IVGCVSW-4777 Add QLstm serialization support

* Adds serialization/deserilization for QLstm.
* 3 unit tests: basic, layer norm and advanced.

Signed-off-by: James Conroy <james.conroy@arm.com>
Change-Id: I97d825e06b0d4a1257713cdd71ff06afa10d4380

7 files changed, 1008 insertions, 18 deletions

diff --git a/src/armnnDeserializer/Deserializer.cpp b/src/armnnDeserializer/Deserializer.cpp
index 42b0052b03..36beebc1cd 100644
--- a/src/armnnDeserializer/Deserializer.cpp
+++ b/src/armnnDeserializer/Deserializer.cpp
@@ -222,6 +222,7 @@ m_ParserFunctions(Layer_MAX+1, &Deserializer::ParseUnsupportedLayer)
     m_ParserFunctions[Layer_PermuteLayer]                = &Deserializer::ParsePermute;
     m_ParserFunctions[Layer_Pooling2dLayer]              = &Deserializer::ParsePooling2d;
     m_ParserFunctions[Layer_PreluLayer]                  = &Deserializer::ParsePrelu;
+    m_ParserFunctions[Layer_QLstmLayer]                  = &Deserializer::ParseQLstm;
     m_ParserFunctions[Layer_QuantizeLayer]               = &Deserializer::ParseQuantize;
     m_ParserFunctions[Layer_QuantizedLstmLayer]          = &Deserializer::ParseQuantizedLstm;
     m_ParserFunctions[Layer_ReshapeLayer]                = &Deserializer::ParseReshape;
@@ -322,6 +323,8 @@ Deserializer::LayerBaseRawPtr Deserializer::GetBaseLayer(const GraphPtr& graphPt
             return graphPtr->layers()->Get(layerIndex)->layer_as_Pooling2dLayer()->base();
         case Layer::Layer_PreluLayer:
             return graphPtr->layers()->Get(layerIndex)->layer_as_PreluLayer()->base();
+        case Layer::Layer_QLstmLayer:
+            return graphPtr->layers()->Get(layerIndex)->layer_as_QLstmLayer()->base();
         case Layer::Layer_QuantizeLayer:
             return graphPtr->layers()->Get(layerIndex)->layer_as_QuantizeLayer()->base();
         case Layer::Layer_QuantizedLstmLayer:
@@ -2610,6 +2613,155 @@ void Deserializer::ParseLstm(GraphPtr graph, unsigned int layerIndex)
     RegisterOutputSlots(graph, layerIndex, layer);
 }
 
