IVGCVSW-1622 LSTM Neon support

Change-Id: I107d732760dd8662e2ee9a5e7ebb5baf61c45246

4 files changed, 456 insertions, 8 deletions

diff --git a/src/backends/neon/workloads/NeonLstmFloatWorkload.cpp b/src/backends/neon/workloads/NeonLstmFloatWorkload.cpp
index 8b2b58d9b1..5899f13780 100644
--- a/src/backends/neon/workloads/NeonLstmFloatWorkload.cpp
+++ b/src/backends/neon/workloads/NeonLstmFloatWorkload.cpp
@@ -4,19 +4,409 @@
 //
 
 #include "NeonLstmFloatWorkload.hpp"
+#include "NeonWorkloadUtils.hpp"
+
+#include "backends/CpuTensorHandle.hpp"
+#include "backends/aclCommon/ArmComputeTensorUtils.hpp"
+#include "backends/neon/NeonTensorHandle.hpp"
 
 namespace armnn
 {
-NeonLstmFloatWorkload::NeonLstmFloatWorkload(const LstmQueueDescriptor& descriptor,
-                                             const WorkloadInfo& info)
+using namespace armcomputetensorutils;
+
+NeonLstmFloatWorkload::NeonLstmFloatWorkload(const LstmQueueDescriptor &descriptor, const WorkloadInfo &info)
         : FloatWorkload<LstmQueueDescriptor>(descriptor, info)
 {
-    m_Data.ValidateInputsOutputs("NeonLstmFloatWorkload", 1, 1);
+    arm_compute::LSTMParams<arm_compute::ITensor> lstm_param;
+
+    // Basic parameters
+    m_InputToForgetWeightsTensor = std::make_unique<arm_compute::Tensor>();
+    BuildArmComputeTensor(*m_InputToForgetWeightsTensor, m_Data.m_InputToForgetWeights->GetTensorInfo());
+
+    m_InputToCellWeightsTensor = std::make_unique<arm_compute::Tensor>();
+    BuildArmComputeTensor(*m_InputToCellWeightsTensor, m_Data.m_InputToCellWeights->GetTensorInfo());
+
+    m_InputToOutputWeightsTensor = std::make_unique<arm_compute::Tensor>();
+    BuildArmComputeTensor(*m_InputToOutputWeightsTensor, m_Data.m_InputToOutputWeights->GetTensorInfo());
+
+    m_RecurrentToForgetWeightsTensor = std::make_unique<arm_compute::Tensor>();
+    BuildArmComputeTensor(*m_RecurrentToForgetWeightsTensor, m_Data.m_RecurrentToForgetWeights->GetTensorInfo());
+
+    m_RecurrentToCellWeightsTensor = std::make_unique<arm_compute::Tensor>();
+    BuildArmComputeTensor(*m_RecurrentToCellWeightsTensor, m_Data.m_RecurrentToCellWeights->GetTensorInfo());
+
+    m_RecurrentToOutputWeightsTensor = std::make_unique<arm_compute::Tensor>();
+    BuildArmComputeTensor(*m_RecurrentToOutputWeightsTensor, m_Data.m_RecurrentToOutputWeights->GetTensorInfo());
+
+    m_ForgetGateBiasTensor = std::make_unique<arm_compute::Tensor>();
+    BuildArmComputeTensor(*m_ForgetGateBiasTensor, m_Data.m_ForgetGateBias->GetTensorInfo());
+
+    m_CellBiasTensor = std::make_unique<arm_compute::Tensor>();
+    BuildArmComputeTensor(*m_CellBiasTensor, m_Data.m_CellBias->GetTensorInfo());
+
+    m_OutputGateBiasTensor = std::make_unique<arm_compute::Tensor>();
+    BuildArmComputeTensor(*m_OutputGateBiasTensor, m_Data.m_OutputGateBias->GetTensorInfo());
+
+    // for future reference: check the AndroidNN API for the logic here
+    if (!m_Data.m_Parameters.m_CifgEnabled)
+    {
+        m_InputToInputWeightsTensor = std::make_unique<arm_compute::Tensor>();
+        BuildArmComputeTensor(*m_InputToInputWeightsTensor, m_Data.m_InputToInputWeights->GetTensorInfo());
+
+        m_RecurrentToInputWeightsTensor = std::make_unique<arm_compute::Tensor>();
+        BuildArmComputeTensor(*m_RecurrentToInputWeightsTensor, m_Data.m_RecurrentToInputWeights->GetTensorInfo());
+
+        m_CellToInputWeightsTensor = std::make_unique<arm_compute::Tensor>();
+        if (m_Data.m_CellToInputWeights != nullptr)
+        {
+            BuildArmComputeTensor(*m_CellToInputWeightsTensor, m_Data.m_CellToInputWeights->GetTensorInfo());
+        }
+
