWeights);
-        VerifyConstTensors(
-            "m_CellToInputWeights", m_InputParams.m_CellToInputWeights, params.m_CellToInputWeights);
-        VerifyConstTensors(
-            "m_CellToForgetWeights", m_InputParams.m_CellToForgetWeights, params.m_CellToForgetWeights);
-        VerifyConstTensors(
-            "m_CellToOutputWeights", m_InputParams.m_CellToOutputWeights, params.m_CellToOutputWeights);
-        VerifyConstTensors(
-            "m_InputGateBias", m_InputParams.m_InputGateBias, params.m_InputGateBias);
-        VerifyConstTensors(
-            "m_ForgetGateBias", m_InputParams.m_ForgetGateBias, params.m_ForgetGateBias);
-        VerifyConstTensors(
-            "m_CellBias", m_InputParams.m_CellBias, params.m_CellBias);
-        VerifyConstTensors(
-            "m_OutputGateBias", m_InputParams.m_OutputGateBias, params.m_OutputGateBias);
-        VerifyConstTensors(
-            "m_ProjectionWeights", m_InputParams.m_ProjectionWeights, params.m_ProjectionWeights);
-        VerifyConstTensors(
-            "m_ProjectionBias", m_InputParams.m_ProjectionBias, params.m_ProjectionBias);
-        VerifyConstTensors(
-            "m_InputLayerNormWeights", m_InputParams.m_InputLayerNormWeights, params.m_InputLayerNormWeights);
-        VerifyConstTensors(
-            "m_ForgetLayerNormWeights", m_InputParams.m_ForgetLayerNormWeights, params.m_ForgetLayerNormWeights);
-        VerifyConstTensors(
-            "m_CellLayerNormWeights", m_InputParams.m_CellLayerNormWeights, params.m_CellLayerNormWeights);
-        VerifyConstTensors(
-            "m_OutputLayerNormWeights", m_InputParams.m_OutputLayerNormWeights, params.m_OutputLayerNormWeights);
-    }
-
-private:
-    armnn::LstmInputParams m_InputParams;
-};
-
-BOOST_AUTO_TEST_CASE(SerializeDeserializeLstmCifgPeepholeNoProjection)
-{
-    armnn::LstmDescriptor descriptor;
-    descriptor.m_ActivationFunc = 4;
-    descriptor.m_ClippingThresProj = 0.0f;
-    descriptor.m_ClippingThresCell = 0.0f;
-    descriptor.m_CifgEnabled = true; // if this is true then we DON'T need to set the OptCifgParams
-    descriptor.m_ProjectionEnabled = false;
-    descriptor.m_PeepholeEnabled = true;
-
-    const uint32_t batchSize = 1;
-    const uint32_t inputSize = 2;
-    const uint32_t numUnits = 4;
-    const uint32_t outputSize = numUnits;
-
-    armnn::TensorInfo inputWeightsInfo1({numUnits, inputSize}, armnn::DataType::Float32);
-    std::vector<float> inputToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements());
-    armnn::ConstTensor inputToForgetWeights(inputWeightsInfo1, inputToForgetWeightsData);
-
-    std::vector<float> inputToCellWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements());
-    armnn::ConstTensor inputToCellWeights(inputWeightsInfo1, inputToCellWeightsData);
-
-    std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements());
-    armnn::ConstTensor inputToOutputWeights(inputWeightsInfo1, inputToOutputWeightsData);
-
-    armnn::TensorInfo inputWeightsInfo2({numUnits, outputSize}, armnn::DataType::Float32);
-    std::vector<float> recurrentToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements());
-    armnn::ConstTensor recurrentToForgetWeights(inputWeightsInfo2, recurrentToForgetWeightsData);
-
-    std::vector<float> recurrentToCellWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements());
-    armnn::ConstTensor recurrentToCellWeights(inputWeightsInfo2, recurrentToCellWeightsData);
-
-    std::vector<float> recurrentToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements());
-    armnn::ConstTensor recurrentToOutputWeights(inputWeightsInfo2, recurrentToOutputWeightsData);
-
-    armnn::TensorInfo inputWeightsInfo3({numUnits}, armnn::DataType::Float32);
-    std::vector<float> cellToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo3.GetNumElements());
-    armnn::ConstTensor cellToForgetWeights(inputWeightsInfo3, cellToForgetWeightsData);
-
-    std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo3.GetNumElements());
-    armnn::ConstTensor cellToOutputWeights(inputWeightsInfo3, cellToOutputWeightsData);
-
-    std::vector<float> forgetGateBiasData(numUnits, 1.0f);
-    armnn::ConstTensor forgetGateBias(inputWeightsInfo3, forgetGateBiasData);
-
-    std::vector<float> cellBiasData(numUnits, 0.0f);
-    armnn::ConstTensor cellBias(inputWeightsInfo3, cellBiasData);
-
-    std::vector<float> outputGateBiasData(numUnits, 0.0f);
-    armnn::ConstTensor outputGateBias(inputWeightsInfo3, outputGateBiasData);
-
-    armnn::LstmInputParams params;
-    params.m_InputToForgetWeights = &inputToForgetWeights;
-    params.m_InputToCellWeights = &inputToCellWeights;
-    params.m_InputToOutputWeights = &inputToOutputWeights;
-    params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
-    params.m_RecurrentToCellWeights = &recurrentToCellWeights;
-    params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
-    params.m_ForgetGateBias = &forgetGateBias;
-    params.m_CellBias = &cellBias;
-    params.m_OutputGateBias = &outputGateBias;
-    params.m_CellToForgetWeights = &cellToForgetWeights;
-    params.m_CellToOutputWeights = &cellToOutputWeights;
-
-    armnn::INetworkPtr network = armnn::INetwork::Create();
-    armnn::IConnectableLayer* const inputLayer   = network->AddInputLayer(0);
-    armnn::IConnectableLayer* const cellStateIn = network->AddInputLayer(1);
-    armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(2);
-    const std::string layerName("lstm");
-    armnn::IConnectableLayer* const lstmLayer = network->AddLstmLayer(descriptor, params, layerName.c_str());
-    armnn::IConnectableLayer* const scratchBuffer  = network->AddOutputLayer(0);
-    armnn::IConnectableLayer* const outputStateOut  = network->AddOutputLayer(1);
-    armnn::IConnectableLayer* const cellStateOut  = network->AddOutputLayer(2);
-    armnn::IConnectableLayer* const outputLayer  = network->AddOutputLayer(3);
-
-    // connect up
-    armnn::TensorInfo inputTensorInfo({ batchSize, inputSize }, armnn::DataType::Float32);
-    armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits}, armnn::DataType::Float32);
-    armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32);
-    armnn::TensorInfo lstmTensorInfoScratchBuff({ batchSize, numUnits * 3 }, armnn::DataType::Float32);
-
-    inputLayer->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(0));
-    inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
-
-    outputStateIn->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(1));
-    outputStateIn->GetOutputSlot(0).SetTensorInfo(outputStateTensorInfo);
-
-    cellStateIn->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(2));
-    cellStateIn->GetOutputSlot(0).SetTensorInfo(cellStateTensorInfo);
-
-    lstmLayer->GetOutputSlot(0).Connect(scratchBuffer->GetInputSlot(0));
-    lstmLayer->GetOutputSlot(0).SetTensorInfo(lstmTensorInfoScratchBuff);
-
-    lstmLayer->GetOutputSlot(1).Connect(outputStateOut->GetInputSlot(0));
-    lstmLayer->GetOutputSlot(1).SetTensorInfo(outputStateTensorInfo);
-
-    lstmLayer->GetOutputSlot(2).Connect(cellStateOut->GetInputSlot(0));
-    lstmLayer->GetOutputSlot(2).SetTensorInfo(cellStateTensorInfo);
-
-    lstmLayer->GetOutputSlot(3).Connect(outputLayer->GetInputSlot(0));
-    lstmLayer->GetOutputSlot(3).SetTensorInfo(outputStateTensorInfo);
-
-    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
-    BOOST_CHECK(deserializedNetwork);
-
-    VerifyLstmLayer checker(
-        layerName,
-        {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
-        {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},
-        descriptor,
-        params);
-    deserializedNetwork->Accept(checker);
-}
-
-BOOST_AUTO_TEST_CASE(SerializeDeserializeLstmNoCifgWithPeepholeAndProjection)
-{
-    armnn::LstmDescriptor descriptor;
-    descriptor.m_ActivationFunc = 4;
-    descriptor.m_ClippingThresProj = 0.0f;
-    descriptor.m_ClippingThresCell = 0.0f;
-    descriptor.m_CifgEnabled = false; // if this is true then we DON'T need to set the OptCifgParams
-    descriptor.m_ProjectionEnabled = true;
-    descriptor.m_PeepholeEnabled = true;
-
-    const uint32_t batchSize = 2;
-    const uint32_t inputSize = 5;
-    const uint32_t numUnits = 20;
-    const uint32_t outputSize = 16;
-
-    armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::DataType::Float32);
-    std::vector<float> inputToInputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
-    armnn::ConstTensor inputToInputWeights(tensorInfo20x5, inputToInputWeightsData);
-
-    std::vector<float> inputToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
-    armnn::ConstTensor inputToForgetWeights(tensorInfo20x5, inputToForgetWeightsData);
-
-    std::vector<float> inputToCellWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
-    armnn::ConstTensor inputToCellWeights(tensorInfo20x5, inputToCellWeightsData);
-
-    std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
-    armnn::ConstTensor inputToOutputWeights(tensorInfo20x5, inputToOutputWeightsData);
-
-    armnn::TensorInfo tensorInfo20({numUnits}, armnn::DataType::Float32);
-    std::vector<float> inputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
-    armnn::ConstTensor inputGateBias(tensorInfo20, inputGateBiasData);
-
-    std::vector<float> forgetGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
-    armnn::ConstTensor forgetGateBias(tensorInfo20, forgetGateBiasData);
-
-    std::vector<float> cellBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
-    armnn::ConstTensor cellBias(tensorInfo20, cellBiasData);
-
-    std::vector<float> outputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
-    armnn::ConstTensor outputGateBias(tensorInfo20, outputGateBiasData);
-