+armnn::QLstmDescriptor Deserializer::GetQLstmDescriptor(Deserializer::QLstmDescriptorPtr qLstmDescriptor)
+{
+    armnn::QLstmDescriptor desc;
+
+    desc.m_CifgEnabled       = qLstmDescriptor->cifgEnabled();
+    desc.m_PeepholeEnabled   = qLstmDescriptor->peepholeEnabled();
+    desc.m_ProjectionEnabled = qLstmDescriptor->projectionEnabled();
+    desc.m_LayerNormEnabled  = qLstmDescriptor->layerNormEnabled();
+
+    desc.m_CellClip       = qLstmDescriptor->cellClip();
+    desc.m_ProjectionClip = qLstmDescriptor->projectionClip();
+
+    desc.m_InputIntermediateScale  = qLstmDescriptor->inputIntermediateScale();
+    desc.m_ForgetIntermediateScale = qLstmDescriptor->forgetIntermediateScale();
+    desc.m_CellIntermediateScale   = qLstmDescriptor->cellIntermediateScale();
+    desc.m_OutputIntermediateScale = qLstmDescriptor->outputIntermediateScale();
+
+    desc.m_HiddenStateScale     = qLstmDescriptor->hiddenStateScale();
+    desc.m_HiddenStateZeroPoint = qLstmDescriptor->hiddenStateZeroPoint();
+
+    return desc;
+}
+
+void Deserializer::ParseQLstm(GraphPtr graph, unsigned int layerIndex)
+{
+    CHECK_LAYERS(graph, 0, layerIndex);
+
+    auto inputs = GetInputs(graph, layerIndex);
+    CHECK_VALID_SIZE(inputs.size(), 3);
+
+    auto outputs = GetOutputs(graph, layerIndex);
+    CHECK_VALID_SIZE(outputs.size(), 3);
+
+    auto flatBufferLayer       = graph->layers()->Get(layerIndex)->layer_as_QLstmLayer();
+    auto layerName             = GetLayerName(graph, layerIndex);
+    auto flatBufferDescriptor  = flatBufferLayer->descriptor();
+    auto flatBufferInputParams = flatBufferLayer->inputParams();
+
+    auto qLstmDescriptor = GetQLstmDescriptor(flatBufferDescriptor);
+    armnn::LstmInputParams qLstmInputParams;
+
+    // Mandatory params
+    armnn::ConstTensor inputToForgetWeights     = ToConstTensor(flatBufferInputParams->inputToForgetWeights());
+    armnn::ConstTensor inputToCellWeights       = ToConstTensor(flatBufferInputParams->inputToCellWeights());
+    armnn::ConstTensor inputToOutputWeights     = ToConstTensor(flatBufferInputParams->inputToOutputWeights());
+    armnn::ConstTensor recurrentToForgetWeights = ToConstTensor(flatBufferInputParams->recurrentToForgetWeights());
+    armnn::ConstTensor recurrentToCellWeights   = ToConstTensor(flatBufferInputParams->recurrentToCellWeights());
+    armnn::ConstTensor recurrentToOutputWeights = ToConstTensor(flatBufferInputParams->recurrentToOutputWeights());
+    armnn::ConstTensor forgetGateBias           = ToConstTensor(flatBufferInputParams->forgetGateBias());
+    armnn::ConstTensor cellBias                 = ToConstTensor(flatBufferInputParams->cellBias());
+    armnn::ConstTensor outputGateBias           = ToConstTensor(flatBufferInputParams->outputGateBias());
+
+    qLstmInputParams.m_InputToForgetWeights = &inputToForgetWeights;
+    qLstmInputParams.m_InputToCellWeights = &inputToCellWeights;
+    qLstmInputParams.m_InputToOutputWeights = &inputToOutputWeights;
+    qLstmInputParams.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
+    qLstmInputParams.m_RecurrentToCellWeights = &recurrentToCellWeights;
+    qLstmInputParams.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
+    qLstmInputParams.m_ForgetGateBias = &forgetGateBias;
+    qLstmInputParams.m_CellBias = &cellBias;
+    qLstmInputParams.m_OutputGateBias = &outputGateBias;
+
+    // Optional CIFG params
+    armnn::ConstTensor inputToInputWeights;
+    armnn::ConstTensor recurrentToInputWeights;
+    armnn::ConstTensor inputGateBias;
+
+    if (!qLstmDescriptor.m_CifgEnabled)
+    {
+        inputToInputWeights     = ToConstTensor(flatBufferInputParams->inputToInputWeights());
+        recurrentToInputWeights = ToConstTensor(flatBufferInputParams->recurrentToInputWeights());
+        inputGateBias           = ToConstTensor(flatBufferInputParams->inputGateBias());
+
+        qLstmInputParams.m_InputToInputWeights     = &inputToInputWeights;
+        qLstmInputParams.m_RecurrentToInputWeights = &recurrentToInputWeights;
+        qLstmInputParams.m_InputGateBias           = &inputGateBias;
+    }
+
+    // Optional projection params
+    armnn::ConstTensor projectionWeights;
+    armnn::ConstTensor projectionBias;
+
+    if (qLstmDescriptor.m_ProjectionEnabled)
+    {
+        projectionWeights = ToConstTensor(flatBufferInputParams->projectionWeights());
+        projectionBias = ToConstTensor(flatBufferInputParams->projectionBias());
+
+        qLstmInputParams.m_ProjectionWeights = &projectionWeights;
+        qLstmInputParams.m_ProjectionBias = &projectionBias;
+    }
+
+    // Optional peephole params
+    armnn::ConstTensor cellToInputWeights;
+    armnn::ConstTensor cellToForgetWeights;
+    armnn::ConstTensor cellToOutputWeights;
+
+    if (qLstmDescriptor.m_PeepholeEnabled)
+    {
+        if (!qLstmDescriptor.m_CifgEnabled)
+        {
+            cellToInputWeights = ToConstTensor(flatBufferInputParams->cellToInputWeights());
+            qLstmInputParams.m_CellToInputWeights = &cellToInputWeights;
+        }
+
+        cellToForgetWeights = ToConstTensor(flatBufferInputParams->cellToForgetWeights());
+        cellToOutputWeights = ToConstTensor(flatBufferInputParams->cellToOutputWeights());
+
+        qLstmInputParams.m_CellToForgetWeights = &cellToForgetWeights;
+        qLstmInputParams.m_CellToOutputWeights = &cellToOutputWeights;
+    }
+
+    // Optional layer norm params
+    armnn::ConstTensor inputLayerNormWeights;
+    armnn::ConstTensor forgetLayerNormWeights;
+    armnn::ConstTensor cellLayerNormWeights;
+    armnn::ConstTensor outputLayerNormWeights;
+
+    if (qLstmDescriptor.m_LayerNormEnabled)
+    {
+        if (!qLstmDescriptor.m_CifgEnabled)
+        {
+            inputLayerNormWeights = ToConstTensor(flatBufferInputParams->inputLayerNormWeights());
+            qLstmInputParams.m_InputLayerNormWeights = &inputLayerNormWeights;
+        }
+
+        forgetLayerNormWeights = ToConstTensor(flatBufferInputParams->forgetLayerNormWeights());
+        cellLayerNormWeights   = ToConstTensor(flatBufferInputParams->cellLayerNormWeights());
+        outputLayerNormWeights = ToConstTensor(flatBufferInputParams->outputLayerNormWeights());
+
+        qLstmInputParams.m_ForgetLayerNormWeights = &forgetLayerNormWeights;
+        qLstmInputParams.m_CellLayerNormWeights   = &cellLayerNormWeights;
+        qLstmInputParams.m_OutputLayerNormWeights = &outputLayerNormWeights;
+    }
+
+    IConnectableLayer* layer = m_Network->AddQLstmLayer(qLstmDescriptor, qLstmInputParams, layerName.c_str());
+
+    armnn::TensorInfo outputStateOutInfo = ToTensorInfo(outputs[0]);
+    layer->GetOutputSlot(0).SetTensorInfo(outputStateOutInfo);
+
+    armnn::TensorInfo cellStateOutInfo = ToTensorInfo(outputs[1]);
+    layer->GetOutputSlot(1).SetTensorInfo(cellStateOutInfo);
+
+    armnn::TensorInfo outputInfo = ToTensorInfo(outputs[2]);
+    layer->GetOutputSlot(2).SetTensorInfo(outputInfo);
+
+    RegisterInputSlots(graph, layerIndex, layer);
+    RegisterOutputSlots(graph, layerIndex, layer);
+}
+
 void Deserializer::ParseQuantizedLstm(GraphPtr graph, unsigned int layerIndex)
 {
     CHECK_LAYERS(graph, 0, layerIndex);
diff --git a/src/armnnDeserializer/Deserializer.hpp b/src/armnnDeserializer/Deserializer.hpp
index f7e47cc8c2..d6ceced7c6 100644
--- a/src/armnnDeserializer/Deserializer.hpp
+++ b/src/armnnDeserializer/Deserializer.hpp
@@ -24,6 +24,7 @@ public:
     using NormalizationDescriptorPtr = const armnnSerializer::NormalizationDescriptor *;
     using LstmDescriptorPtr = const armnnSerializer::LstmDescriptor *;
     using LstmInputParamsPtr = const armnnSerializer::LstmInputParams *;
+    using QLstmDescriptorPtr = const armnnSerializer::QLstmDescriptor *;
     using QunatizedLstmInputParamsPtr = const armnnSerializer::QuantizedLstmInputParams *;
     using TensorRawPtrVector = std::vector<TensorRawPtr>;
     using LayerRawPtr = const armnnSerializer::LayerBase *;
@@ -62,6 +63,7 @@ public:
     static armnn::LstmDescriptor GetLstmDescriptor(LstmDescriptorPtr lstmDescriptor);
     static armnn::LstmInputParams GetLstmInputParams(LstmDescriptorPtr lstmDescriptor,
                                                      LstmInputParamsPtr lstmInputParams);
+    static armnn::QLstmDescriptor GetQLstmDescriptor(QLstmDescriptorPtr qLstmDescriptorPtr);
     static armnn::TensorInfo OutputShapeOfReshape(const armnn::TensorInfo & inputTensorInfo,
                                                   const std::vector<uint32_t> & targetDimsIn);
 