+        m_InputGateBiasTensor = std::make_unique<arm_compute::Tensor>();
+        BuildArmComputeTensor(*m_InputGateBiasTensor, m_Data.m_InputGateBias->GetTensorInfo());
+
+        lstm_param.set_cifg_params(m_InputToInputWeightsTensor.get(),
+                                   m_RecurrentToInputWeightsTensor.get(),
+                                   m_Data.m_CellToInputWeights != nullptr ? m_CellToInputWeightsTensor.get() : nullptr,
+                                   m_InputGateBiasTensor.get());
+    }
+
+    if (m_Data.m_Parameters.m_ProjectionEnabled)
+    {
+        m_ProjectionWeightsTensor = std::make_unique<arm_compute::Tensor>();
+        BuildArmComputeTensor(*m_ProjectionWeightsTensor, m_Data.m_ProjectionWeights->GetTensorInfo());
+
+        m_ProjectionBiasTensor = std::make_unique<arm_compute::Tensor>();
+        if (m_Data.m_ProjectionBias != nullptr)
+        {
+            BuildArmComputeTensor(*m_ProjectionBiasTensor, m_Data.m_ProjectionBias->GetTensorInfo());
+        }
+
+        lstm_param.set_projection_params(m_ProjectionWeightsTensor.get(),
+                                         m_Data.m_ProjectionBias != nullptr ? m_ProjectionBiasTensor.get() : nullptr);
+    }
+
+    if (m_Data.m_Parameters.m_PeepholeEnabled)
+    {
+        m_CellToForgetWeightsTensor = std::make_unique<arm_compute::Tensor>();
+        BuildArmComputeTensor(*m_CellToForgetWeightsTensor, m_Data.m_CellToForgetWeights->GetTensorInfo());
+
+        m_CellToOutputWeightsTensor = std::make_unique<arm_compute::Tensor>();
+        BuildArmComputeTensor(*m_CellToOutputWeightsTensor, m_Data.m_CellToOutputWeights->GetTensorInfo());
+
+        lstm_param.set_peephole_params(m_CellToForgetWeightsTensor.get(), m_CellToOutputWeightsTensor.get());
+    }
+
+    const arm_compute::ITensor& input           = static_cast<INeonTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
+    const arm_compute::ITensor& output_state_in = static_cast<INeonTensorHandle*>(m_Data.m_Inputs[1])->GetTensor();
+    const arm_compute::ITensor& cell_state_in   = static_cast<INeonTensorHandle*>(m_Data.m_Inputs[2])->GetTensor();
+
+    arm_compute::ITensor& output_state_out      = static_cast<INeonTensorHandle*>(m_Data.m_Outputs[1])->GetTensor();
+    arm_compute::ITensor& cell_state_out        = static_cast<INeonTensorHandle*>(m_Data.m_Outputs[2])->GetTensor();
+    arm_compute::ITensor& output                = static_cast<INeonTensorHandle*>(m_Data.m_Outputs[3])->GetTensor();
+
+    // Get the batch_size and the num_units from the cellStateIn dimensions
+    const TensorInfo& inputTensorInfo = info.m_InputTensorInfos[2];
+    const unsigned int batch_size = boost::numeric_cast<unsigned int>(inputTensorInfo.GetShape()[0]);
+    const unsigned int num_units  = boost::numeric_cast<unsigned int>(inputTensorInfo.GetShape()[1]);
+
+    m_ScratchBuffer = std::make_unique<arm_compute::Tensor>();
+    if (m_Data.m_Parameters.m_CifgEnabled)
+    {
+        // 2D tensor with dimensions [num_units * 4, batch_size] with CIFG
+        armnn::TensorInfo scratchBuffer1({ batch_size, num_units * 4 }, DataType::Float32);
+        BuildArmComputeTensor(*m_ScratchBuffer, scratchBuffer1);
+    }
+    else
+    {
+        // scratch_buffer [num_units * 3, batch_size] without CIFG
+        armnn::TensorInfo scratchBuffer2({ batch_size, num_units * 3 }, DataType::Float32);
+        BuildArmComputeTensor(*m_ScratchBuffer, scratchBuffer2);
+    }
+
+    float cell_threshold = m_Data.m_Parameters.m_ClippingThresCell;
+    float projection_threshold = m_Data.m_Parameters.m_ClippingThresProj;
+
+    // for preparing the object for the class ActivationLayerInfo, we need to consider 5 situations