-    armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, armnn::DataType::Float32);
-    std::vector<float> recurrentToInputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
-    armnn::ConstTensor recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData);
-
-    std::vector<float> recurrentToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
-    armnn::ConstTensor recurrentToForgetWeights(tensorInfo20x16, recurrentToForgetWeightsData);
-
-    std::vector<float> recurrentToCellWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
-    armnn::ConstTensor recurrentToCellWeights(tensorInfo20x16, recurrentToCellWeightsData);
-
-    std::vector<float> recurrentToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
-    armnn::ConstTensor recurrentToOutputWeights(tensorInfo20x16, recurrentToOutputWeightsData);
-
-    std::vector<float> cellToInputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
-    armnn::ConstTensor cellToInputWeights(tensorInfo20, cellToInputWeightsData);
-
-    std::vector<float> cellToForgetWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
-    armnn::ConstTensor cellToForgetWeights(tensorInfo20, cellToForgetWeightsData);
-
-    std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
-    armnn::ConstTensor cellToOutputWeights(tensorInfo20,  cellToOutputWeightsData);
-
-    armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, armnn::DataType::Float32);
-    std::vector<float> projectionWeightsData = GenerateRandomData<float>(tensorInfo16x20.GetNumElements());
-    armnn::ConstTensor projectionWeights(tensorInfo16x20, projectionWeightsData);
-
-    armnn::TensorInfo tensorInfo16({outputSize}, armnn::DataType::Float32);
-    std::vector<float> projectionBiasData(outputSize, 0.f);
-    armnn::ConstTensor projectionBias(tensorInfo16, projectionBiasData);
-
-    armnn::LstmInputParams params;
-    params.m_InputToForgetWeights = &inputToForgetWeights;
-    params.m_InputToCellWeights = &inputToCellWeights;
-    params.m_InputToOutputWeights = &inputToOutputWeights;
-    params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
-    params.m_RecurrentToCellWeights = &recurrentToCellWeights;
-    params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
-    params.m_ForgetGateBias = &forgetGateBias;
-    params.m_CellBias = &cellBias;
-    params.m_OutputGateBias = &outputGateBias;
-
-    // additional params because: descriptor.m_CifgEnabled = false
-    params.m_InputToInputWeights = &inputToInputWeights;
-    params.m_RecurrentToInputWeights = &recurrentToInputWeights;
-    params.m_CellToInputWeights = &cellToInputWeights;
-    params.m_InputGateBias = &inputGateBias;
-
-    // additional params because: descriptor.m_ProjectionEnabled = true
-    params.m_ProjectionWeights = &projectionWeights;
-    params.m_ProjectionBias = &projectionBias;
-
-    // additional params because: descriptor.m_PeepholeEnabled = true
-    params.m_CellToForgetWeights = &cellToForgetWeights;
-    params.m_CellToOutputWeights = &cellToOutputWeights;
-
-    armnn::INetworkPtr network = armnn::INetwork::Create();
-    armnn::IConnectableLayer* const inputLayer   = network->AddInputLayer(0);
-    armnn::IConnectableLayer* const cellStateIn = network->AddInputLayer(1);
-    armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(2);
-    const std::string layerName("lstm");
-    armnn::IConnectableLayer* const lstmLayer = network->AddLstmLayer(descriptor, params, layerName.c_str());
-    armnn::IConnectableLayer* const scratchBuffer  = network->AddOutputLayer(0);
-    armnn::IConnectableLayer* const outputStateOut  = network->AddOutputLayer(1);
-    armnn::IConnectableLayer* const cellStateOut  = network->AddOutputLayer(2);
-    armnn::IConnectableLayer* const outputLayer  = network->AddOutputLayer(3);
-
-    // connect up
-    armnn::TensorInfo inputTensorInfo({ batchSize, inputSize }, armnn::DataType::Float32);
-    armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits}, armnn::DataType::Float32);
-    armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32);
-    armnn::TensorInfo lstmTensorInfoScratchBuff({ batchSize, numUnits * 4 }, armnn::DataType::Float32);
-
-    inputLayer->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(0));
-    inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
-
-    outputStateIn->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(1));
-    outputStateIn->GetOutputSlot(0).SetTensorInfo(outputStateTensorInfo);
-
-    cellStateIn->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(2));
-    cellStateIn->GetOutputSlot(0).SetTensorInfo(cellStateTensorInfo);
-
-    lstmLayer->GetOutputSlot(0).Connect(scratchBuffer->GetInputSlot(0));
-    lstmLayer->GetOutputSlot(0).SetTensorInfo(lstmTensorInfoScratchBuff);
-
-    lstmLayer->GetOutputSlot(1).Connect(outputStateOut->GetInputSlot(0));
-    lstmLayer->GetOutputSlot(1).SetTensorInfo(outputStateTensorInfo);
-
-    lstmLayer->GetOutputSlot(2).Connect(cellStateOut->GetInputSlot(0));
-    lstmLayer->GetOutputSlot(2).SetTensorInfo(cellStateTensorInfo);
-
-    lstmLayer->GetOutputSlot(3).Connect(outputLayer->GetInputSlot(0));
-    lstmLayer->GetOutputSlot(3).SetTensorInfo(outputStateTensorInfo);
-
-    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
-    BOOST_CHECK(deserializedNetwork);
-
-    VerifyLstmLayer checker(
-        layerName,
-        {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
-        {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},
-        descriptor,
-        params);
-    deserializedNetwork->Accept(checker);
-}
-
-BOOST_AUTO_TEST_CASE(SerializeDeserializeLstmNoCifgWithPeepholeWithProjectionWithLayerNorm)
-{
-    armnn::LstmDescriptor descriptor;
-    descriptor.m_ActivationFunc = 4;
-    descriptor.m_ClippingThresProj = 0.0f;
-    descriptor.m_ClippingThresCell = 0.0f;
-    descriptor.m_CifgEnabled = false; // if this is true then we DON'T need to set the OptCifgParams
-    descriptor.m_ProjectionEnabled = true;
-    descriptor.m_PeepholeEnabled = true;
-    descriptor.m_LayerNormEnabled = true;
-
-    const uint32_t batchSize = 2;
-    const uint32_t inputSize = 5;
-    const uint32_t numUnits = 20;
-    const uint32_t outputSize = 16;
-
-    armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::DataType::Float32);
-    std::vector<float> inputToInputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
-    armnn::ConstTensor inputToInputWeights(tensorInfo20x5, inputToInputWeightsData);
-
-    std::vector<float> inputToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
-    armnn::ConstTensor inputToForgetWeights(tensorInfo20x5, inputToForgetWeightsData);
-
-    std::vector<float> inputToCellWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
-    armnn::ConstTensor inputToCellWeights(tensorInfo20x5, inputToCellWeightsData);
-
-    std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
-    armnn::ConstTensor inputToOutputWeights(tensorInfo20x5, inputToOutputWeightsData);
-
-    armnn::TensorInfo tensorInfo20({numUnits}, armnn::DataType::Float32);
-    std::vector<float> inputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
-    armnn::ConstTensor inputGateBias(tensorInfo20, inputGateBiasData);
-
-    std::vector<float> forgetGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
-    armnn::ConstTensor forgetGateBias(tensorInfo20, forgetGateBiasData);
-
-    std::vector<float> cellBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
-    armnn::ConstTensor cellBias(tensorInfo20, cellBiasData);
-
-    std::vector<float> outputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
-    armnn::ConstTensor outputGateBias(tensorInfo20, outputGateBiasData);
-
-    armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, armnn::DataType::Float32);
-    std::vector<float> recurrentToInputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
-    armnn::ConstTensor recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData);
-
-    std::vector<float> recurrentToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
-    armnn::ConstTensor recurrentToForgetWeights(tensorInfo20x16, recurrentToForgetWeightsData);
-
-    std::vector<float> recurrentToCellWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
-    armnn::ConstTensor recurrentToCellWeights(tensorInfo20x16, recurrentToCellWeightsData);
-
-    std::vector<float> recurrentToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
-    armnn::ConstTensor recurrentToOutputWeights(tensorInfo20x16, recurrentToOutputWeightsData);
-
-    std::vector<float> cellToInputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
-    armnn::ConstTensor cellToInputWeights(tensorInfo20, cellToInputWeightsData);
-
-    std::vector<float> cellToForgetWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
-    armnn::ConstTensor cellToForgetWeights(tensorInfo20, cellToForgetWeightsData);
-
-    std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
-    armnn::ConstTensor cellToOutputWeights(tensorInfo20,  cellToOutputWeightsData);
-
-    armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, armnn::DataType::Float32);
-    std::vector<float> projectionWeightsData = GenerateRandomData<float>(tensorInfo16x20.GetNumElements());
-    armnn::ConstTensor projectionWeights(tensorInfo16x20, projectionWeightsData);
-
-    armnn::TensorInfo tensorInfo16({outputSize}, armnn::DataType::Float32);
-    std::vector<float> projectionBiasData(outputSize, 0.f);
-    armnn::ConstTensor projectionBias(tensorInfo16, projectionBiasData);
-
-    std::vector<float> inputLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
-    armnn::ConstTensor inputLayerNormWeights(tensorInfo20, forgetGateBiasData);
-
-    std::vector<float> forgetLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
-    armnn::ConstTensor forgetLayerNormWeights(tensorInfo20, forgetGateBiasData);