@@ -113,6 +115,7 @@ private:
     void ParsePermute(GraphPtr graph, unsigned int layerIndex);
     void ParsePooling2d(GraphPtr graph, unsigned int layerIndex);
     void ParsePrelu(GraphPtr graph, unsigned int layerIndex);
+    void ParseQLstm(GraphPtr graph, unsigned int layerIndex);
     void ParseQuantize(GraphPtr graph, unsigned int layerIndex);
     void ParseReshape(GraphPtr graph, unsigned int layerIndex);
     void ParseResize(GraphPtr graph, unsigned int layerIndex);
diff --git a/src/armnnDeserializer/DeserializerSupport.md b/src/armnnDeserializer/DeserializerSupport.md
index 491cd1de42..c47810c11e 100644
--- a/src/armnnDeserializer/DeserializerSupport.md
+++ b/src/armnnDeserializer/DeserializerSupport.md
@@ -42,6 +42,7 @@ The Arm NN SDK Deserialize parser currently supports the following layers:
 * Pooling2d
 * Prelu
 * Quantize
+* QLstm
 * QuantizedLstm
 * Reshape
 * Resize
diff --git a/src/armnnSerializer/ArmnnSchema.fbs b/src/armnnSerializer/ArmnnSchema.fbs
index ff79f6cffe..6e5ee3f3d3 100644
--- a/src/armnnSerializer/ArmnnSchema.fbs
+++ b/src/armnnSerializer/ArmnnSchema.fbs
@@ -155,7 +155,8 @@ enum LayerType : uint {
     Comparison = 52,
     StandIn = 53,
     ElementwiseUnary = 54,
-    Transpose = 55
+    Transpose = 55,
+    QLstm = 56
 }
 