+    arm_compute::ActivationLayerInfo activationLayerInfo;
+    if (m_Data.m_Parameters.m_ActivationFunc == 0)
+    {
+        // no activation, do nothing
+    }
+    else if (m_Data.m_Parameters.m_ActivationFunc == 1)
+    {
+        activationLayerInfo = arm_compute::ActivationLayerInfo(
+                arm_compute::ActivationLayerInfo::ActivationFunction::RELU);
+    }
+    else if (m_Data.m_Parameters.m_ActivationFunc == 3)
+    {
+        activationLayerInfo = arm_compute::ActivationLayerInfo(
+                arm_compute::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.0);
+    }
+    else if (m_Data.m_Parameters.m_ActivationFunc == 4)
+    {
+        activationLayerInfo = arm_compute::ActivationLayerInfo(
+                arm_compute::ActivationLayerInfo::ActivationFunction::TANH, 1.0, 1.0);
+    }
+    else if (m_Data.m_Parameters.m_ActivationFunc == 6)
+    {
+        activationLayerInfo = arm_compute::ActivationLayerInfo(
+                arm_compute::ActivationLayerInfo::ActivationFunction::LOGISTIC);
+    }
+    else
+    {
+        throw armnn::Exception("Wrong Type of Activation Function!");
+    }
+
+
+    m_LstmLayer.configure(&input, m_InputToForgetWeightsTensor.get(), m_InputToCellWeightsTensor.get(),
+                          m_InputToOutputWeightsTensor.get(), m_RecurrentToForgetWeightsTensor.get(),
+                          m_RecurrentToCellWeightsTensor.get(), m_RecurrentToOutputWeightsTensor.get(),
+                          m_ForgetGateBiasTensor.get(), m_CellBiasTensor.get(), m_OutputGateBiasTensor.get(),
+                          &output_state_in, &cell_state_in, m_ScratchBuffer.get(), &output_state_out,
+                          &cell_state_out, &output, lstm_param, activationLayerInfo,
+                          cell_threshold, projection_threshold);
+
+    armcomputetensorutils::InitialiseArmComputeTensorEmpty(*m_ScratchBuffer);
+
+    InitialiseArmComputeTensorData(*m_InputToForgetWeightsTensor,
+                                   m_Data.m_InputToForgetWeights->GetConstTensor<float>());
+    InitialiseArmComputeTensorData(*m_InputToCellWeightsTensor,
+                                   m_Data.m_InputToCellWeights->GetConstTensor<float>());
+    InitialiseArmComputeTensorData(*m_InputToOutputWeightsTensor,
+                                   m_Data.m_InputToOutputWeights->GetConstTensor<float>());
+    InitialiseArmComputeTensorData(*m_RecurrentToForgetWeightsTensor,
+                                   m_Data.m_RecurrentToForgetWeights->GetConstTensor<float>());
+    InitialiseArmComputeTensorData(*m_RecurrentToCellWeightsTensor,
+                                   m_Data.m_RecurrentToCellWeights->GetConstTensor<float>());
+    InitialiseArmComputeTensorData(*m_RecurrentToOutputWeightsTensor,
+                                   m_Data.m_RecurrentToOutputWeights->GetConstTensor<float>());
+    InitialiseArmComputeTensorData(*m_ForgetGateBiasTensor,
+                                   m_Data.m_ForgetGateBias->GetConstTensor<float>());
+    InitialiseArmComputeTensorData(*m_CellBiasTensor,
+                                   m_Data.m_CellBias->GetConstTensor<float>());
+    InitialiseArmComputeTensorData(*m_OutputGateBiasTensor,
+                                   m_Data.m_OutputGateBias->GetConstTensor<float>());
+
+    if (!m_Data.m_Parameters.m_CifgEnabled)
+    {
+        InitialiseArmComputeTensorData(*m_InputToInputWeightsTensor,
+                                       m_Data.m_InputToInputWeights->GetConstTensor<float>());
+        InitialiseArmComputeTensorData(*m_RecurrentToInputWeightsTensor,