-
-    std::vector<float> cellLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
-    armnn::ConstTensor cellLayerNormWeights(tensorInfo20, forgetGateBiasData);
-
-    std::vector<float> outLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
-    armnn::ConstTensor outLayerNormWeights(tensorInfo20, forgetGateBiasData);
-
-    armnn::LstmInputParams params;
-    params.m_InputToForgetWeights = &inputToForgetWeights;
-    params.m_InputToCellWeights = &inputToCellWeights;
-    params.m_InputToOutputWeights = &inputToOutputWeights;
-    params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
-    params.m_RecurrentToCellWeights = &recurrentToCellWeights;
-    params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
-    params.m_ForgetGateBias = &forgetGateBias;
-    params.m_CellBias = &cellBias;
-    params.m_OutputGateBias = &outputGateBias;
-
-    // additional params because: descriptor.m_CifgEnabled = false
-    params.m_InputToInputWeights = &inputToInputWeights;
-    params.m_RecurrentToInputWeights = &recurrentToInputWeights;
-    params.m_CellToInputWeights = &cellToInputWeights;
-    params.m_InputGateBias = &inputGateBias;
-
-    // additional params because: descriptor.m_ProjectionEnabled = true
-    params.m_ProjectionWeights = &projectionWeights;
-    params.m_ProjectionBias = &projectionBias;
-
-    // additional params because: descriptor.m_PeepholeEnabled = true
-    params.m_CellToForgetWeights = &cellToForgetWeights;
-    params.m_CellToOutputWeights = &cellToOutputWeights;
-
-    // additional params because: despriptor.m_LayerNormEnabled = true
-    params.m_InputLayerNormWeights = &inputLayerNormWeights;
-    params.m_ForgetLayerNormWeights = &forgetLayerNormWeights;
-    params.m_CellLayerNormWeights = &cellLayerNormWeights;
-    params.m_OutputLayerNormWeights = &outLayerNormWeights;
-
-    armnn::INetworkPtr network = armnn::INetwork::Create();
-    armnn::IConnectableLayer* const inputLayer   = network->AddInputLayer(0);
-    armnn::IConnectableLayer* const cellStateIn = network->AddInputLayer(1);
-    armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(2);
-    const std::string layerName("lstm");
-    armnn::IConnectableLayer* const lstmLayer = network->AddLstmLayer(descriptor, params, layerName.c_str());
-    armnn::IConnectableLayer* const scratchBuffer  = network->AddOutputLayer(0);
-    armnn::IConnectableLayer* const outputStateOut  = network->AddOutputLayer(1);
-    armnn::IConnectableLayer* const cellStateOut  = network->AddOutputLayer(2);
-    armnn::IConnectableLayer* const outputLayer  = network->AddOutputLayer(3);
-
-    // connect up
-    armnn::TensorInfo inputTensorInfo({ batchSize, inputSize }, armnn::DataType::Float32);
-    armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits}, armnn::DataType::Float32);
-    armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32);
-    armnn::TensorInfo lstmTensorInfoScratchBuff({ batchSize, numUnits * 4 }, armnn::DataType::Float32);
-
-    inputLayer->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(0));
-    inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
-
-    outputStateIn->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(1));
-    outputStateIn->GetOutputSlot(0).SetTensorInfo(outputStateTensorInfo);
-
-    cellStateIn->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(2));
-    cellStateIn->GetOutputSlot(0).SetTensorInfo(cellStateTensorInfo);
-
-    lstmLayer->GetOutputSlot(0).Connect(scratchBuffer->GetInputSlot(0));
-    lstmLayer->GetOutputSlot(0).SetTensorInfo(lstmTensorInfoScratchBuff);
-
-    lstmLayer->GetOutputSlot(1).Connect(outputStateOut->GetInputSlot(0));
-    lstmLayer->GetOutputSlot(1).SetTensorInfo(outputStateTensorInfo);
-
-    lstmLayer->GetOutputSlot(2).Connect(cellStateOut->GetInputSlot(0));
-    lstmLayer->GetOutputSlot(2).SetTensorInfo(cellStateTensorInfo);
-
-    lstmLayer->GetOutputSlot(3).Connect(outputLayer->GetInputSlot(0));
-    lstmLayer->GetOutputSlot(3).SetTensorInfo(outputStateTensorInfo);
-
-    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
-    BOOST_CHECK(deserializedNetwork);
-
-    VerifyLstmLayer checker(
-            layerName,
-            {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
-            {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},
-            descriptor,
-            params);
-    deserializedNetwork->Accept(checker);
-}
-
-BOOST_AUTO_TEST_CASE(EnsureLstmLayersBackwardCompatibility)
-{
-    // The hex data below is a flat buffer containing a lstm layer with no Cifg, with peephole and projection
-    // enabled. That data was obtained before additional layer normalization parameters where added to the
-    // lstm serializer. That way it can be tested if a lstm model with the old parameter configuration can
-    // still be loaded
-    const std::vector<uint8_t> lstmNoCifgWithPeepholeAndProjectionModel =
-    {
-        0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,
-        0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x2C, 0x00, 0x00, 0x00, 0x38, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00,
-        0xDC, 0x29, 0x00, 0x00, 0x38, 0x29, 0x00, 0x00, 0xB4, 0x28, 0x00, 0x00, 0x94, 0x01, 0x00, 0x00, 0x3C, 0x01,
-        0x00, 0x00, 0xE0, 0x00, 0x00, 0x00, 0x84, 0x00, 0x00, 0x00, 0x28, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00,
-        0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x70, 0xD6, 0xFF, 0xFF,
-        0x00, 0x00, 0x00, 0x0B, 0x04, 0x00, 0x00, 0x00, 0x06, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x88, 0xD7,
-        0xFF, 0xFF, 0x08, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0xF6, 0xD6, 0xFF, 0xFF, 0x07, 0x00, 0x00, 0x00,
-        0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0xE8, 0xD7, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0xC8, 0xD6, 0xFF, 0xFF, 0x00, 0x00,
-        0x00, 0x0B, 0x04, 0x00, 0x00, 0x00, 0x5E, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0xE0, 0xD7, 0xFF, 0xFF,
-        0x08, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x4E, 0xD7, 0xFF, 0xFF, 0x06, 0x00, 0x00, 0x00, 0x10, 0x00,
-        0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x40, 0xD8,
-        0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x20, 0xD7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B,
-        0x04, 0x00, 0x00, 0x00, 0xB6, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x38, 0xD8, 0xFF, 0xFF, 0x08, 0x00,
-        0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0xA6, 0xD7, 0xFF, 0xFF, 0x05, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
-        0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x98, 0xD8, 0xFF, 0xFF,
-        0x03, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x78, 0xD7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B, 0x04, 0x00,
-        0x00, 0x00, 0x0E, 0xD8, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x16, 0xD8, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00,
-        0xFA, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00,
-        0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
-        0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xD8, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x6C, 0xD8, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x23, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00,
-        0x12, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00, 0x00, 0x00, 0xE0, 0x25, 0x00, 0x00, 0xD0, 0x25,
-        0x00, 0x00, 0x2C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x26, 0x00, 0x48, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00,
-        0x10, 0x00, 0x14, 0x00, 0x18, 0x00, 0x1C, 0x00, 0x20, 0x00, 0x24, 0x00, 0x28, 0x00, 0x2C, 0x00, 0x30, 0x00,
-        0x34, 0x00, 0x38, 0x00, 0x3C, 0x00, 0x40, 0x00, 0x44, 0x00, 0x26, 0x00, 0x00, 0x00, 0xC4, 0x23, 0x00, 0x00,
-        0xF8, 0x21, 0x00, 0x00, 0x2C, 0x20, 0x00, 0x00, 0xF0, 0x1A, 0x00, 0x00, 0xB4, 0x15, 0x00, 0x00, 0x78, 0x10,
-        0x00, 0x00, 0xF0, 0x0F, 0x00, 0x00, 0x68, 0x0F, 0x00, 0x00, 0xE0, 0x0E, 0x00, 0x00, 0x14, 0x0D, 0x00, 0x00,
-        0xD8, 0x07, 0x00, 0x00, 0x50, 0x07, 0x00, 0x00, 0xC8, 0x06, 0x00, 0x00, 0x8C, 0x01, 0x00, 0x00, 0x14, 0x01,
-        0x00, 0x00, 0x8C, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xEE, 0xD7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03,
-        0x64, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xFE, 0xD8, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x14, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5A, 0xD8, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01,
-        0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x72, 0xD8,
-        0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0x64, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x82, 0xD9, 0xFF, 0xFF,
-        0x04, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDE, 0xD8,
-        0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
-        0x14, 0x00, 0x00, 0x00, 0xF6, 0xD8, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0x54, 0x00, 0x00, 0x00, 0x04, 0x00,
-        0x00, 0x00, 0x06, 0xDA, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x52, 0xD9, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x6A, 0xD9, 0xFF, 0xFF, 0x00, 0x00,
-        0x00, 0x03, 0x14, 0x05, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x7A, 0xDA, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00,
-        0x40, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
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-    };
-
-    armnn::INetworkPtr deserializedNetwork =
-        DeserializeNetwork(std::string(lstmNoCifgWithPeepholeAndProjectionModel.begin(),
-                                       lstmNoCifgWithPeepholeAndProjectionModel.end()));
