From e5339e7013cf24e5a34509fb0a60377e5f8a244e Mon Sep 17 00:00:00 2001
From: Narumol Prangnawarat <narumol.prangnawarat@arm.com>
Date: Wed, 28 Jul 2021 17:33:28 +0100
Subject: MLCE-530 Add support for UnidirectionalSequenceLstm to RefWorkload

 * Add implementation of IsUnidirectionalSequenceLstmSupported to RefLayerSupport
 * Add RefUnidirectionalSequenceLstmWorkload
 * Refactor Lstm to be able to use for Lstm and SequenceLstm
 * Unit tests

Signed-off-by: Narumol Prangnawarat <narumol.prangnawarat@arm.com>
Change-Id: Ibc066d213213a11b955dfefbe518de643298ba0c
---
 src/backends/reference/workloads/CMakeLists.txt    |   4 +
 src/backends/reference/workloads/Lstm.cpp          | 259 +++++++++++++++++
 src/backends/reference/workloads/Lstm.hpp          |  61 ++++
 .../reference/workloads/RefLstmWorkload.cpp        | 235 ++++------------
 .../RefUnidirectionalSequenceLstmWorkload.cpp      | 307 +++++++++++++++++++++
 .../RefUnidirectionalSequenceLstmWorkload.hpp      |  56 ++++
 src/backends/reference/workloads/RefWorkloads.hpp  |   1 +
 7 files changed, 735 insertions(+), 188 deletions(-)
 create mode 100644 src/backends/reference/workloads/Lstm.cpp
 create mode 100644 src/backends/reference/workloads/Lstm.hpp
 create mode 100644 src/backends/reference/workloads/RefUnidirectionalSequenceLstmWorkload.cpp
 create mode 100644 src/backends/reference/workloads/RefUnidirectionalSequenceLstmWorkload.hpp

(limited to 'src/backends/reference/workloads')

diff --git a/src/backends/reference/workloads/CMakeLists.txt b/src/backends/reference/workloads/CMakeLists.txt
index 7a769e5246..b9f477cb6d 100644
--- a/src/backends/reference/workloads/CMakeLists.txt
+++ b/src/backends/reference/workloads/CMakeLists.txt
@@ -42,6 +42,8 @@ list(APPEND armnnRefBackendWorkloads_sources
     Log.hpp
     LogSoftmax.cpp
     LogSoftmax.hpp
+    Lstm.cpp
+    Lstm.hpp
     LstmUtils.hpp
     LstmUtils.cpp
     Maximum.hpp
@@ -162,6 +164,8 @@ list(APPEND armnnRefBackendWorkloads_sources
     RefTransposeConvolution2dWorkload.hpp
     RefTransposeWorkload.cpp
     RefTransposeWorkload.hpp
+    RefUnidirectionalSequenceLstmWorkload.cpp
+    RefUnidirectionalSequenceLstmWorkload.hpp
     RefWorkloads.hpp
     RefWorkloadUtils.hpp
     Resize.cpp
diff --git a/src/backends/reference/workloads/Lstm.cpp b/src/backends/reference/workloads/Lstm.cpp
new file mode 100644
index 0000000000..c1fb2bf4aa
--- /dev/null
+++ b/src/backends/reference/workloads/Lstm.cpp
@@ -0,0 +1,259 @@
+//
+// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "Activation.hpp"
+#include "Lstm.hpp"
+#include "LstmUtils.hpp"
+
+namespace armnn
+{
+
+void LstmImpl(const LstmDescriptor& descriptor,
+              const TensorInfo& inputInfo,
+              const TensorInfo& outputInfo,
+              const TensorShape& inputToOutputWeightsShape,
+              const TensorShape& recurrentToOutputWeightsShape,
+              std::unique_ptr<Decoder<float>>& inputData,
+              std::unique_ptr<Decoder<float>>& outputStateIn,
+              std::unique_ptr<Decoder<float>>& cellStateIn,
+              std::unique_ptr<Encoder<float>>& outputStateOut,
+              std::unique_ptr<Encoder<float>>& cellStateOut,
+              std::unique_ptr<Encoder<float>>& output,
+              std::unique_ptr<Decoder<float>>& cellStateOutDecoder,
+              std::unique_ptr<Decoder<float>>& outputDecoder,
+              std::unique_ptr<Decoder<float>>& inputToInputWeightsTensor,
+              std::unique_ptr<Decoder<float>>& inputToForgetWeightsTensor,
+              std::unique_ptr<Decoder<float>>& inputToCellWeightsTensor,
+              std::unique_ptr<Decoder<float>>& inputToOutputWeightsTensor,
+              std::unique_ptr<Decoder<float>>& recurrentToInputWeightsTensor,
+              std::unique_ptr<Decoder<float>>& recurrentToForgetWeightsTensor,
+              std::unique_ptr<Decoder<float>>& recurrentToCellWeightsTensor,
+              std::unique_ptr<Decoder<float>>& recurrentToOutputWeightsTensor,
+              std::unique_ptr<Decoder<float>>& cellToInputWeightsTensor,
+              std::unique_ptr<Decoder<float>>& cellToForgetWeightsTensor,
+              std::unique_ptr<Decoder<float>>& cellToOutputWeightsTensor,
+              std::unique_ptr<Decoder<float>>& inputGateBiasTensor,
+              std::unique_ptr<Decoder<float>>& forgetGateBiasTensor,
+              std::unique_ptr<Decoder<float>>& cellBiasTensor,