+                                       m_Data.m_RecurrentToInputWeights->GetConstTensor<float>());
+        if (m_Data.m_CellToInputWeights != nullptr)
+        {
+            InitialiseArmComputeTensorData(*m_CellToInputWeightsTensor,
+                                           m_Data.m_CellToInputWeights->GetConstTensor<float>());
+        }
+        InitialiseArmComputeTensorData(*m_InputGateBiasTensor,
+                                       m_Data.m_InputGateBias->GetConstTensor<float>());
+    }
+
+    if (m_Data.m_Parameters.m_ProjectionEnabled)
+    {
+        InitialiseArmComputeTensorData(*m_ProjectionWeightsTensor,
+                                       m_Data.m_ProjectionWeights->GetConstTensor<float>());
+        if (m_Data.m_ProjectionBias != nullptr)
+        {
+            InitialiseArmComputeTensorData(*m_ProjectionBiasTensor,
+                                           m_Data.m_ProjectionBias->GetConstTensor<float>());
+        }
+    }
+
+    if (m_Data.m_Parameters.m_PeepholeEnabled)
+    {
+        InitialiseArmComputeTensorData(*m_CellToForgetWeightsTensor,
+                                       m_Data.m_CellToForgetWeights->GetConstTensor<float>());
+        InitialiseArmComputeTensorData(*m_CellToOutputWeightsTensor,
+                                       m_Data.m_CellToOutputWeights->GetConstTensor<float>());
+    }
+
+    // Force Compute Library to perform the necessary copying and reshaping, after which
+    // delete all the input tensors that will no longer be needed
+    m_LstmLayer.prepare();
+    FreeUnusedTensors();
 }
 
 void NeonLstmFloatWorkload::Execute() const
 {
-    throw armnn::Exception("No implementation of Lstm in the Neon backend!");
+    m_LstmLayer.run();
+}
+
+arm_compute::Status NeonLstmFloatWorkloadValidate(const TensorInfo& input,
+                                                  const TensorInfo& outputStateIn,
+                                                  const TensorInfo& cellStateIn,
+                                                  const TensorInfo& scratchBuffer,
+                                                  const TensorInfo& outputStateOut,
+                                                  const TensorInfo& cellStateOut,
+                                                  const TensorInfo& output,
+                                                  const LstmDescriptor& descriptor,
+                                                  const TensorInfo& inputToForgetWeights,
+                                                  const TensorInfo& inputToCellWeights,
+                                                  const TensorInfo& inputToOutputWeights,
+                                                  const TensorInfo& recurrentToForgetWeights,
+                                                  const TensorInfo& recurrentToCellWeights,
+                                                  const TensorInfo& recurrentToOutputWeights,
+                                                  const TensorInfo& forgetGateBias,
+                                                  const TensorInfo& cellBias,
+                                                  const TensorInfo& outputGateBias,
+                                                  const TensorInfo* inputToInputWeights,
+                                                  const TensorInfo* recurrentToInputWeights,
+                                                  const TensorInfo* cellToInputWeights,
+                                                  const TensorInfo* inputGateBias,
+                                                  const TensorInfo* projectionWeights,
+                                                  const TensorInfo* projectionBias,