 // Base layer table to be used as part of other layers
@@ -666,37 +667,96 @@ table LstmInputParams {
     outputLayerNormWeights:ConstTensor;
 }
 
-table QuantizedLstmInputParams {
-    inputToInputWeights:ConstTensor;
+table LstmDescriptor {
+    activationFunc:uint;
+    clippingThresCell:float;
+    clippingThresProj:float;
+    cifgEnabled:bool = true;
+    peepholeEnabled:bool = false;
+    projectionEnabled:bool = false;
+    layerNormEnabled:bool = false;
+}
+
+table LstmLayer {
+    base:LayerBase;
+    descriptor:LstmDescriptor;
+    inputParams:LstmInputParams;
+}
+
+table QLstmInputParams {
+    // Mandatory
     inputToForgetWeights:ConstTensor;
     inputToCellWeights:ConstTensor;
     inputToOutputWeights:ConstTensor;
 
-    recurrentToInputWeights:ConstTensor;
     recurrentToForgetWeights:ConstTensor;
     recurrentToCellWeights:ConstTensor;
     recurrentToOutputWeights:ConstTensor;
 
-    inputGateBias:ConstTensor;
     forgetGateBias:ConstTensor;
     cellBias:ConstTensor;
     outputGateBias:ConstTensor;
+
+    // CIFG
+    inputToInputWeights:ConstTensor;
+    recurrentToInputWeights:ConstTensor;
+    inputGateBias:ConstTensor;
+
+    // Projection
+    projectionWeights:ConstTensor;
+    projectionBias:ConstTensor;
+
+    // Peephole
+    cellToInputWeights:ConstTensor;
+    cellToForgetWeights:ConstTensor;
+    cellToOutputWeights:ConstTensor;
+
+    // Layer norm
+    inputLayerNormWeights:ConstTensor;
+    forgetLayerNormWeights:ConstTensor;
+    cellLayerNormWeights:ConstTensor;
+    outputLayerNormWeights:ConstTensor;
 }
 
-table LstmDescriptor {
-    activationFunc:uint;
-    clippingThresCell:float;
-    clippingThresProj:float;
-    cifgEnabled:bool = true;
-    peepholeEnabled:bool = false;
+table QLstmDescriptor {
+    cifgEnabled:bool       = true;
+    peepholeEnabled:bool   = false;
     projectionEnabled:bool = false;
-    layerNormEnabled:bool = false;
+    layerNormEnabled:bool  = false;
+
+    cellClip:float;
+    projectionClip:float;
+
+    inputIntermediateScale:float;
+    forgetIntermediateScale:float;
+    cellIntermediateScale:float;
+    outputIntermediateScale:float;
+
+    hiddenStateZeroPoint:int;
+    hiddenStateScale:float;
 }
 