+              std::unique_ptr<Decoder<float>>& outputGateBiasTensor,
+              std::unique_ptr<Decoder<float>>& projectionWeightsTensor,
+              std::unique_ptr<Decoder<float>>& projectionBiasTensor,
+              std::unique_ptr<Decoder<float>>& inputLayerNormWeights,
+              std::unique_ptr<Decoder<float>>& forgetLayerNormWeights,
+              std::unique_ptr<Decoder<float>>& cellLayerNormWeights,
+              std::unique_ptr<Decoder<float>>& outputLayerNormWeights,
+              std::unique_ptr<Encoder<float>>& inputGateScratch,
+              std::unique_ptr<Encoder<float>>& cellScratch,
+              std::unique_ptr<Encoder<float>>& forgetGateScratch,
+              std::unique_ptr<Encoder<float>>& outputGateScratch,
+              std::unique_ptr<Decoder<float>>& inputGateScratchDecoder,
+              std::unique_ptr<Decoder<float>>& cellScratchDecoder,
+              std::unique_ptr<Decoder<float>>& forgetGateScratchDecoder,
+              std::unique_ptr<Decoder<float>>& outputGateScratchDecoder,
+              float layerNormEpsilon)
+{
+    // This is a porting of the LSTM::Eval() method in the Android code base
+    // Refer to: android/frameworks/ml/nn/common/operations/LSTM.cpp
+
+    const TensorShape& inputShape = inputInfo.GetShape();
+    const DataType& outputType = outputInfo.GetDataType();
+
+    const uint32_t nBatch = inputShape[0];
+    const uint32_t nInput = inputShape[1];
+
+    const uint32_t nCell   = inputToOutputWeightsShape[0];
+    const uint32_t nOutput = recurrentToOutputWeightsShape[1];
+
+    const bool useCifg      = descriptor.m_CifgEnabled;
+    const bool usePeephole  = descriptor.m_PeepholeEnabled;
+    const bool useLayerNorm = descriptor.m_LayerNormEnabled;
+
+    if (!useLayerNorm)
+    {
+        // Initialize scratch buffers with bias.
+        if (!useCifg)
+        {
+            VectorBatchVectorAssign(*inputGateBiasTensor,
+                                    nCell, nBatch, *inputGateScratch);
+        }
+        VectorBatchVectorAssign(*forgetGateBiasTensor,
+                                nCell, nBatch, *forgetGateScratch);
+        VectorBatchVectorAssign(*cellBiasTensor,
+                                nCell, nBatch, *cellScratch);
+        VectorBatchVectorAssign(*outputGateBiasTensor,
+                                nCell, nBatch, *outputGateScratch);
+    }
+    else
+    {
+        // Initialize scratch buffers with zeroes.
+        if (!useCifg)
+        {
+            ZeroVector(*inputGateScratch, nCell * nBatch);
+        }
+        ZeroVector(*forgetGateScratch, nCell * nBatch);
+        ZeroVector(*cellScratch      , nCell * nBatch);
+        ZeroVector(*outputGateScratch, nCell * nBatch);
+    }
+
+    // For each batch and cell: compute input_weight * input.
+    if (!useCifg)
+    {
+        MatrixBatchVectorMultiplyAccumulate(*inputToInputWeightsTensor,
+                                            nCell, nInput, *inputData, nBatch, *inputGateScratch);
+    }
+    MatrixBatchVectorMultiplyAccumulate(*inputToForgetWeightsTensor,
+                                        nCell, nInput, *inputData, nBatch, *forgetGateScratch);
+    MatrixBatchVectorMultiplyAccumulate(*inputToCellWeightsTensor,
+                                        nCell, nInput, *inputData, nBatch, *cellScratch);
+    MatrixBatchVectorMultiplyAccumulate(*inputToOutputWeightsTensor,
+                                        nCell, nInput, *inputData, nBatch, *outputGateScratch);
+
+    // For each batch and cell: compute recurrent_weight * output_state.
+    if (!useCifg)
+    {
+        MatrixBatchVectorMultiplyAccumulate(*recurrentToInputWeightsTensor,
+                                            nCell, nOutput, *outputStateIn, nBatch, *inputGateScratch);
+    }
+    MatrixBatchVectorMultiplyAccumulate(*recurrentToForgetWeightsTensor,
+                                        nCell, nOutput, *outputStateIn, nBatch, *forgetGateScratch);
+    MatrixBatchVectorMultiplyAccumulate(*recurrentToCellWeightsTensor,
+                                        nCell, nOutput, *outputStateIn, nBatch, *cellScratch);
+    MatrixBatchVectorMultiplyAccumulate(*recurrentToOutputWeightsTensor,
+                                        nCell, nOutput, *outputStateIn, nBatch, *outputGateScratch);
+
+    // For each batch and cell: update input gate.