-
-    BOOST_CHECK(deserializedNetwork);
-
-    // generating the same model parameters which where used to serialize the model (Layer norm is not specified)
-    armnn::LstmDescriptor descriptor;
-    descriptor.m_ActivationFunc    = 4;
-    descriptor.m_ClippingThresProj = 0.0f;
-    descriptor.m_ClippingThresCell = 0.0f;
-    descriptor.m_CifgEnabled       = false;
-    descriptor.m_ProjectionEnabled = true;
-    descriptor.m_PeepholeEnabled   = true;
-
-    const uint32_t batchSize  = 2u;
-    const uint32_t inputSize  = 5u;
-    const uint32_t numUnits   = 20u;
-    const uint32_t outputSize = 16u;
-
-    armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::DataType::Float32);
-    std::vector<float> inputToInputWeightsData(tensorInfo20x5.GetNumElements(), 0.0f);
-    armnn::ConstTensor inputToInputWeights(tensorInfo20x5, inputToInputWeightsData);
-
-    std::vector<float> inputToForgetWeightsData(tensorInfo20x5.GetNumElements(), 0.0f);
-    armnn::ConstTensor inputToForgetWeights(tensorInfo20x5, inputToForgetWeightsData);
-
-    std::vector<float> inputToCellWeightsData(tensorInfo20x5.GetNumElements(), 0.0f);
-    armnn::ConstTensor inputToCellWeights(tensorInfo20x5, inputToCellWeightsData);
-
-    std::vector<float> inputToOutputWeightsData(tensorInfo20x5.GetNumElements(), 0.0f);
-    armnn::ConstTensor inputToOutputWeights(tensorInfo20x5, inputToOutputWeightsData);
-
-    armnn::TensorInfo tensorInfo20({numUnits}, armnn::DataType::Float32);
-    std::vector<float> inputGateBiasData(tensorInfo20.GetNumElements(), 0.0f);
-    armnn::ConstTensor inputGateBias(tensorInfo20, inputGateBiasData);
-
-    std::vector<float> forgetGateBiasData(tensorInfo20.GetNumElements(), 0.0f);
-    armnn::ConstTensor forgetGateBias(tensorInfo20, forgetGateBiasData);
-
-    std::vector<float> cellBiasData(tensorInfo20.GetNumElements(), 0.0f);
-    armnn::ConstTensor cellBias(tensorInfo20, cellBiasData);
-
-    std::vector<float> outputGateBiasData(tensorInfo20.GetNumElements(), 0.0f);
-    armnn::ConstTensor outputGateBias(tensorInfo20, outputGateBiasData);
-
-    armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, armnn::DataType::Float32);
-    std::vector<float> recurrentToInputWeightsData(tensorInfo20x16.GetNumElements(), 0.0f);
-    armnn::ConstTensor recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData);
-
-    std::vector<float> recurrentToForgetWeightsData(tensorInfo20x16.GetNumElements(), 0.0f);
-    armnn::ConstTensor recurrentToForgetWeights(tensorInfo20x16, recurrentToForgetWeightsData);
-
-    std::vector<float> recurrentToCellWeightsData(tensorInfo20x16.GetNumElements(), 0.0f);
-    armnn::ConstTensor recurrentToCellWeights(tensorInfo20x16, recurrentToCellWeightsData);
-
-    std::vector<float> recurrentToOutputWeightsData(tensorInfo20x16.GetNumElements(), 0.0f);
-    armnn::ConstTensor recurrentToOutputWeights(tensorInfo20x16, recurrentToOutputWeightsData);
-
-    std::vector<float> cellToInputWeightsData(tensorInfo20.GetNumElements(), 0.0f);
-    armnn::ConstTensor cellToInputWeights(tensorInfo20, cellToInputWeightsData);
-
-    std::vector<float> cellToForgetWeightsData(tensorInfo20.GetNumElements(), 0.0f);
-    armnn::ConstTensor cellToForgetWeights(tensorInfo20, cellToForgetWeightsData);
-
-    std::vector<float> cellToOutputWeightsData(tensorInfo20.GetNumElements(), 0.0f);
-    armnn::ConstTensor cellToOutputWeights(tensorInfo20,  cellToOutputWeightsData);
-
-    armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, armnn::DataType::Float32);
-    std::vector<float> projectionWeightsData(tensorInfo16x20.GetNumElements(), 0.0f);
-    armnn::ConstTensor projectionWeights(tensorInfo16x20, projectionWeightsData);
-
-    armnn::TensorInfo tensorInfo16({outputSize}, armnn::DataType::Float32);
-    std::vector<float> projectionBiasData(outputSize, 0.0f);
-    armnn::ConstTensor projectionBias(tensorInfo16, projectionBiasData);
-
-    armnn::LstmInputParams params;
-    params.m_InputToForgetWeights     = &inputToForgetWeights;
-    params.m_InputToCellWeights       = &inputToCellWeights;
-    params.m_InputToOutputWeights     = &inputToOutputWeights;
-    params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
-    params.m_RecurrentToCellWeights   = &recurrentToCellWeights;
-    params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
-    params.m_ForgetGateBias           = &forgetGateBias;
-    params.m_CellBias                 = &cellBias;
-    params.m_OutputGateBias           = &outputGateBias;
-
-    // additional params because: descriptor.m_CifgEnabled = false
-    params.m_InputToInputWeights      = &inputToInputWeights;
-    params.m_RecurrentToInputWeights  = &recurrentToInputWeights;
-    params.m_CellToInputWeights       = &cellToInputWeights;
-    params.m_InputGateBias            = &inputGateBias;
-
-    // additional params because: descriptor.m_ProjectionEnabled = true
-    params.m_ProjectionWeights        = &projectionWeights;
-    params.m_ProjectionBias           = &projectionBias;
-
-    // additional params because: descriptor.m_PeepholeEnabled = true
-    params.m_CellToForgetWeights      = &cellToForgetWeights;
-    params.m_CellToOutputWeights      = &cellToOutputWeights;
-
-    const std::string layerName("lstm");
-    armnn::TensorInfo inputTensorInfo({ batchSize, inputSize }, armnn::DataType::Float32);
-    armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits}, armnn::DataType::Float32);
-    armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32);
-    armnn::TensorInfo lstmTensorInfoScratchBuff({ batchSize, numUnits * 4 }, armnn::DataType::Float32);
-
-    VerifyLstmLayer checker(
-            layerName,
-            {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
-            {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},
-            descriptor,
-            params);
-    deserializedNetwork->Accept(checker);
-}
-class VerifyQuantizedLstmLayer : public LayerVerifierBase
-{
-
-public:
-    VerifyQuantizedLstmLayer(const std::string& layerName,
-                             const std::vector<armnn::TensorInfo>& inputInfos,
-                             const std::vector<armnn::TensorInfo>& outputInfos,
-                             const armnn::QuantizedLstmInputParams& inputParams)
-        : LayerVerifierBase(layerName, inputInfos, outputInfos), m_InputParams(inputParams) {}
-
-    void VisitQuantizedLstmLayer(const armnn::IConnectableLayer* layer,
-                                 const armnn::QuantizedLstmInputParams& params,
-                                 const char* name)
-    {
-        VerifyNameAndConnections(layer, name);
-        VerifyInputParameters(params);
-    }
-
-protected:
-    void VerifyInputParameters(const armnn::QuantizedLstmInputParams& params)
-    {
-        VerifyConstTensors("m_InputToInputWeights",
-                           m_InputParams.m_InputToInputWeights, params.m_InputToInputWeights);
-        VerifyConstTensors("m_InputToForgetWeights",
-                           m_InputParams.m_InputToForgetWeights, params.m_InputToForgetWeights);
-        VerifyConstTensors("m_InputToCellWeights",
-                           m_InputParams.m_InputToCellWeights, params.m_InputToCellWeights);
-        VerifyConstTensors("m_InputToOutputWeights",
-                           m_InputParams.m_InputToOutputWeights, params.m_InputToOutputWeights);
-        VerifyConstTensors("m_RecurrentToInputWeights",
-                           m_InputParams.m_RecurrentToInputWeights, params.m_RecurrentToInputWeights);
-        VerifyConstTensors("m_RecurrentToForgetWeights",
-                           m_InputParams.m_RecurrentToForgetWeights, params.m_RecurrentToForgetWeights);
-        VerifyConstTensors("m_RecurrentToCellWeights",
-                           m_InputParams.m_RecurrentToCellWeights, params.m_RecurrentToCellWeights);
-        VerifyConstTensors("m_RecurrentToOutputWeights",
-                           m_InputParams.m_RecurrentToOutputWeights, params.m_RecurrentToOutputWeights);
-        VerifyConstTensors("m_InputGateBias",
-                           m_InputParams.m_InputGateBias, params.m_InputGateBias);
-        VerifyConstTensors("m_ForgetGateBias",
-                           m_InputParams.m_ForgetGateBias, params.m_ForgetGateBias);
-        VerifyConstTensors("m_CellBias",
-                           m_InputParams.m_CellBias, params.m_CellBias);
-        VerifyConstTensors("m_OutputGateBias",
-                           m_InputParams.m_OutputGateBias, params.m_OutputGateBias);
-    }
-
-private:
-    armnn::QuantizedLstmInputParams m_InputParams;
-};
-
-BOOST_AUTO_TEST_CASE(SerializeDeserializeQuantizedLstm)
-{
-    const uint32_t batchSize = 1;
-    const uint32_t inputSize = 2;
-    const uint32_t numUnits = 4;
-    const uint32_t outputSize = numUnits;
-
-    // Scale/Offset for input/output, cellState In/Out, weights, bias
-    float inputOutputScale = 0.0078125f;
-    int32_t inputOutputOffset = 128;
-
-    float cellStateScale = 0.00048828125f;
-    int32_t cellStateOffset = 0;
-
-    float weightsScale = 0.00408021f;
-    int32_t weightsOffset = 100;
-
-    float biasScale = 3.1876640625e-05f;
-    int32_t biasOffset = 0;
-
-    // The shape of weight data is {outputSize, inputSize} = {4, 2}
-    armnn::TensorShape inputToInputWeightsShape = {4, 2};
-    std::vector<uint8_t> inputToInputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};
-    armnn::TensorInfo inputToInputWeightsInfo(inputToInputWeightsShape,
-                                              armnn::DataType::QAsymmU8,
-                                              weightsScale,
-                                              weightsOffset);
-    armnn::ConstTensor inputToInputWeights(inputToInputWeightsInfo, inputToInputWeightsData);