-table LstmLayer {
+table QLstmLayer {
     base:LayerBase;
-    descriptor:LstmDescriptor;
-    inputParams:LstmInputParams;
+    descriptor:QLstmDescriptor;
+    inputParams:QLstmInputParams;
+}
+
+table QuantizedLstmInputParams {
+    inputToInputWeights:ConstTensor;
+    inputToForgetWeights:ConstTensor;
+    inputToCellWeights:ConstTensor;
+    inputToOutputWeights:ConstTensor;
+
+    recurrentToInputWeights:ConstTensor;
+    recurrentToForgetWeights:ConstTensor;
+    recurrentToCellWeights:ConstTensor;
+    recurrentToOutputWeights:ConstTensor;
+
+    inputGateBias:ConstTensor;
+    forgetGateBias:ConstTensor;
+    cellBias:ConstTensor;
+    outputGateBias:ConstTensor;
 }
 
 table QuantizedLstmLayer {
@@ -836,7 +896,8 @@ union Layer {
     ComparisonLayer,
     StandInLayer,
     ElementwiseUnaryLayer,
-    TransposeLayer
+    TransposeLayer,
+    QLstmLayer
 }
 
 table AnyLayer {
diff --git a/src/armnnSerializer/Serializer.cpp b/src/armnnSerializer/Serializer.cpp
index 355673697f..c4d3cfb5dd 100644
--- a/src/armnnSerializer/Serializer.cpp
+++ b/src/armnnSerializer/Serializer.cpp
@@ -1335,9 +1335,124 @@ void SerializerVisitor::VisitQLstmLayer(const armnn::IConnectableLayer* layer,
                                         const armnn::LstmInputParams& params,
                                         const char* name)
 {
-    IgnoreUnused(layer, descriptor, params, name);
+    IgnoreUnused(name);
+
+    auto fbQLstmBaseLayer = CreateLayerBase(layer, serializer::LayerType::LayerType_QLstm);
+
+    auto fbQLstmDescriptor = serializer::CreateQLstmDescriptor(
+            m_flatBufferBuilder,
+            descriptor.m_CifgEnabled,
+            descriptor.m_PeepholeEnabled,
+            descriptor.m_ProjectionEnabled,
+            descriptor.m_LayerNormEnabled,
+            descriptor.m_CellClip,
+            descriptor.m_ProjectionClip,
+            descriptor.m_InputIntermediateScale,
+            descriptor.m_ForgetIntermediateScale,
+            descriptor.m_CellIntermediateScale,
+            descriptor.m_OutputIntermediateScale,
+            descriptor.m_HiddenStateZeroPoint,
+            descriptor.m_HiddenStateScale
+            );
+
+    // Mandatory params
+    auto inputToForgetWeights = CreateConstTensorInfo(*params.m_InputToForgetWeights);
+    auto inputToCellWeights = CreateConstTensorInfo(*params.m_InputToCellWeights);
+    auto inputToOutputWeights = CreateConstTensorInfo(*params.m_InputToOutputWeights);
+    auto recurrentToForgetWeights = CreateConstTensorInfo(*params.m_RecurrentToForgetWeights);
+    auto recurrentToCellWeights = CreateConstTensorInfo(*params.m_RecurrentToCellWeights);
+    auto recurrentToOutputWeights = CreateConstTensorInfo(*params.m_RecurrentToOutputWeights);
+    auto forgetGateBias = CreateConstTensorInfo(*params.m_ForgetGateBias);
+    auto cellBias = CreateConstTensorInfo(*params.m_CellBias);
+    auto outputGateBias = CreateConstTensorInfo(*params.m_OutputGateBias);
+
+    // CIFG
+    flatbuffers::Offset<serializer::ConstTensor> inputToInputWeights;
+    flatbuffers::Offset<serializer::ConstTensor> recurrentToInputWeights;
+    flatbuffers::Offset<serializer::ConstTensor> inputGateBias;
+
+    if (!descriptor.m_CifgEnabled)
+    {
+        inputToInputWeights = CreateConstTensorInfo(*params.m_InputToInputWeights);
+        recurrentToInputWeights = CreateConstTensorInfo(*params.m_RecurrentToInputWeights);
+        inputGateBias = CreateConstTensorInfo(*params.m_InputGateBias);
+    }
+
+    // Projectiom
+    flatbuffers::Offset<serializer::ConstTensor> projectionWeights;
+    flatbuffers::Offset<serializer::ConstTensor> projectionBias;
+
+    if (descriptor.m_ProjectionEnabled)
+    {
+        projectionWeights = CreateConstTensorInfo(*params.m_ProjectionWeights);
+        projectionBias = CreateConstTensorInfo(*params.m_ProjectionBias);
+    }
+
+    // Peephole
+    flatbuffers::Offset<serializer::ConstTensor> cellToInputWeights;
+    flatbuffers::Offset<serializer::ConstTensor> cellToForgetWeights;
+    flatbuffers::Offset<serializer::ConstTensor> cellToOutputWeights;
+
+    if (descriptor.m_PeepholeEnabled)
+    {
+        if (!descriptor.m_CifgEnabled)
+        {
+            cellToInputWeights  = CreateConstTensorInfo(*params.m_CellToInputWeights);
+        }
+
+        cellToForgetWeights = CreateConstTensorInfo(*params.m_CellToForgetWeights);
+        cellToOutputWeights = CreateConstTensorInfo(*params.m_CellToOutputWeights);
+    }
+
+    // Layer norm
+    flatbuffers::Offset<serializer::ConstTensor> inputLayerNormWeights;
+    flatbuffers::Offset<serializer::ConstTensor> forgetLayerNormWeights;
+    flatbuffers::Offset<serializer::ConstTensor> cellLayerNormWeights;
+    flatbuffers::Offset<serializer::ConstTensor> outputLayerNormWeights;
+
+    if (descriptor.m_LayerNormEnabled)
+    {
+        if (!descriptor.m_CifgEnabled)
+        {
+            inputLayerNormWeights = CreateConstTensorInfo((*params.m_InputLayerNormWeights));
+        }
+
+        forgetLayerNormWeights = CreateConstTensorInfo(*params.m_ForgetLayerNormWeights);
+        cellLayerNormWeights   = CreateConstTensorInfo(*params.m_CellLayerNormWeights);
+        outputLayerNormWeights = CreateConstTensorInfo(*params.m_OutputLayerNormWeights);
+    }
+
+    auto fbQLstmParams = serializer::CreateQLstmInputParams(
+            m_flatBufferBuilder,
+            inputToForgetWeights,
+            inputToCellWeights,
+            inputToOutputWeights,
+            recurrentToForgetWeights,
+            recurrentToCellWeights,
+            recurrentToOutputWeights,
+            forgetGateBias,
+            cellBias,
+            outputGateBias,
+            inputToInputWeights,
+            recurrentToInputWeights,
+            inputGateBias,
+            projectionWeights,
+            projectionBias,
+            cellToInputWeights,
+            cellToForgetWeights,
+            cellToOutputWeights,
+            inputLayerNormWeights,
+            forgetLayerNormWeights,
+            cellLayerNormWeights,
+            outputLayerNormWeights);
+
+    auto fbQLstmLayer = serializer::CreateQLstmLayer(
+            m_flatBufferBuilder,
+            fbQLstmBaseLayer,
+            fbQLstmDescriptor,
+            fbQLstmParams);
 