+    if (!useCifg)
+    {
+        if (usePeephole)
+        {
+            VectorBatchVectorCwiseProductAccumulate(*cellToInputWeightsTensor,
+                                                    nCell, *cellStateIn, nBatch, *inputGateScratch);
+        }
+        if (useLayerNorm)
+        {
+            MeanStddevNormalization(*inputGateScratchDecoder,
+                                    *inputGateScratch, nCell, nBatch, layerNormEpsilon);
+            VectorBatchVectorCwiseProduct(*inputLayerNormWeights,
+                                          nCell, *inputGateScratchDecoder, nBatch, *inputGateScratch);
+            VectorBatchVectorAdd(*inputGateBiasTensor,
+                                 nCell, *inputGateScratchDecoder, nBatch, *inputGateScratch);
+        }
+        Activation(*inputGateScratchDecoder, *inputGateScratch,
+                   TensorInfo({nCell, nBatch}, outputType),
+                   ActivationFunction::Sigmoid, 0, 0);
+    }
+
+    // For each batch and cell: update forget gate.
+    if (usePeephole)
+    {
+        VectorBatchVectorCwiseProductAccumulate(*cellToForgetWeightsTensor, nCell,
+                                                *cellStateIn, nBatch, *forgetGateScratch);
+    }
+    if (useLayerNorm)
+    {
+        MeanStddevNormalization(*forgetGateScratchDecoder,
+                                *forgetGateScratch, nCell, nBatch, layerNormEpsilon);
+        VectorBatchVectorCwiseProduct(*forgetLayerNormWeights,
+                                      nCell, *forgetGateScratchDecoder, nBatch, *forgetGateScratch);
+        VectorBatchVectorAdd(*forgetGateBiasTensor,
+                             nCell, *forgetGateScratchDecoder, nBatch, *forgetGateScratch);
+    }
+    Activation(*forgetGateScratchDecoder, *forgetGateScratch,
+               TensorInfo({nCell, nBatch}, outputType),
+               ActivationFunction::Sigmoid, 0, 0);
+
+    // For each batch and cell: update the cell.
+    if (useLayerNorm)
+    {
+        MeanStddevNormalization(*cellScratchDecoder,
+                                *cellScratch, nCell, nBatch, layerNormEpsilon);
+        VectorBatchVectorCwiseProduct(*cellLayerNormWeights,
+                                      nCell, *cellScratchDecoder, nBatch, *cellScratch);
+        VectorBatchVectorAdd(*cellBiasTensor,
+                             nCell, *cellScratchDecoder, nBatch, *cellScratch);
+    }
+
+    VectorVectorCwiseProduct(*forgetGateScratchDecoder, *cellStateIn, nBatch * nCell, *cellStateOut);
+
+    ActivationFunction armnnActivationFunc = ActivationFunction::Sigmoid;
+    float a = 0;
+    float b = 0;
+    SetActivationParameters(descriptor.m_ActivationFunc, armnnActivationFunc, a, b);
+
+    if (descriptor.m_ActivationFunc > 0)
+    {
+        Activation(*cellScratchDecoder, *cellScratch,
+                   TensorInfo({nCell, nBatch}, outputType),
+                   armnnActivationFunc, a, b);
+    }
+    if (useCifg)
+    {
+        Sub1Vector(*forgetGateScratchDecoder, nBatch * nCell, *forgetGateScratch);
+        VectorVectorCwiseProductAccumulate(
+            *cellScratchDecoder, *forgetGateScratchDecoder, nBatch * nCell, *cellStateOut);
+    }
+    else
+    {
+        VectorVectorCwiseProductAccumulate(
+            *cellScratchDecoder, *inputGateScratchDecoder, nBatch * nCell, *cellStateOut);
+    }
+    if (descriptor.m_ClippingThresCell > 0.0)
+    {
+        ClipVector(*cellStateOutDecoder, nBatch * nCell, descriptor.m_ClippingThresCell, *cellStateOut);
+    }
+
+    // For each batch and cell: update the output gate.
+    if (usePeephole)
+    {
+        VectorBatchVectorCwiseProductAccumulate(*cellToOutputWeightsTensor,
+                                                nCell, *cellStateOutDecoder, nBatch, *outputGateScratch);
+    }
+    if (useLayerNorm)
+    {
+        MeanStddevNormalization(*outputGateScratchDecoder,
+                                *outputGateScratch, nCell, nBatch, layerNormEpsilon);
+        VectorBatchVectorCwiseProduct(*outputLayerNormWeights,
+                                      nCell, *outputGateScratchDecoder, nBatch, *outputGateScratch);
+        VectorBatchVectorAdd(*outputGateBiasTensor,
+                             nCell, *outputGateScratchDecoder, nBatch, *outputGateScratch);
+    }
+    Activation(*outputGateScratchDecoder, *outputGateScratch,
+               TensorInfo({nCell, nBatch}, outputType),
+               ActivationFunction::Sigmoid, 0, 0);
+
+    if (descriptor.m_ActivationFunc > 0)
+    {
+        Activation(*cellStateOutDecoder, *cellScratch,
+                   TensorInfo({nCell, nBatch}, outputType),
+                   armnnActivationFunc, a, b);
+    }
+
+    VectorVectorCwiseProduct(*outputGateScratchDecoder, *cellScratchDecoder, nBatch * nCell, *outputGateScratch);
+
+    // For each batch: update the projection and output_state.