+                                                  const TensorInfo* cellToForgetWeights,
+                                                  const TensorInfo* cellToOutputWeights)
+{
+    arm_compute::LSTMParams<arm_compute::ITensorInfo> lstm_params_info;
+
+    // The inputs and the outputs
+    const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input);
+    const arm_compute::TensorInfo aclOutputStateInInfo = BuildArmComputeTensorInfo(outputStateIn);
+    const arm_compute::TensorInfo aclCellStateInInfo = BuildArmComputeTensorInfo(cellStateIn);
+    const arm_compute::TensorInfo aclScratchBufferInfo = BuildArmComputeTensorInfo(scratchBuffer);
+    const arm_compute::TensorInfo aclOutputStateOutInfo = BuildArmComputeTensorInfo(outputStateOut);
+    const arm_compute::TensorInfo aclCellStateOutInfo = BuildArmComputeTensorInfo(cellStateOut);
+    const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output);
+
+    // Basic parameters
+    const arm_compute::TensorInfo aclInputToForgetWeightsInfo = BuildArmComputeTensorInfo(inputToForgetWeights);
+    const arm_compute::TensorInfo aclInputToCellWeightsInfo = BuildArmComputeTensorInfo(inputToCellWeights);
+    const arm_compute::TensorInfo aclInputToOutputWeightsInfo = BuildArmComputeTensorInfo(inputToOutputWeights);
+    const arm_compute::TensorInfo aclRecurrentToForgetWeightsInfo
+                                  = BuildArmComputeTensorInfo(recurrentToForgetWeights);
+    const arm_compute::TensorInfo aclRecurrentToCellWeightsInfo
+                                  = BuildArmComputeTensorInfo(recurrentToCellWeights);
+    const arm_compute::TensorInfo aclRecurrentToOutputWeightsInfo
+                                  = BuildArmComputeTensorInfo(recurrentToOutputWeights);
+    const arm_compute::TensorInfo aclForgetGateBiasInfo = BuildArmComputeTensorInfo(forgetGateBias);
+    const arm_compute::TensorInfo aclCellBiasInfo = BuildArmComputeTensorInfo(cellBias);
+    const arm_compute::TensorInfo aclOutputGateBiasInfo = BuildArmComputeTensorInfo(outputGateBias);
+
+    arm_compute::TensorInfo aclInputToInputWeightsInfo;
+    arm_compute::TensorInfo aclRecurrentToInputWeightsInfo;
+    arm_compute::TensorInfo aclCellToInputWeightsInfo;
+    arm_compute::TensorInfo aclInputGateBiasInfo;
+    arm_compute::TensorInfo aclProjectionWeightsInfo;
+    arm_compute::TensorInfo aclProjectionBiasInfo;
+    arm_compute::TensorInfo aclCellToForgetWeightsInfo;
+    arm_compute::TensorInfo aclCellToOutputWeightsInfo;
+
+    if (!descriptor.m_CifgEnabled)
+    {
+        armnn::TensorInfo inputToInputWInfo = *inputToInputWeights;
+        aclInputToInputWeightsInfo = BuildArmComputeTensorInfo(inputToInputWInfo);
+        armnn::TensorInfo recurrentToInputWInfo = *recurrentToInputWeights;
+        aclRecurrentToInputWeightsInfo = BuildArmComputeTensorInfo(recurrentToInputWInfo);
+
+        if (cellToInputWeights != nullptr)
+        {
+            armnn::TensorInfo cellToInputWInfo = *cellToInputWeights;
+            aclCellToInputWeightsInfo = BuildArmComputeTensorInfo(cellToInputWInfo);
+        }
+        armnn::TensorInfo inputGateBiasInfo = *inputGateBias;
+        aclInputGateBiasInfo = BuildArmComputeTensorInfo(inputGateBiasInfo);
+        lstm_params_info.set_cifg_params(&aclInputToInputWeightsInfo, &aclRecurrentToInputWeightsInfo,
+                                         cellToInputWeights != nullptr ? &aclCellToInputWeightsInfo: nullptr,
+                                         &aclInputGateBiasInfo);
+    }
+
+    if (descriptor.m_ProjectionEnabled)
+    {