-    throw UnimplementedException("SerializerVisitor::VisitQLstmLayer not yet implemented");
+    CreateAnyLayer(fbQLstmLayer.o, serializer::Layer::Layer_QLstmLayer);
 }
 
 void SerializerVisitor::VisitQuantizedLstmLayer(const armnn::IConnectableLayer* layer,
diff --git a/src/armnnSerializer/SerializerSupport.md b/src/armnnSerializer/SerializerSupport.md
index 79b551f03a..8ba164c91e 100644
--- a/src/armnnSerializer/SerializerSupport.md
+++ b/src/armnnSerializer/SerializerSupport.md
@@ -40,6 +40,7 @@ The Arm NN SDK Serializer currently supports the following layers:
 * Permute
 * Pooling2d
 * Prelu
+* QLstm
 * Quantize
 * QuantizedLstm
 * Reshape
diff --git a/src/armnnSerializer/test/SerializerTests.cpp b/src/armnnSerializer/test/SerializerTests.cpp
index db89430439..76ac5a4de2 100644
--- a/src/armnnSerializer/test/SerializerTests.cpp
+++ b/src/armnnSerializer/test/SerializerTests.cpp
@@ -4326,4 +4326,661 @@ BOOST_AUTO_TEST_CASE(SerializeDeserializeQuantizedLstm)
     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);
+}
+
 BOOST_AUTO_TEST_SUITE_END()