+    if (descriptor.m_ProjectionEnabled)
+    {
+        if (projectionBiasTensor)
+        {
+            VectorBatchVectorAssign(*projectionBiasTensor,
+                                    nOutput, nBatch, *output);
+        }
+        MatrixBatchVectorMultiplyAccumulate(*projectionWeightsTensor,
+                                            nOutput, nCell, *outputGateScratchDecoder, nBatch, *output);
+
+        if (descriptor.m_ClippingThresProj > 0.0)
+        {
+            ClipVector(*outputDecoder, nBatch * nOutput, descriptor.m_ClippingThresProj, *output);
+        }
+    }
+    else
+    {
+        CopyVector(*outputGateScratchDecoder, nBatch * nOutput, *output);
+    }
+
+    CopyVector(*outputDecoder, nBatch * nOutput, *outputStateOut);
+}
+
+} //namespace armnn
diff --git a/src/backends/reference/workloads/Lstm.hpp b/src/backends/reference/workloads/Lstm.hpp
new file mode 100644
index 0000000000..7d0a1d436e
--- /dev/null
+++ b/src/backends/reference/workloads/Lstm.hpp
@@ -0,0 +1,61 @@
+//
+// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include <armnn/TypesUtils.hpp>
+#include <backendsCommon/WorkloadData.hpp>
+
+#include "Encoders.hpp"
+#include "Decoders.hpp"
+
+namespace armnn
+{
+
+void LstmImpl(const LstmDescriptor& descriptor,
+              const TensorInfo& inputInfo,
+              const TensorInfo& outputInfo,
+              const TensorShape& inputToOutputWeightsShape,
+              const TensorShape& recurrentToOutputWeightsShape,
+              std::unique_ptr<Decoder<float>>& inputData,
+              std::unique_ptr<Decoder<float>>& outputStateIn,
+              std::unique_ptr<Decoder<float>>& cellStateIn,
+              std::unique_ptr<Encoder<float>>& outputStateOut,
+              std::unique_ptr<Encoder<float>>& cellStateOut,
+              std::unique_ptr<Encoder<float>>& output,
+              std::unique_ptr<Decoder<float>>& cellStateOutDecoder,
+              std::unique_ptr<Decoder<float>>& outputDecoder,
+              std::unique_ptr<Decoder<float>>& inputToInputWeightsTensor,
+              std::unique_ptr<Decoder<float>>& inputToForgetWeightsTensor,
+              std::unique_ptr<Decoder<float>>& inputToCellWeightsTensor,
+              std::unique_ptr<Decoder<float>>& inputToOutputWeightsTensor,
+              std::unique_ptr<Decoder<float>>& recurrentToInputWeightsTensor,
+              std::unique_ptr<Decoder<float>>& recurrentToForgetWeightsTensor,
+              std::unique_ptr<Decoder<float>>& recurrentToCellWeightsTensor,
+              std::unique_ptr<Decoder<float>>& recurrentToOutputWeightsTensor,
+              std::unique_ptr<Decoder<float>>& cellToInputWeightsTensor,
+              std::unique_ptr<Decoder<float>>& cellToForgetWeightsTensor,
+              std::unique_ptr<Decoder<float>>& cellToOutputWeightsTensor,
+              std::unique_ptr<Decoder<float>>& inputGateBiasTensor,
+              std::unique_ptr<Decoder<float>>& forgetGateBiasTensor,
+              std::unique_ptr<Decoder<float>>& cellBiasTensor,
+              std::unique_ptr<Decoder<float>>& outputGateBiasTensor,
+              std::unique_ptr<Decoder<float>>& projectionWeightsTensor,
+              std::unique_ptr<Decoder<float>>& projectionBiasTensor,
+              std::unique_ptr<Decoder<float>>& inputLayerNormWeights,
+              std::unique_ptr<Decoder<float>>& forgetLayerNormWeights,
+              std::unique_ptr<Decoder<float>>& cellLayerNormWeights,
+              std::unique_ptr<Decoder<float>>& outputLayerNormWeights,
+              std::unique_ptr<Encoder<float>>& inputGateScratch,
+              std::unique_ptr<Encoder<float>>& cellScratch,
+              std::unique_ptr<Encoder<float>>& forgetGateScratch,
+              std::unique_ptr<Encoder<float>>& outputGateScratch,
+              std::unique_ptr<Decoder<float>>& inputGateScratchDecoder,
+              std::unique_ptr<Decoder<float>>& cellScratchDecoder,
+              std::unique_ptr<Decoder<float>>& forgetGateScratchDecoder,
+              std::unique_ptr<Decoder<float>>& outputGateScratchDecoder,
+              float layerNormEpsilon);
+
+} //namespace armnn
diff --git a/src/backends/reference/workloads/RefLstmWorkload.cpp b/src/backends/reference/workloads/RefLstmWorkload.cpp
index 3ddfd334b8..1ff6f50ed5 100644
--- a/src/backends/reference/workloads/RefLstmWorkload.cpp
+++ b/src/backends/reference/workloads/RefLstmWorkload.cpp
@@ -7,6 +7,7 @@
 #include "Activation.hpp"
 #include "Encoders.hpp"
 #include "Decoders.hpp"
+#include "Lstm.hpp"
 #include "LstmUtils.hpp"
 #include "RefWorkloadUtils.hpp"
 
@@ -57,7 +58,6 @@ void RefLstmWorkload::Execute(std::vector<ITensorHandle*> inputs, std::vector<IT
     const TensorInfo& outputInfo = GetTensorInfo(outputs[0]);
 
     const TensorShape& inputShape = inputInfo.GetShape();
-    const DataType& outputType = outputInfo.GetDataType();
 
     std::unique_ptr<Encoder<float>> outputStateOut = MakeEncoder<float>(outputInfo, outputs[1]->Map());