+        const armnn::TensorInfo& projectionWInfo = *projectionWeights;
+        aclProjectionWeightsInfo = BuildArmComputeTensorInfo(projectionWInfo);
+
+        if (projectionBias != nullptr)
+        {
+            const armnn::TensorInfo& projectionBiasInfo = *projectionBias;
+            aclProjectionBiasInfo = BuildArmComputeTensorInfo(projectionBiasInfo);
+        }
+        lstm_params_info.set_projection_params(&aclProjectionWeightsInfo,
+                                               projectionBias != nullptr ? &aclProjectionBiasInfo: nullptr);
+    }
+
+    if (descriptor.m_PeepholeEnabled)
+    {
+        const armnn::TensorInfo& cellToForgetWInfo = *cellToForgetWeights;
+        aclCellToForgetWeightsInfo = BuildArmComputeTensorInfo(cellToForgetWInfo);
+        const armnn::TensorInfo& cellToOutputWInfo = *cellToOutputWeights;
+        aclCellToOutputWeightsInfo = BuildArmComputeTensorInfo(cellToOutputWInfo);
+        lstm_params_info.set_peephole_params(&aclCellToForgetWeightsInfo, &aclCellToOutputWeightsInfo);
+    }
+
+    float cell_threshold = descriptor.m_ClippingThresCell;
+    float projection_threshold = descriptor.m_ClippingThresProj;
+
+    // for preparing the object for the class ActivationLayerInfo, we need to consider 5 situations
+    arm_compute::ActivationLayerInfo activationLayerInfo;
+    switch (descriptor.m_ActivationFunc)
+    {
+        case 0:
+            // no activation, do nothing
+            break;
+        case 1:
+            activationLayerInfo = arm_compute::ActivationLayerInfo(
+                    arm_compute::ActivationLayerInfo::ActivationFunction::RELU);
+            break;
+        case 3:
+            activationLayerInfo = arm_compute::ActivationLayerInfo(
+                    arm_compute::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.0);
+            break;
+        case 4:
+            activationLayerInfo = arm_compute::ActivationLayerInfo(
+                    arm_compute::ActivationLayerInfo::ActivationFunction::TANH, 1.0, 1.0);
+            break;
+        case 6:
+            activationLayerInfo = arm_compute::ActivationLayerInfo(
+                    arm_compute::ActivationLayerInfo::ActivationFunction::LOGISTIC);
+            break;
+        default:
+            throw armnn::Exception("Wrong Type of Activation Function!");
+    }
+
+    return arm_compute::NELSTMLayer::validate(&aclInputInfo,
+                                              &aclInputToForgetWeightsInfo,
+                                              &aclInputToCellWeightsInfo,
+                                              &aclInputToOutputWeightsInfo,
+                                              &aclRecurrentToForgetWeightsInfo,
+                                              &aclRecurrentToCellWeightsInfo,
+                                              &aclRecurrentToOutputWeightsInfo,
+                                              &aclForgetGateBiasInfo,
+                                              &aclCellBiasInfo,
+                                              &aclOutputGateBiasInfo,
+                                              &aclOutputStateInInfo,
+                                              &aclCellStateInInfo,
+                                              &aclScratchBufferInfo,
+                                              &aclOutputStateOutInfo,
+                                              &aclCellStateOutInfo,
+                                              &aclOutputInfo,
+                                              lstm_params_info,
+                                              activationLayerInfo,
+                                              cell_threshold,