-
-    armnn::TensorShape inputToForgetWeightsShape = {4, 2};
-    std::vector<uint8_t> inputToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};
-    armnn::TensorInfo inputToForgetWeightsInfo(inputToForgetWeightsShape,
-                                               armnn::DataType::QAsymmU8,
-                                               weightsScale,
-                                               weightsOffset);
-    armnn::ConstTensor inputToForgetWeights(inputToForgetWeightsInfo, inputToForgetWeightsData);
-
-    armnn::TensorShape inputToCellWeightsShape = {4, 2};
-    std::vector<uint8_t> inputToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};
-    armnn::TensorInfo inputToCellWeightsInfo(inputToCellWeightsShape,
-                                             armnn::DataType::QAsymmU8,
-                                             weightsScale,
-                                             weightsOffset);
-    armnn::ConstTensor inputToCellWeights(inputToCellWeightsInfo, inputToCellWeightsData);
-
-    armnn::TensorShape inputToOutputWeightsShape = {4, 2};
-    std::vector<uint8_t> inputToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};
-    armnn::TensorInfo inputToOutputWeightsInfo(inputToOutputWeightsShape,
-                                               armnn::DataType::QAsymmU8,
-                                               weightsScale,
-                                               weightsOffset);
-    armnn::ConstTensor inputToOutputWeights(inputToOutputWeightsInfo, inputToOutputWeightsData);
-
-    // The shape of recurrent weight data is {outputSize, outputSize} = {4, 4}
-    armnn::TensorShape recurrentToInputWeightsShape = {4, 4};
-    std::vector<uint8_t> recurrentToInputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
-    armnn::TensorInfo recurrentToInputWeightsInfo(recurrentToInputWeightsShape,
-                                                  armnn::DataType::QAsymmU8,
-                                                  weightsScale,
-                                                  weightsOffset);
-    armnn::ConstTensor recurrentToInputWeights(recurrentToInputWeightsInfo, recurrentToInputWeightsData);
-
-    armnn::TensorShape recurrentToForgetWeightsShape = {4, 4};
-    std::vector<uint8_t> recurrentToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
-    armnn::TensorInfo recurrentToForgetWeightsInfo(recurrentToForgetWeightsShape,
-                                                   armnn::DataType::QAsymmU8,
-                                                   weightsScale,
-                                                   weightsOffset);
-    armnn::ConstTensor recurrentToForgetWeights(recurrentToForgetWeightsInfo, recurrentToForgetWeightsData);
-
-    armnn::TensorShape recurrentToCellWeightsShape = {4, 4};
-    std::vector<uint8_t> recurrentToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
-    armnn::TensorInfo recurrentToCellWeightsInfo(recurrentToCellWeightsShape,
-                                                 armnn::DataType::QAsymmU8,
-                                                 weightsScale,
-                                                 weightsOffset);
-    armnn::ConstTensor recurrentToCellWeights(recurrentToCellWeightsInfo, recurrentToCellWeightsData);
-
-    armnn::TensorShape recurrentToOutputWeightsShape = {4, 4};
-    std::vector<uint8_t> recurrentToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
-    armnn::TensorInfo recurrentToOutputWeightsInfo(recurrentToOutputWeightsShape,
-                                                   armnn::DataType::QAsymmU8,
-                                                   weightsScale,
-                                                   weightsOffset);
-    armnn::ConstTensor recurrentToOutputWeights(recurrentToOutputWeightsInfo, recurrentToOutputWeightsData);
-
-    // The shape of bias data is {outputSize} = {4}
-    armnn::TensorShape inputGateBiasShape = {4};
-    std::vector<int32_t> inputGateBiasData = {1, 2, 3, 4};
-    armnn::TensorInfo inputGateBiasInfo(inputGateBiasShape,
-                                        armnn::DataType::Signed32,
-                                        biasScale,
-                                        biasOffset);
-    armnn::ConstTensor inputGateBias(inputGateBiasInfo, inputGateBiasData);
-
-    armnn::TensorShape forgetGateBiasShape = {4};
-    std::vector<int32_t> forgetGateBiasData = {1, 2, 3, 4};
-    armnn::TensorInfo forgetGateBiasInfo(forgetGateBiasShape,
-                                         armnn::DataType::Signed32,
-                                         biasScale,
-                                         biasOffset);
-    armnn::ConstTensor forgetGateBias(forgetGateBiasInfo, forgetGateBiasData);
-
-    armnn::TensorShape cellBiasShape = {4};
-    std::vector<int32_t> cellBiasData = {1, 2, 3, 4};
-    armnn::TensorInfo cellBiasInfo(cellBiasShape,
-                                   armnn::DataType::Signed32,
-                                   biasScale,
-                                   biasOffset);
-    armnn::ConstTensor cellBias(cellBiasInfo, cellBiasData);
-
-    armnn::TensorShape outputGateBiasShape = {4};
-    std::vector<int32_t> outputGateBiasData = {1, 2, 3, 4};
-    armnn::TensorInfo outputGateBiasInfo(outputGateBiasShape,
-                                         armnn::DataType::Signed32,
-                                         biasScale,
-                                         biasOffset);
-    armnn::ConstTensor outputGateBias(outputGateBiasInfo, outputGateBiasData);
-
-    armnn::QuantizedLstmInputParams params;
-    params.m_InputToInputWeights = &inputToInputWeights;
-    params.m_InputToForgetWeights = &inputToForgetWeights;
-    params.m_InputToCellWeights = &inputToCellWeights;
-    params.m_InputToOutputWeights = &inputToOutputWeights;
-    params.m_RecurrentToInputWeights = &recurrentToInputWeights;
-    params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
-    params.m_RecurrentToCellWeights = &recurrentToCellWeights;
-    params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
-    params.m_InputGateBias = &inputGateBias;
-    params.m_ForgetGateBias = &forgetGateBias;
-    params.m_CellBias = &cellBias;
-    params.m_OutputGateBias = &outputGateBias;
-
-    armnn::INetworkPtr network = armnn::INetwork::Create();
-    armnn::IConnectableLayer* const inputLayer = network->AddInputLayer(0);
-    armnn::IConnectableLayer* const cellStateIn = network->AddInputLayer(1);
-    armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(2);
-    const std::string layerName("QuantizedLstm");
-    armnn::IConnectableLayer* const quantizedLstmLayer = network->AddQuantizedLstmLayer(params, layerName.c_str());
-    armnn::IConnectableLayer* const cellStateOut = network->AddOutputLayer(0);
-    armnn::IConnectableLayer* const outputLayer = network->AddOutputLayer(1);
-
-    // Connect up
-    armnn::TensorInfo inputTensorInfo({ batchSize, inputSize },
-                                      armnn::DataType::QAsymmU8,
-                                      inputOutputScale,
-                                      inputOutputOffset);
-    armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits },
-                                          armnn::DataType::QSymmS16,
-                                          cellStateScale,
-                                          cellStateOffset);
-    armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize },
-                                            armnn::DataType::QAsymmU8,
-                                            inputOutputScale,
-                                            inputOutputOffset);
-
-    inputLayer->GetOutputSlot(0).Connect(quantizedLstmLayer->GetInputSlot(0));
-    inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
-
-    cellStateIn->GetOutputSlot(0).Connect(quantizedLstmLayer->GetInputSlot(1));
-    cellStateIn->GetOutputSlot(0).SetTensorInfo(cellStateTensorInfo);
-
-    outputStateIn->GetOutputSlot(0).Connect(quantizedLstmLayer->GetInputSlot(2));
-    outputStateIn->GetOutputSlot(0).SetTensorInfo(outputStateTensorInfo);
-
-    quantizedLstmLayer->GetOutputSlot(0).Connect(cellStateOut->GetInputSlot(0));
-    quantizedLstmLayer->GetOutputSlot(0).SetTensorInfo(cellStateTensorInfo);
-
-    quantizedLstmLayer->GetOutputSlot(1).Connect(outputLayer->GetInputSlot(0));
-    quantizedLstmLayer->GetOutputSlot(1).SetTensorInfo(outputStateTensorInfo);
-
-    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
-    BOOST_CHECK(deserializedNetwork);
-
-    VerifyQuantizedLstmLayer checker(layerName,
-                                     {inputTensorInfo, cellStateTensorInfo, outputStateTensorInfo},
-                                     {cellStateTensorInfo, outputStateTensorInfo},
-                                     params);
-
-    deserializedNetwork->Accept(checker);
-}
-
-class VerifyQLstmLayer : public LayerVerifierBaseWithDescriptor<armnn::QLstmDescriptor>
-{
-public:
-    VerifyQLstmLayer(const std::string& layerName,
-                     const std::vector<armnn::TensorInfo>& inputInfos,
-                     const std::vector<armnn::TensorInfo>& outputInfos,
-                     const armnn::QLstmDescriptor& descriptor,
-                     const armnn::LstmInputParams& inputParams)
-        : LayerVerifierBaseWithDescriptor<armnn::QLstmDescriptor>(layerName, inputInfos, outputInfos, descriptor)
-        , m_InputParams(inputParams) {}
-
-    void VisitQLstmLayer(const armnn::IConnectableLayer* layer,
-                         const armnn::QLstmDescriptor& descriptor,
-                         const armnn::LstmInputParams& params,
-                         const char* name)
-    {