     std::unique_ptr<Encoder<float>> cellStateOut   = MakeEncoder<float>(outputInfo, outputs[2]->Map());
@@ -71,10 +71,7 @@ void RefLstmWorkload::Execute(std::vector<ITensorHandle*> inputs, std::vector<IT
     std::unique_ptr<Decoder<float>> cellStateIn   = MakeDecoder<float>(inputInfo, inputs[2]->Map());
 
     const uint32_t nBatch = inputShape[0];
-    const uint32_t nInput = inputShape[1];
-
     const uint32_t nCell   = m_InputToOutputWeightsTensor->GetShape()[0];
-    const uint32_t nOutput = m_RecurrentToOutputWeightsTensor->GetShape()[1];
 
     const bool useCifg      = m_Data.m_Parameters.m_CifgEnabled;
     const bool usePeephole  = m_Data.m_Parameters.m_PeepholeEnabled;
@@ -154,6 +151,9 @@ void RefLstmWorkload::Execute(std::vector<ITensorHandle*> inputs, std::vector<IT
     std::unique_ptr<Decoder<float>> cellLayerNormWeights;
     std::unique_ptr<Decoder<float>> outputLayerNormWeights;
 
+    const TensorShape& inputToOutputWeightsShape = m_InputToOutputWeightsTensor->GetShape();
+    const TensorShape& recurrentToOutputWeightsShape = m_RecurrentToOutputWeightsTensor->GetShape();
+
     if (useLayerNorm)
     {
         if (!useCifg)
@@ -204,190 +204,49 @@ void RefLstmWorkload::Execute(std::vector<ITensorHandle*> inputs, std::vector<IT
         }
     }
 
-    if (!useLayerNorm)
-    {
-        // Initialize scratch buffers with bias.
-        if (!useCifg)
-        {
-            VectorBatchVectorAssign(*inputGateBiasTensor,
-                                    nCell, nBatch, *inputGateScratch);
-        }
-        VectorBatchVectorAssign(*forgetGateBiasTensor,
-                                nCell, nBatch, *forgetGateScratch);
-        VectorBatchVectorAssign(*cellBiasTensor,
-                                nCell, nBatch, *cellScratch);
-        VectorBatchVectorAssign(*outputGateBiasTensor,
-                                nCell, nBatch, *outputGateScratch);
-    }
-    else
-    {
-        // Initialize scratch buffers with zeroes.
-        if (!useCifg)
-        {
-            ZeroVector(*inputGateScratch, nCell * nBatch);
-        }
-        ZeroVector(*forgetGateScratch, nCell * nBatch);
-        ZeroVector(*cellScratch      , nCell * nBatch);
-        ZeroVector(*outputGateScratch, nCell * nBatch);
-    }
-
-    // For each batch and cell: compute input_weight * input.
-    if (!useCifg)
-    {
-        MatrixBatchVectorMultiplyAccumulate(*inputToInputWeightsTensor,
-                                            nCell, nInput, *inputData, nBatch, *inputGateScratch);
-    }
-    MatrixBatchVectorMultiplyAccumulate(*inputToForgetWeightsTensor,
-                                        nCell, nInput, *inputData, nBatch, *forgetGateScratch);
-    MatrixBatchVectorMultiplyAccumulate(*inputToCellWeightsTensor,
-                                        nCell, nInput, *inputData, nBatch, *cellScratch);
-    MatrixBatchVectorMultiplyAccumulate(*inputToOutputWeightsTensor,
-                                        nCell, nInput, *inputData, nBatch, *outputGateScratch);
-
-    // For each batch and cell: compute recurrent_weight * output_state.
-    if (!useCifg)
-    {
-        MatrixBatchVectorMultiplyAccumulate(*recurrentToInputWeightsTensor,
-                                            nCell, nOutput, *outputStateIn, nBatch, *inputGateScratch);
-    }
-    MatrixBatchVectorMultiplyAccumulate(*recurrentToForgetWeightsTensor,
-                                        nCell, nOutput, *outputStateIn, nBatch, *forgetGateScratch);
-    MatrixBatchVectorMultiplyAccumulate(*recurrentToCellWeightsTensor,
-                                        nCell, nOutput, *outputStateIn, nBatch, *cellScratch);
-    MatrixBatchVectorMultiplyAccumulate(*recurrentToOutputWeightsTensor,
-                                        nCell, nOutput, *outputStateIn, nBatch, *outputGateScratch);
-
-    // For each batch and cell: update input gate.
-    if (!useCifg)
-    {
-        if (usePeephole)
-        {
-            VectorBatchVectorCwiseProductAccumulate(*cellToInputWeightsTensor,
-                                                    nCell, *cellStateIn, nBatch, *inputGateScratch);
-        }
-        if (useLayerNorm)
-        {
-            MeanStddevNormalization(*inputGateScratchDecoder,
-                                    *inputGateScratch, nCell, nBatch, m_LayerNormEpsilon);
-            VectorBatchVectorCwiseProduct(*inputLayerNormWeights,
-                                          nCell, *inputGateScratchDecoder, nBatch, *inputGateScratch);
-            VectorBatchVectorAdd(*inputGateBiasTensor,
-                                 nCell, *inputGateScratchDecoder, nBatch, *inputGateScratch);
-        }
-        Activation(*inputGateScratchDecoder, *inputGateScratch,
-                   TensorInfo({nCell, nBatch}, outputType),
-                   ActivationFunction::Sigmoid, 0, 0);
-    }
-
-    // For each batch and cell: update forget gate.