+                                              projection_threshold);
+}
+
+void NeonLstmFloatWorkload::FreeUnusedTensors()
+{
+    FreeTensorIfUnused(m_InputToInputWeightsTensor);
+    FreeTensorIfUnused(m_InputToForgetWeightsTensor);
+    FreeTensorIfUnused(m_InputToCellWeightsTensor);
+    FreeTensorIfUnused(m_InputToOutputWeightsTensor);
+    FreeTensorIfUnused(m_RecurrentToInputWeightsTensor);
+    FreeTensorIfUnused(m_RecurrentToForgetWeightsTensor);
+    FreeTensorIfUnused(m_RecurrentToCellWeightsTensor);
+    FreeTensorIfUnused(m_RecurrentToOutputWeightsTensor);
+    FreeTensorIfUnused(m_CellToInputWeightsTensor);
+    FreeTensorIfUnused(m_CellToForgetWeightsTensor);
+    FreeTensorIfUnused(m_CellToOutputWeightsTensor);
+    FreeTensorIfUnused(m_InputGateBiasTensor);
+    FreeTensorIfUnused(m_ForgetGateBiasTensor);
+    FreeTensorIfUnused(m_CellBiasTensor);
+    FreeTensorIfUnused(m_OutputGateBiasTensor);
+    FreeTensorIfUnused(m_ProjectionWeightsTensor);
+    FreeTensorIfUnused(m_ProjectionBiasTensor);
+    FreeTensorIfUnused(m_ScratchBuffer);
 }
 
-} // namespace armnn
+} //namespace armnn
diff --git a/src/backends/neon/workloads/NeonLstmFloatWorkload.hpp b/src/backends/neon/workloads/NeonLstmFloatWorkload.hpp
index 4a5394f0a0..d21462bf83 100644
--- a/src/backends/neon/workloads/NeonLstmFloatWorkload.hpp
+++ b/src/backends/neon/workloads/NeonLstmFloatWorkload.hpp
@@ -5,7 +5,11 @@
 
 #pragma once
 
-#include <backends/neon/workloads/NeonWorkloadUtils.hpp>
+#include <backends/Workload.hpp>
+#include <backends/WorkloadData.hpp>
+
+#include "arm_compute/graph/Tensor.h"
+#include "arm_compute/runtime/NEON/functions/NELSTMLayer.h"
 
 namespace armnn
 {
@@ -15,6 +19,52 @@ class NeonLstmFloatWorkload : public FloatWorkload<LstmQueueDescriptor>
 public:
     NeonLstmFloatWorkload(const LstmQueueDescriptor& descriptor, const WorkloadInfo& info);
     virtual void Execute() const override;
+
+private:
+    mutable arm_compute::NELSTMLayer m_LstmLayer;
+
+    std::unique_ptr<arm_compute::Tensor> m_InputToInputWeightsTensor;
+    std::unique_ptr<arm_compute::Tensor> m_InputToForgetWeightsTensor;
+    std::unique_ptr<arm_compute::Tensor> m_InputToCellWeightsTensor;
+    std::unique_ptr<arm_compute::Tensor> m_InputToOutputWeightsTensor;
+    std::unique_ptr<arm_compute::Tensor> m_RecurrentToInputWeightsTensor;
+    std::unique_ptr<arm_compute::Tensor> m_RecurrentToForgetWeightsTensor;
+    std::unique_ptr<arm_compute::Tensor> m_RecurrentToCellWeightsTensor;
+    std::unique_ptr<arm_compute::Tensor> m_RecurrentToOutputWeightsTensor;
+    std::unique_ptr<arm_compute::Tensor> m_CellToInputWeightsTensor;
+    std::unique_ptr<arm_compute::Tensor> m_CellToForgetWeightsTensor;
+    std::unique_ptr<arm_compute::Tensor> m_CellToOutputWeightsTensor;
+    std::unique_ptr<arm_compute::Tensor> m_InputGateBiasTensor;
+    std::unique_ptr<arm_compute::Tensor> m_ForgetGateBiasTensor;
+    std::unique_ptr<arm_compute::Tensor> m_CellBiasTensor;
+    std::unique_ptr<arm_compute::Tensor> m_OutputGateBiasTensor;
+    std::unique_ptr<arm_compute::Tensor> m_ProjectionWeightsTensor;
+    std::unique_ptr<arm_compute::Tensor> m_ProjectionBiasTensor;
+
+    std::unique_ptr<arm_compute::Tensor> m_ScratchBuffer;
+
+    void FreeUnusedTensors();
 };
 
+arm_compute::Status NeonLstmFloatWorkloadValidate(const TensorInfo& input, const TensorInfo& outputStateIn,
+                                                  const TensorInfo& cellStateIn, const TensorInfo& scratchBuffer,
+                                                  const TensorInfo& outputStateOut, const TensorInfo& cellStateOut,