-        VerifyNameAndConnections(layer, name);
-        VerifyDescriptor(descriptor);
-        VerifyInputParameters(params);
-    }
-
-protected:
-    void VerifyInputParameters(const armnn::LstmInputParams& params)
-    {
-        VerifyConstTensors(
-            "m_InputToInputWeights", m_InputParams.m_InputToInputWeights, params.m_InputToInputWeights);
-        VerifyConstTensors(
-            "m_InputToForgetWeights", m_InputParams.m_InputToForgetWeights, params.m_InputToForgetWeights);
-        VerifyConstTensors(
-            "m_InputToCellWeights", m_InputParams.m_InputToCellWeights, params.m_InputToCellWeights);
-        VerifyConstTensors(
-            "m_InputToOutputWeights", m_InputParams.m_InputToOutputWeights, params.m_InputToOutputWeights);
-        VerifyConstTensors(
-            "m_RecurrentToInputWeights", m_InputParams.m_RecurrentToInputWeights, params.m_RecurrentToInputWeights);
-        VerifyConstTensors(
-            "m_RecurrentToForgetWeights", m_InputParams.m_RecurrentToForgetWeights, params.m_RecurrentToForgetWeights);
-        VerifyConstTensors(
-            "m_RecurrentToCellWeights", m_InputParams.m_RecurrentToCellWeights, params.m_RecurrentToCellWeights);
-        VerifyConstTensors(
-            "m_RecurrentToOutputWeights", m_InputParams.m_RecurrentToOutputWeights, params.m_RecurrentToOutputWeights);
-        VerifyConstTensors(
-            "m_CellToInputWeights", m_InputParams.m_CellToInputWeights, params.m_CellToInputWeights);
-        VerifyConstTensors(
-            "m_CellToForgetWeights", m_InputParams.m_CellToForgetWeights, params.m_CellToForgetWeights);
-        VerifyConstTensors(
-            "m_CellToOutputWeights", m_InputParams.m_CellToOutputWeights, params.m_CellToOutputWeights);
-        VerifyConstTensors(
-            "m_InputGateBias", m_InputParams.m_InputGateBias, params.m_InputGateBias);
-        VerifyConstTensors(
-            "m_ForgetGateBias", m_InputParams.m_ForgetGateBias, params.m_ForgetGateBias);
-        VerifyConstTensors(
-            "m_CellBias", m_InputParams.m_CellBias, params.m_CellBias);
-        VerifyConstTensors(
-            "m_OutputGateBias", m_InputParams.m_OutputGateBias, params.m_OutputGateBias);
-        VerifyConstTensors(
-            "m_ProjectionWeights", m_InputParams.m_ProjectionWeights, params.m_ProjectionWeights);
-        VerifyConstTensors(
-            "m_ProjectionBias", m_InputParams.m_ProjectionBias, params.m_ProjectionBias);
-        VerifyConstTensors(
-            "m_InputLayerNormWeights", m_InputParams.m_InputLayerNormWeights, params.m_InputLayerNormWeights);
-        VerifyConstTensors(
-            "m_ForgetLayerNormWeights", m_InputParams.m_ForgetLayerNormWeights, params.m_ForgetLayerNormWeights);
-        VerifyConstTensors(
-            "m_CellLayerNormWeights", m_InputParams.m_CellLayerNormWeights, params.m_CellLayerNormWeights);
-        VerifyConstTensors(
-            "m_OutputLayerNormWeights", m_InputParams.m_OutputLayerNormWeights, params.m_OutputLayerNormWeights);
-    }
-
-private:
-    armnn::LstmInputParams m_InputParams;
-};
-
-BOOST_AUTO_TEST_CASE(SerializeDeserializeQLstmBasic)
-{
-    armnn::QLstmDescriptor descriptor;
-
-    descriptor.m_CifgEnabled       = true;
-    descriptor.m_ProjectionEnabled = false;
-    descriptor.m_PeepholeEnabled   = false;
-    descriptor.m_LayerNormEnabled  = false;
-
-    descriptor.m_CellClip       = 0.0f;
-    descriptor.m_ProjectionClip = 0.0f;
-
-    descriptor.m_InputIntermediateScale  = 0.00001f;
-    descriptor.m_ForgetIntermediateScale = 0.00001f;
-    descriptor.m_CellIntermediateScale   = 0.00001f;
-    descriptor.m_OutputIntermediateScale = 0.00001f;
-
-    descriptor.m_HiddenStateScale     = 0.07f;
-    descriptor.m_HiddenStateZeroPoint = 0;
-
-    const unsigned int numBatches = 2;
-    const unsigned int inputSize  = 5;
-    const unsigned int outputSize = 4;
-    const unsigned int numUnits   = 4;
-
-    // Scale/Offset quantization info
-    float inputScale    = 0.0078f;
-    int32_t inputOffset = 0;
-
-    float outputScale    = 0.0078f;
-    int32_t outputOffset = 0;
-
-    float cellStateScale    = 3.5002e-05f;
-    int32_t cellStateOffset = 0;
-
-    float weightsScale    = 0.007f;
-    int32_t weightsOffset = 0;
-
-    float biasScale    = 3.5002e-05f / 1024;
-    int32_t biasOffset = 0;
-
-    // Weights and bias tensor and quantization info
-    armnn::TensorInfo inputWeightsInfo({numUnits, inputSize},
-                                       armnn::DataType::QSymmS8,
-                                       weightsScale,
-                                       weightsOffset);
-
-    armnn::TensorInfo recurrentWeightsInfo({numUnits, outputSize},
-                                           armnn::DataType::QSymmS8,
-                                           weightsScale,
-                                           weightsOffset);
-
-    armnn::TensorInfo biasInfo({numUnits}, armnn::DataType::Signed32, biasScale, biasOffset);
-
-    std::vector<int8_t> inputToForgetWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
-    std::vector<int8_t> inputToCellWeightsData   = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
-    std::vector<int8_t> inputToOutputWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
-
-    armnn::ConstTensor inputToForgetWeights(inputWeightsInfo, inputToForgetWeightsData);
-    armnn::ConstTensor inputToCellWeights(inputWeightsInfo, inputToCellWeightsData);
-    armnn::ConstTensor inputToOutputWeights(inputWeightsInfo, inputToOutputWeightsData);
-
-    std::vector<int8_t> recurrentToForgetWeightsData =
-            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
-    std::vector<int8_t> recurrentToCellWeightsData   =
-            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
-    std::vector<int8_t> recurrentToOutputWeightsData =
-            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
-
-    armnn::ConstTensor recurrentToForgetWeights(recurrentWeightsInfo, recurrentToForgetWeightsData);
-    armnn::ConstTensor recurrentToCellWeights(recurrentWeightsInfo, recurrentToCellWeightsData);
-    armnn::ConstTensor recurrentToOutputWeights(recurrentWeightsInfo, recurrentToOutputWeightsData);
-
-    std::vector<int32_t> forgetGateBiasData(numUnits, 1);
-    std::vector<int32_t> cellBiasData(numUnits, 0);
-    std::vector<int32_t> outputGateBiasData(numUnits, 0);
-
-    armnn::ConstTensor forgetGateBias(biasInfo, forgetGateBiasData);
-    armnn::ConstTensor cellBias(biasInfo, cellBiasData);
-    armnn::ConstTensor outputGateBias(biasInfo, outputGateBiasData);
-
-    // Set up params
-    armnn::LstmInputParams params;
-    params.m_InputToForgetWeights = &inputToForgetWeights;
-    params.m_InputToCellWeights   = &inputToCellWeights;
-    params.m_InputToOutputWeights = &inputToOutputWeights;
-
-    params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
-    params.m_RecurrentToCellWeights   = &recurrentToCellWeights;
-    params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
-
-    params.m_ForgetGateBias = &forgetGateBias;
-    params.m_CellBias       = &cellBias;
-    params.m_OutputGateBias = &outputGateBias;
-
-    // Create network
-    armnn::INetworkPtr network = armnn::INetwork::Create();
-    const std::string layerName("qLstm");
-
-    armnn::IConnectableLayer* const input         = network->AddInputLayer(0);
-    armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(1);
-    armnn::IConnectableLayer* const cellStateIn   = network->AddInputLayer(2);
-
-    armnn::IConnectableLayer* const qLstmLayer = network->AddQLstmLayer(descriptor, params, layerName.c_str());
-
-    armnn::IConnectableLayer* const outputStateOut = network->AddOutputLayer(0);
-    armnn::IConnectableLayer* const cellStateOut   = network->AddOutputLayer(1);
-    armnn::IConnectableLayer* const outputLayer    = network->AddOutputLayer(2);
-
-    // Input/Output tensor info
-    armnn::TensorInfo inputInfo({numBatches , inputSize},
-                                armnn::DataType::QAsymmS8,
-                                inputScale,
-                                inputOffset);
-
-    armnn::TensorInfo cellStateInfo({numBatches , numUnits},
-                                    armnn::DataType::QSymmS16,
-                                    cellStateScale,
-                                    cellStateOffset);
-
-    armnn::TensorInfo outputStateInfo({numBatches , outputSize},
-                                      armnn::DataType::QAsymmS8,
-                                      outputScale,
-                                      outputOffset);
-
-    // Connect input/output slots
-    input->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(0));
-    input->GetOutputSlot(0).SetTensorInfo(inputInfo);
-
-    outputStateIn->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(1));
-    outputStateIn->GetOutputSlot(0).SetTensorInfo(cellStateInfo);
-
-    cellStateIn->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(2));
-    cellStateIn->GetOutputSlot(0).SetTensorInfo(outputStateInfo);
-
-    qLstmLayer->GetOutputSlot(0).Connect(outputStateOut->GetInputSlot(0));
-    qLstmLayer->GetOutputSlot(0).SetTensorInfo(outputStateInfo);
-
-    qLstmLayer->GetOutputSlot(1).Connect(cellStateOut->GetInputSlot(0));
-    qLstmLayer->GetOutputSlot(1).SetTensorInfo(cellStateInfo);
-
-    qLstmLayer->GetOutputSlot(2).Connect(outputLayer->GetInputSlot(0));
-    qLstmLayer->GetOutputSlot(2).SetTensorInfo(outputStateInfo);
-
-    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
-    BOOST_CHECK(deserializedNetwork);
-
-    VerifyQLstmLayer checker(layerName,
-                             {inputInfo, cellStateInfo, outputStateInfo},
-                             {outputStateInfo, cellStateInfo, outputStateInfo},