-    if (usePeephole)
-    {
-        VectorBatchVectorCwiseProductAccumulate(*cellToForgetWeightsTensor, nCell,
-                                                *cellStateIn, nBatch, *forgetGateScratch);
-    }
-    if (useLayerNorm)
-    {
-        MeanStddevNormalization(*forgetGateScratchDecoder,
-                                *forgetGateScratch, nCell, nBatch, m_LayerNormEpsilon);
-        VectorBatchVectorCwiseProduct(*forgetLayerNormWeights,
-                                      nCell, *forgetGateScratchDecoder, nBatch, *forgetGateScratch);
-        VectorBatchVectorAdd(*forgetGateBiasTensor,
-                             nCell, *forgetGateScratchDecoder, nBatch, *forgetGateScratch);
-    }
-    Activation(*forgetGateScratchDecoder, *forgetGateScratch,
-               TensorInfo({nCell, nBatch}, outputType),
-               ActivationFunction::Sigmoid, 0, 0);
-
-    // For each batch and cell: update the cell.
-    if (useLayerNorm)
-    {
-        MeanStddevNormalization(*cellScratchDecoder,
-                                *cellScratch, nCell, nBatch, m_LayerNormEpsilon);
-        VectorBatchVectorCwiseProduct(*cellLayerNormWeights,
-                                      nCell, *cellScratchDecoder, nBatch, *cellScratch);
-        VectorBatchVectorAdd(*cellBiasTensor,
-                             nCell, *cellScratchDecoder, nBatch, *cellScratch);
-    }
-
-    VectorVectorCwiseProduct(*forgetGateScratchDecoder, *cellStateIn, nBatch * nCell, *cellStateOut);
-
-    ActivationFunction armnnActivationFunc = ActivationFunction::Sigmoid;
-    float a = 0;
-    float b = 0;
-    SetActivationParameters(m_Data.m_Parameters.m_ActivationFunc, armnnActivationFunc, a, b);
-
-    if (m_Data.m_Parameters.m_ActivationFunc > 0)
-    {
-        Activation(*cellScratchDecoder, *cellScratch,
-                   TensorInfo({nCell, nBatch}, outputType),
-                   armnnActivationFunc, a, b);
-    }
-    if (useCifg)
-    {
-        Sub1Vector(*forgetGateScratchDecoder, nBatch * nCell, *forgetGateScratch);
-        VectorVectorCwiseProductAccumulate(
-            *cellScratchDecoder, *forgetGateScratchDecoder, nBatch * nCell, *cellStateOut);
-    }
-    else
-    {
-        VectorVectorCwiseProductAccumulate(
-            *cellScratchDecoder, *inputGateScratchDecoder, nBatch * nCell, *cellStateOut);
-    }
-    if (m_Data.m_Parameters.m_ClippingThresCell > 0.0)
-    {
-        ClipVector(*cellStateOutDecoder, nBatch * nCell, m_Data.m_Parameters.m_ClippingThresCell, *cellStateOut);
-    }
-
-    // For each batch and cell: update the output gate.
-    if (usePeephole)
-    {
-        VectorBatchVectorCwiseProductAccumulate(*cellToOutputWeightsTensor,
-                                                nCell, *cellStateOutDecoder, nBatch, *outputGateScratch);
-    }
-    if (useLayerNorm)
-    {
-        MeanStddevNormalization(*outputGateScratchDecoder,
-                                *outputGateScratch, nCell, nBatch, m_LayerNormEpsilon);
-        VectorBatchVectorCwiseProduct(*outputLayerNormWeights,
-                                      nCell, *outputGateScratchDecoder, nBatch, *outputGateScratch);
-        VectorBatchVectorAdd(*outputGateBiasTensor,
-                             nCell, *outputGateScratchDecoder, nBatch, *outputGateScratch);
-    }
-    Activation(*outputGateScratchDecoder, *outputGateScratch,
-               TensorInfo({nCell, nBatch}, outputType),
-               ActivationFunction::Sigmoid, 0, 0);
-
-    if (m_Data.m_Parameters.m_ActivationFunc > 0)
-    {
-        Activation(*cellStateOutDecoder, *cellScratch,
-                   TensorInfo({nCell, nBatch}, outputType),
-                   armnnActivationFunc, a, b);
-    }
-
-    VectorVectorCwiseProduct(*outputGateScratchDecoder, *cellScratchDecoder, nBatch * nCell, *outputGateScratch);
-
-    // For each batch: update the projection and output_state.
