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/LstmSerializationTests.cpp                | 2199 ++++++++++++++++++++
 1 file changed, 2199 insertions(+)
 create mode 100644 src/armnnSerializer/test/LstmSerializationTests.cpp

(limited to 'src/armnnSerializer/test/LstmSerializationTests.cpp')

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,
+        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+        0x00, 0x00, 0x00, 0x00, 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,
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+        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()
-- 
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