+                                                  const TensorInfo& output, const LstmDescriptor &descriptor,
+                                                  const TensorInfo& inputToForgetWeights,
+                                                  const TensorInfo& inputToCellWeights,
+                                                  const TensorInfo& inputToOutputWeights,
+                                                  const TensorInfo& recurrentToForgetWeights,
+                                                  const TensorInfo& recurrentToCellWeights,
+                                                  const TensorInfo& recurrentToOutputWeights,
+                                                  const TensorInfo& forgetGateBias, const TensorInfo& cellBias,
+                                                  const TensorInfo& outputGateBias,
+                                                  const TensorInfo* inputToInputWeights,
+                                                  const TensorInfo* recurrentToInputWeights,
+                                                  const TensorInfo* cellToInputWeights,
+                                                  const TensorInfo* inputGateBias,
+                                                  const TensorInfo* projectionWeights,
+                                                  const TensorInfo* projectionBias,
+                                                  const TensorInfo* cellToForgetWeights,
+                                                  const TensorInfo* cellToOutputWeights);
+
 } //namespace armnn
diff --git a/src/backends/test/ArmComputeCl.cpp b/src/backends/test/ArmComputeCl.cpp
index af30ff0c29..b4ec9bab16 100644
--- a/src/backends/test/ArmComputeCl.cpp
+++ b/src/backends/test/ArmComputeCl.cpp
@@ -240,11 +240,11 @@ ARMNN_AUTO_TEST_CASE(PermuteFloat32ValueSet3, PermuteFloat32ValueSet3Test)
 
 // Lstm
 ARMNN_AUTO_TEST_CASE(LstmLayerFloat32WithCifgWithPeepholeNoProjection,
-        LstmLayerFloat32WithCifgWithPeepholeNoProjectionTest)
+                     LstmLayerFloat32WithCifgWithPeepholeNoProjectionTest)
 ARMNN_AUTO_TEST_CASE(LstmLayerFloat32NoCifgNoPeepholeNoProjection,
                      LstmLayerFloat32NoCifgNoPeepholeNoProjectionTest)
 ARMNN_AUTO_TEST_CASE(LstmLayerFloat32NoCifgWithPeepholeWithProjection,
-                         LstmLayerFloat32NoCifgWithPeepholeWithProjectionTest)
+                     LstmLayerFloat32NoCifgWithPeepholeWithProjectionTest)
 
 // Convert from Float16 to Float32
 ARMNN_AUTO_TEST_CASE(SimpleConvertFp16ToFp32, SimpleConvertFp16ToFp32Test)
diff --git a/src/backends/test/ArmComputeNeon.cpp b/src/backends/test/ArmComputeNeon.cpp
index 214f8d8f05..a5733d8a18 100644
--- a/src/backends/test/ArmComputeNeon.cpp
+++ b/src/backends/test/ArmComputeNeon.cpp
@@ -392,6 +392,14 @@ ARMNN_AUTO_TEST_CASE(PermuteFloat32ValueSet1, PermuteFloat32ValueSet1Test)
 ARMNN_AUTO_TEST_CASE(PermuteFloat32ValueSet2, PermuteFloat32ValueSet2Test)
 ARMNN_AUTO_TEST_CASE(PermuteFloat32ValueSet3, PermuteFloat32ValueSet3Test)
 
+// Lstm
+ARMNN_AUTO_TEST_CASE(LstmLayerFloat32WithCifgWithPeepholeNoProjection,
+                     LstmLayerFloat32WithCifgWithPeepholeNoProjectionTest)
+ARMNN_AUTO_TEST_CASE(LstmLayerFloat32NoCifgNoPeepholeNoProjection,
+                     LstmLayerFloat32NoCifgNoPeepholeNoProjectionTest)
+ARMNN_AUTO_TEST_CASE(LstmLayerFloat32NoCifgWithPeepholeWithProjection,
+                     LstmLayerFloat32NoCifgWithPeepholeWithProjectionTest)
+
 // Normalization
 ARMNN_AUTO_TEST_CASE(SimpleNormalizationAcross, SimpleNormalizationAcrossTest)
 ARMNN_AUTO_TEST_CASE(SimpleNormalizationWithin, SimpleNormalizationWithinTest)