-    if (m_Data.m_Parameters.m_ProjectionEnabled)
-    {
-        if (m_ProjectionBiasTensor)
-        {
-            VectorBatchVectorAssign(*projectionBiasTensor,
-                                    nOutput, nBatch, *output);
-        }
-        MatrixBatchVectorMultiplyAccumulate(*projectionWeightsTensor,
-                                            nOutput, nCell, *outputGateScratchDecoder, nBatch, *output);
-
-        if (m_Data.m_Parameters.m_ClippingThresProj > 0.0)
-        {
-            ClipVector(*outputDecoder, nBatch * nOutput, m_Data.m_Parameters.m_ClippingThresProj, *output);
-        }
-    }
-    else
-    {
-        CopyVector(*outputGateScratchDecoder, nBatch * nOutput, *output);
-    }
-
-    CopyVector(*outputDecoder, nBatch * nOutput, *outputStateOut);
+    LstmImpl(m_Data.m_Parameters,
+                 inputInfo,
+                 outputInfo,
+                 inputToOutputWeightsShape,
+                 recurrentToOutputWeightsShape,
+                 inputData,
+                 outputStateIn,
+                 cellStateIn,
+                 outputStateOut,
+                 cellStateOut,
+                 output,
+                 cellStateOutDecoder,
+                 outputDecoder,
+                 inputToInputWeightsTensor,
+                 inputToForgetWeightsTensor,
+                 inputToCellWeightsTensor,
+                 inputToOutputWeightsTensor,
+                 recurrentToInputWeightsTensor,
+                 recurrentToForgetWeightsTensor,
+                 recurrentToCellWeightsTensor,
+                 recurrentToOutputWeightsTensor,
+                 cellToInputWeightsTensor,
+                 cellToForgetWeightsTensor,
+                 cellToOutputWeightsTensor,
+                 inputGateBiasTensor,
+                 forgetGateBiasTensor,
+                 cellBiasTensor,
+                 outputGateBiasTensor,
+                 projectionWeightsTensor,
+                 projectionBiasTensor,
+                 inputLayerNormWeights,
+                 forgetLayerNormWeights,
+                 cellLayerNormWeights,
+                 outputLayerNormWeights,
+                 inputGateScratch,
+                 cellScratch,
+                 forgetGateScratch,
+                 outputGateScratch,
+                 inputGateScratchDecoder,
+                 cellScratchDecoder,
+                 forgetGateScratchDecoder,
+                 outputGateScratchDecoder,
+                 m_LayerNormEpsilon);
 }
 
 } //namespace armnn
diff --git a/src/backends/reference/workloads/RefUnidirectionalSequenceLstmWorkload.cpp b/src/backends/reference/workloads/RefUnidirectionalSequenceLstmWorkload.cpp
new file mode 100644
index 0000000000..311fa18f91
--- /dev/null
+++ b/src/backends/reference/workloads/RefUnidirectionalSequenceLstmWorkload.cpp
@@ -0,0 +1,307 @@
+//
+// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "RefUnidirectionalSequenceLstmWorkload.hpp"
+#include "Activation.hpp"
+#include "Encoders.hpp"
+#include "Decoders.hpp"
+#include "Lstm.hpp"
+#include "LstmUtils.hpp"
+#include "RefWorkloadUtils.hpp"
+
+#include <armnnUtils/Permute.hpp>
+
+namespace armnn
+{
+
+RefUnidirectionalSequenceLstmWorkload::RefUnidirectionalSequenceLstmWorkload(
+    const UnidirectionalSequenceLstmQueueDescriptor& descriptor,
+    const WorkloadInfo& info)
+    : BaseWorkload<UnidirectionalSequenceLstmQueueDescriptor>(descriptor, info)
+    , m_InputToInputWeightsTensor     (AssignScopedTensorHandle(descriptor.m_InputToInputWeights))
+    , m_InputToForgetWeightsTensor    (AssignScopedTensorHandle(descriptor.m_InputToForgetWeights))
+    , m_InputToCellWeightsTensor      (AssignScopedTensorHandle(descriptor.m_InputToCellWeights))
+    , m_InputToOutputWeightsTensor    (AssignScopedTensorHandle(descriptor.m_InputToOutputWeights))
+    , m_RecurrentToInputWeightsTensor (AssignScopedTensorHandle(descriptor.m_RecurrentToInputWeights))
+    , m_RecurrentToForgetWeightsTensor(AssignScopedTensorHandle(descriptor.m_RecurrentToForgetWeights))
+    , m_RecurrentToCellWeightsTensor  (AssignScopedTensorHandle(descriptor.m_RecurrentToCellWeights))
+    , m_RecurrentToOutputWeightsTensor(AssignScopedTensorHandle(descriptor.m_RecurrentToOutputWeights))
+    , m_CellToInputWeightsTensor      (AssignScopedTensorHandle(descriptor.m_CellToInputWeights))
+    , m_CellToForgetWeightsTensor     (AssignScopedTensorHandle(descriptor.m_CellToForgetWeights))
+    , m_CellToOutputWeightsTensor     (AssignScopedTensorHandle(descriptor.m_CellToOutputWeights))
+    , m_InputGateBiasTensor           (AssignScopedTensorHandle(descriptor.m_InputGateBias))
+    , m_ForgetGateBiasTensor          (AssignScopedTensorHandle(descriptor.m_ForgetGateBias))