-                             descriptor,
-                             params);
-
-    deserializedNetwork->Accept(checker);
-}
-
-BOOST_AUTO_TEST_CASE(SerializeDeserializeQLstmCifgLayerNorm)
-{
-    armnn::QLstmDescriptor descriptor;
-
-    // CIFG params are used when CIFG is disabled
-    descriptor.m_CifgEnabled       = true;
-    descriptor.m_ProjectionEnabled = false;
-    descriptor.m_PeepholeEnabled   = false;
-    descriptor.m_LayerNormEnabled  = true;
-
-    descriptor.m_CellClip       = 0.0f;
-    descriptor.m_ProjectionClip = 0.0f;
-
-    descriptor.m_InputIntermediateScale  = 0.00001f;
-    descriptor.m_ForgetIntermediateScale = 0.00001f;
-    descriptor.m_CellIntermediateScale   = 0.00001f;
-    descriptor.m_OutputIntermediateScale = 0.00001f;
-
-    descriptor.m_HiddenStateScale     = 0.07f;
-    descriptor.m_HiddenStateZeroPoint = 0;
-
-    const unsigned int numBatches = 2;
-    const unsigned int inputSize  = 5;
-    const unsigned int outputSize = 4;
-    const unsigned int numUnits   = 4;
-
-    // Scale/Offset quantization info
-    float inputScale    = 0.0078f;
-    int32_t inputOffset = 0;
-
-    float outputScale    = 0.0078f;
-    int32_t outputOffset = 0;
-
-    float cellStateScale    = 3.5002e-05f;
-    int32_t cellStateOffset = 0;
-
-    float weightsScale    = 0.007f;
-    int32_t weightsOffset = 0;
-
-    float layerNormScale    = 3.5002e-05f;
-    int32_t layerNormOffset = 0;
-
-    float biasScale    = layerNormScale / 1024;
-    int32_t biasOffset = 0;
-
-    // Weights and bias tensor and quantization info
-    armnn::TensorInfo inputWeightsInfo({numUnits, inputSize},
-                                       armnn::DataType::QSymmS8,
-                                       weightsScale,
-                                       weightsOffset);
-
-    armnn::TensorInfo recurrentWeightsInfo({numUnits, outputSize},
-                                           armnn::DataType::QSymmS8,
-                                           weightsScale,
-                                           weightsOffset);
-
-    armnn::TensorInfo biasInfo({numUnits},
-                               armnn::DataType::Signed32,
-                               biasScale,
-                               biasOffset);
-
-    armnn::TensorInfo layerNormWeightsInfo({numUnits},
-                                           armnn::DataType::QSymmS16,
-                                           layerNormScale,
-                                           layerNormOffset);
-
-    // Mandatory params
-    std::vector<int8_t> inputToForgetWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
-    std::vector<int8_t> inputToCellWeightsData   = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
-    std::vector<int8_t> inputToOutputWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
-
-    armnn::ConstTensor inputToForgetWeights(inputWeightsInfo, inputToForgetWeightsData);
-    armnn::ConstTensor inputToCellWeights(inputWeightsInfo, inputToCellWeightsData);
-    armnn::ConstTensor inputToOutputWeights(inputWeightsInfo, inputToOutputWeightsData);
-
-    std::vector<int8_t> recurrentToForgetWeightsData =
-            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
-    std::vector<int8_t> recurrentToCellWeightsData   =
-            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
-    std::vector<int8_t> recurrentToOutputWeightsData =
-            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
-
-    armnn::ConstTensor recurrentToForgetWeights(recurrentWeightsInfo, recurrentToForgetWeightsData);
-    armnn::ConstTensor recurrentToCellWeights(recurrentWeightsInfo, recurrentToCellWeightsData);
-    armnn::ConstTensor recurrentToOutputWeights(recurrentWeightsInfo, recurrentToOutputWeightsData);
-
-    std::vector<int32_t> forgetGateBiasData(numUnits, 1);
-    std::vector<int32_t> cellBiasData(numUnits, 0);
-    std::vector<int32_t> outputGateBiasData(numUnits, 0);
-
-    armnn::ConstTensor forgetGateBias(biasInfo, forgetGateBiasData);
-    armnn::ConstTensor cellBias(biasInfo, cellBiasData);
-    armnn::ConstTensor outputGateBias(biasInfo, outputGateBiasData);
-
-    // Layer Norm
-    std::vector<int16_t> forgetLayerNormWeightsData =
-            GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
-    std::vector<int16_t> cellLayerNormWeightsData =
-            GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
-    std::vector<int16_t> outputLayerNormWeightsData =
-            GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
-
-    armnn::ConstTensor forgetLayerNormWeights(layerNormWeightsInfo, forgetLayerNormWeightsData);
-    armnn::ConstTensor cellLayerNormWeights(layerNormWeightsInfo, cellLayerNormWeightsData);
-    armnn::ConstTensor outputLayerNormWeights(layerNormWeightsInfo, outputLayerNormWeightsData);
-
-    // Set up params
-    armnn::LstmInputParams params;
-
-    // Mandatory params
-    params.m_InputToForgetWeights = &inputToForgetWeights;
-    params.m_InputToCellWeights   = &inputToCellWeights;
-    params.m_InputToOutputWeights = &inputToOutputWeights;
-
-    params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
-    params.m_RecurrentToCellWeights   = &recurrentToCellWeights;
-    params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
-
-    params.m_ForgetGateBias = &forgetGateBias;
-    params.m_CellBias       = &cellBias;
-    params.m_OutputGateBias = &outputGateBias;
-
-    // Layer Norm
-    params.m_ForgetLayerNormWeights = &forgetLayerNormWeights;
-    params.m_CellLayerNormWeights   = &cellLayerNormWeights;
-    params.m_OutputLayerNormWeights = &outputLayerNormWeights;
-
-    // Create network
-    armnn::INetworkPtr network = armnn::INetwork::Create();
-    const std::string layerName("qLstm");
-
-    armnn::IConnectableLayer* const input         = network->AddInputLayer(0);
-    armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(1);
-    armnn::IConnectableLayer* const cellStateIn   = network->AddInputLayer(2);
-
-    armnn::IConnectableLayer* const qLstmLayer = network->AddQLstmLayer(descriptor, params, layerName.c_str());
-
-    armnn::IConnectableLayer* const outputStateOut  = network->AddOutputLayer(0);
-    armnn::IConnectableLayer* const cellStateOut  = network->AddOutputLayer(1);
-    armnn::IConnectableLayer* const outputLayer  = network->AddOutputLayer(2);
-
-    // Input/Output tensor info
-    armnn::TensorInfo inputInfo({numBatches , inputSize},
-                                armnn::DataType::QAsymmS8,
-                                inputScale,
-                                inputOffset);
-
-    armnn::TensorInfo cellStateInfo({numBatches , numUnits},
-                                    armnn::DataType::QSymmS16,
-                                    cellStateScale,
-                                    cellStateOffset);
-
-    armnn::TensorInfo outputStateInfo({numBatches , outputSize},
-                                      armnn::DataType::QAsymmS8,
-                                      outputScale,
-                                      outputOffset);
-
-    // Connect input/output slots
-    input->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(0));
-    input->GetOutputSlot(0).SetTensorInfo(inputInfo);
-
-    outputStateIn->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(1));
-    outputStateIn->GetOutputSlot(0).SetTensorInfo(cellStateInfo);
-
-    cellStateIn->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(2));
-    cellStateIn->GetOutputSlot(0).SetTensorInfo(outputStateInfo);
-
-    qLstmLayer->GetOutputSlot(0).Connect(outputStateOut->GetInputSlot(0));
-    qLstmLayer->GetOutputSlot(0).SetTensorInfo(outputStateInfo);
-
-    qLstmLayer->GetOutputSlot(1).Connect(cellStateOut->GetInputSlot(0));
-    qLstmLayer->GetOutputSlot(1).SetTensorInfo(cellStateInfo);
-
-    qLstmLayer->GetOutputSlot(2).Connect(outputLayer->GetInputSlot(0));
-    qLstmLayer->GetOutputSlot(2).SetTensorInfo(outputStateInfo);
-
-    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
-    BOOST_CHECK(deserializedNetwork);
-
-    VerifyQLstmLayer checker(layerName,
-                             {inputInfo, cellStateInfo, outputStateInfo},
-                             {outputStateInfo, cellStateInfo, outputStateInfo},
-                             descriptor,
-                             params);
-
-    deserializedNetwork->Accept(checker);
-}
-
-BOOST_AUTO_TEST_CASE(SerializeDeserializeQLstmAdvanced)
-{
-    armnn::QLstmDescriptor descriptor;
-
-    descriptor.m_CifgEnabled       = false;
-    descriptor.m_ProjectionEnabled = true;
-    descriptor.m_PeepholeEnabled   = true;
-    descriptor.m_LayerNormEnabled  = true;
-
-    descriptor.m_CellClip       = 0.1f;
-    descriptor.m_ProjectionClip = 0.1f;
-
-    descriptor.m_InputIntermediateScale  = 0.00001f;
-    descriptor.m_ForgetIntermediateScale = 0.00001f;
-    descriptor.m_CellIntermediateScale   = 0.00001f;
-    descriptor.m_OutputIntermediateScale = 0.00001f;
-
-    descriptor.m_HiddenStateScale     = 0.07f;
-    descriptor.m_HiddenStateZeroPoint = 0;
-
-    const unsigned int numBatches = 2;
-    const unsigned int inputSize  = 5;
-    const unsigned int outputSize = 4;
-    const unsigned int numUnits   = 4;
-
-    // Scale/Offset quantization info
-    float inputScale    = 0.0078f;
-    int32_t inputOffset = 0;
-
-    float outputScale    = 0.0078f;
-    int32_t outputOffset = 0;
-
-    float cellStateScale    = 3.5002e-05f;
-    int32_t cellStateOffset = 0;
-
-    float weightsScale    = 0.007f;
-    int32_t weightsOffset = 0;
-
-    float layerNormScale    = 3.5002e-05f;
-    int32_t layerNormOffset = 0;
-
-    float biasScale    = layerNormScale / 1024;
-    int32_t biasOffset = 0;
-
-    // Weights and bias tensor and quantization info
-    armnn::TensorInfo inputWeightsInfo({numUnits, inputSize},
-                                       armnn::DataType::QSymmS8,
-                                       weightsScale,
-                                       weightsOffset);
-
-    armnn::TensorInfo recurrentWeightsInfo({numUnits, outputSize},
-                                           armnn::DataType::QSymmS8,
-                                           weightsScale,
-                                           weightsOffset);
-
-    armnn::TensorInfo biasInfo({numUnits},
-                               armnn::DataType::Signed32,
-                               biasScale,
-                               biasOffset);
-
-    armnn::TensorInfo peepholeWeightsInfo({numUnits},
-                                          armnn::DataType::QSymmS16,
-                                          weightsScale,
-                                          weightsOffset);
-
-    armnn::TensorInfo layerNormWeightsInfo({numUnits},
-                                           armnn::DataType::QSymmS16,
-                                           layerNormScale,
-                                           layerNormOffset);
-
-    armnn::TensorInfo projectionWeightsInfo({outputSize, numUnits},
-                                             armnn::DataType::QSymmS8,
-                                             weightsScale,
-                                             weightsOffset);
-
-    // Mandatory params
-    std::vector<int8_t> inputToForgetWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
-    std::vector<int8_t> inputToCellWeightsData   = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
-    std::vector<int8_t> inputToOutputWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
-
-    armnn::ConstTensor inputToForgetWeights(inputWeightsInfo, inputToForgetWeightsData);
-    armnn::ConstTensor inputToCellWeights(inputWeightsInfo, inputToCellWeightsData);
-    armnn::ConstTensor inputToOutputWeights(inputWeightsInfo, inputToOutputWeightsData);
-
-    std::vector<int8_t> recurrentToForgetWeightsData =
-            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
-    std::vector<int8_t> recurrentToCellWeightsData   =
-            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
-    std::vector<int8_t> recurrentToOutputWeightsData =
-            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
-
-    armnn::ConstTensor recurrentToForgetWeights(recurrentWeightsInfo, recurrentToForgetWeightsData);
-    armnn::ConstTensor recurrentToCellWeights(recurrentWeightsInfo, recurrentToCellWeightsData);
-    armnn::ConstTensor recurrentToOutputWeights(recurrentWeightsInfo, recurrentToOutputWeightsData);
-
-    std::vector<int32_t> forgetGateBiasData(numUnits, 1);
-    std::vector<int32_t> cellBiasData(numUnits, 0);
-    std::vector<int32_t> outputGateBiasData(numUnits, 0);
-
-    armnn::ConstTensor forgetGateBias(biasInfo, forgetGateBiasData);
-    armnn::ConstTensor cellBias(biasInfo, cellBiasData);
-    armnn::ConstTensor outputGateBias(biasInfo, outputGateBiasData);
-
-    // CIFG
-    std::vector<int8_t> inputToInputWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
-    std::vector<int8_t> recurrentToInputWeightsData =
-            GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
-    std::vector<int32_t> inputGateBiasData(numUnits, 1);
-
-    armnn::ConstTensor inputToInputWeights(inputWeightsInfo, inputToInputWeightsData);
-    armnn::ConstTensor recurrentToInputWeights(recurrentWeightsInfo, recurrentToInputWeightsData);
-    armnn::ConstTensor inputGateBias(biasInfo, inputGateBiasData);
-
-    // Peephole
-    std::vector<int16_t> cellToInputWeightsData  = GenerateRandomData<int16_t>(peepholeWeightsInfo.GetNumElements());
-    std::vector<int16_t> cellToForgetWeightsData = GenerateRandomData<int16_t>(peepholeWeightsInfo.GetNumElements());
-    std::vector<int16_t> cellToOutputWeightsData = GenerateRandomData<int16_t>(peepholeWeightsInfo.GetNumElements());
-
-    armnn::ConstTensor cellToInputWeights(peepholeWeightsInfo, cellToInputWeightsData);
-    armnn::ConstTensor cellToForgetWeights(peepholeWeightsInfo, cellToForgetWeightsData);
-    armnn::ConstTensor cellToOutputWeights(peepholeWeightsInfo, cellToOutputWeightsData);
-
-    // Projection
-    std::vector<int8_t> projectionWeightsData = GenerateRandomData<int8_t>(projectionWeightsInfo.GetNumElements());
-    std::vector<int32_t> projectionBiasData(outputSize, 1);
-
-    armnn::ConstTensor projectionWeights(projectionWeightsInfo, projectionWeightsData);
-    armnn::ConstTensor projectionBias(biasInfo, projectionBiasData);
-
-    // Layer Norm
-    std::vector<int16_t> inputLayerNormWeightsData =
-            GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
-    std::vector<int16_t> forgetLayerNormWeightsData =
-            GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
-    std::vector<int16_t> cellLayerNormWeightsData =
-            GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
-    std::vector<int16_t> outputLayerNormWeightsData =
-            GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
-
-    armnn::ConstTensor inputLayerNormWeights(layerNormWeightsInfo, inputLayerNormWeightsData);
-    armnn::ConstTensor forgetLayerNormWeights(layerNormWeightsInfo, forgetLayerNormWeightsData);
-    armnn::ConstTensor cellLayerNormWeights(layerNormWeightsInfo, cellLayerNormWeightsData);
-    armnn::ConstTensor outputLayerNormWeights(layerNormWeightsInfo, outputLayerNormWeightsData);
-
-    // Set up params
-    armnn::LstmInputParams params;
-
-    // Mandatory params
-    params.m_InputToForgetWeights = &inputToForgetWeights;
-    params.m_InputToCellWeights   = &inputToCellWeights;
-    params.m_InputToOutputWeights = &inputToOutputWeights;
-
-    params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
-    params.m_RecurrentToCellWeights   = &recurrentToCellWeights;
-    params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
-
-    params.m_ForgetGateBias = &forgetGateBias;
-    params.m_CellBias       = &cellBias;
-    params.m_OutputGateBias = &outputGateBias;
-
-    // CIFG
-    params.m_InputToInputWeights     = &inputToInputWeights;
-    params.m_RecurrentToInputWeights = &recurrentToInputWeights;
-    params.m_InputGateBias           = &inputGateBias;
-
-    // Peephole
-    params.m_CellToInputWeights  = &cellToInputWeights;
-    params.m_CellToForgetWeights = &cellToForgetWeights;
-    params.m_CellToOutputWeights = &cellToOutputWeights;
-
-    // Projection
-    params.m_ProjectionWeights = &projectionWeights;
-    params.m_ProjectionBias    = &projectionBias;
-
-    // Layer Norm
-    params.m_InputLayerNormWeights  = &inputLayerNormWeights;
-    params.m_ForgetLayerNormWeights = &forgetLayerNormWeights;
-    params.m_CellLayerNormWeights   = &cellLayerNormWeights;
-    params.m_OutputLayerNormWeights = &outputLayerNormWeights;
-
-    // Create network
-    armnn::INetworkPtr network = armnn::INetwork::Create();
-    const std::string layerName("qLstm");
-
-    armnn::IConnectableLayer* const input         = network->AddInputLayer(0);
-    armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(1);
-    armnn::IConnectableLayer* const cellStateIn   = network->AddInputLayer(2);
-
-    armnn::IConnectableLayer* const qLstmLayer = network->AddQLstmLayer(descriptor, params, layerName.c_str());
-
-    armnn::IConnectableLayer* const outputStateOut = network->AddOutputLayer(0);
-    armnn::IConnectableLayer* const cellStateOut   = network->AddOutputLayer(1);
-    armnn::IConnectableLayer* const outputLayer    = network->AddOutputLayer(2);
-
-    // Input/Output tensor info
-    armnn::TensorInfo inputInfo({numBatches , inputSize},
-                                armnn::DataType::QAsymmS8,
-                                inputScale,
-                                inputOffset);
-
-    armnn::TensorInfo cellStateInfo({numBatches , numUnits},
-                                    armnn::DataType::QSymmS16,
-                                    cellStateScale,
-                                    cellStateOffset);
-
-    armnn::TensorInfo outputStateInfo({numBatches , outputSize},
-                                      armnn::DataType::QAsymmS8,
-                                      outputScale,
-                                      outputOffset);
-
-    // Connect input/output slots
-    input->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(0));
-    input->GetOutputSlot(0).SetTensorInfo(inputInfo);
-
-    outputStateIn->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(1));
-    outputStateIn->GetOutputSlot(0).SetTensorInfo(cellStateInfo);
-
-    cellStateIn->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(2));
-    cellStateIn->GetOutputSlot(0).SetTensorInfo(outputStateInfo);
-
-    qLstmLayer->GetOutputSlot(0).Connect(outputStateOut->GetInputSlot(0));
-    qLstmLayer->GetOutputSlot(0).SetTensorInfo(outputStateInfo);
-
-    qLstmLayer->GetOutputSlot(1).Connect(cellStateOut->GetInputSlot(0));
-    qLstmLayer->GetOutputSlot(1).SetTensorInfo(cellStateInfo);
-
-    qLstmLayer->GetOutputSlot(2).Connect(outputLayer->GetInputSlot(0));
-    qLstmLayer->GetOutputSlot(2).SetTensorInfo(outputStateInfo);
-
-    armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
-    BOOST_CHECK(deserializedNetwork);
-
-    VerifyQLstmLayer checker(layerName,
-                             {inputInfo, cellStateInfo, outputStateInfo},
-                             {outputStateInfo, cellStateInfo, outputStateInfo},
-                             descriptor,
-                             params);
-
-    deserializedNetwork->Accept(checker);
+    deserializedNetwork->ExecuteStrategy(verifier);
 }
 
 BOOST_AUTO_TEST_SUITE_END()
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