+    , m_CellBiasTensor                (AssignScopedTensorHandle(descriptor.m_CellBias))
+    , m_OutputGateBiasTensor          (AssignScopedTensorHandle(descriptor.m_OutputGateBias))
+    , m_ProjectionWeightsTensor       (AssignScopedTensorHandle(descriptor.m_ProjectionWeights))
+    , m_ProjectionBiasTensor          (AssignScopedTensorHandle(descriptor.m_ProjectionBias))
+    , m_InputLayerNormWeights         (AssignScopedTensorHandle(descriptor.m_InputLayerNormWeights))
+    , m_ForgetLayerNormWeights        (AssignScopedTensorHandle(descriptor.m_ForgetLayerNormWeights))
+    , m_CellLayerNormWeights          (AssignScopedTensorHandle(descriptor.m_CellLayerNormWeights))
+    , m_OutputLayerNormWeights        (AssignScopedTensorHandle(descriptor.m_OutputLayerNormWeights))
+{}
+
+void RefUnidirectionalSequenceLstmWorkload::Execute() const
+{
+    Execute(m_Data.m_Inputs, m_Data.m_Outputs);
+}
+
+void RefUnidirectionalSequenceLstmWorkload::ExecuteAsync(WorkingMemDescriptor& workingMemDescriptor)
+{
+    Execute(workingMemDescriptor.m_Inputs, workingMemDescriptor.m_Outputs);
+}
+
+void RefUnidirectionalSequenceLstmWorkload::Execute(std::vector<ITensorHandle*> inputs,
+                                                    std::vector<ITensorHandle*> outputs) const
+{
+    TensorInfo inputInfo = GetTensorInfo(inputs[0]);
+    const TensorInfo& outputStateInfo = GetTensorInfo(inputs[1]);
+    const TensorInfo& cellStateInfo = GetTensorInfo(inputs[2]);
+    TensorInfo outputInfo = GetTensorInfo(outputs[0]);
+    TensorShape& inputShape = inputInfo.GetShape();
+    TensorShape& outputShape= outputInfo.GetShape();
+    auto inputTensor = reinterpret_cast<float*>(inputs[0]->Map());
+
+    if (!m_Data.m_Parameters.m_TimeMajor)
+    {
+        // Permute to time major
+        const PermutationVector& mappings = {1U, 0U, 2U};
+        std::vector<float> inputValue(inputTensor, inputTensor + inputInfo.GetNumElements());
+        inputShape = armnnUtils::Permuted(inputInfo.GetShape(), mappings);
+        inputInfo.SetShape(inputShape);
+        armnnUtils::Permute(inputShape, mappings,  inputValue.data(), inputTensor, sizeof(float));
+
+        outputShape = armnnUtils::Permuted(outputInfo.GetShape(), mappings);
+        outputInfo.SetShape(outputShape);
+    }
+    unsigned int maxTime = inputShape[0];
+    unsigned int batchSize = inputShape[1];
+    unsigned int outputSize = outputShape[2];
+    unsigned int inputSize = inputShape[2];
+
+    TensorInfo scratchInfo = outputInfo;
+    scratchInfo.SetShape({batchSize, cellStateInfo.GetShape()[1]});
+
+    std::vector<float> inputGateScratchBuffer;
+    std::vector<float> cellScratchBuffer(scratchInfo.GetNumElements(), 0.);
+    std::vector<float> forgetGateScratchBuffer(scratchInfo.GetNumElements(), 0.);
+    std::vector<float> outputGateScratchBuffer(scratchInfo.GetNumElements(), 0.);
+
+    std::vector<float> outputStateOutBuffer(outputStateInfo.GetNumElements(), 0.);
+    std::vector<float> cellStateOutBuffer(cellStateInfo.GetNumElements(), 0.);
+
+    void* outputStateOutData = outputStateOutBuffer.data();
+    void* cellStateOutData = cellStateOutBuffer.data();
+
+    std::unique_ptr<Encoder<float>> inputGateScratch;
+    std::unique_ptr<Encoder<float>> cellScratch = MakeEncoder<float>(scratchInfo, cellScratchBuffer.data());
+    std::unique_ptr<Encoder<float>> forgetGateScratch = MakeEncoder<float>(scratchInfo, forgetGateScratchBuffer.data());
+    std::unique_ptr<Encoder<float>> outputGateScratch = MakeEncoder<float>(scratchInfo, outputGateScratchBuffer.data());
+
+    std::unique_ptr<Decoder<float>> inputGateScratchDecoder;
+    std::unique_ptr<Decoder<float>> cellScratchDecoder = MakeDecoder<float>(scratchInfo, cellScratchBuffer.data());
+    std::unique_ptr<Decoder<float>> forgetGateScratchDecoder = MakeDecoder<float>(scratchInfo,
+                                                                                  forgetGateScratchBuffer.data());
+    std::unique_ptr<Decoder<float>> outputGateScratchDecoder = MakeDecoder<float>(scratchInfo,
+                                                                                  outputGateScratchBuffer.data());
+
+    const bool useCifg      = m_Data.m_Parameters.m_CifgEnabled;
+    const bool usePeephole  = m_Data.m_Parameters.m_PeepholeEnabled;
+    const bool useLayerNorm = m_Data.m_Parameters.m_LayerNormEnabled;
+
+    if (!useCifg)
+    {
+        inputGateScratchBuffer.resize(scratchInfo.GetNumElements(), 0.);
+        inputGateScratch = MakeEncoder<float>(scratchInfo, inputGateScratchBuffer.data());
+        inputGateScratchDecoder = MakeDecoder<float>(scratchInfo, inputGateScratchBuffer.data());
+    }
+
+    std::unique_ptr<Encoder<float>> outputStateOut = MakeEncoder<float>(outputStateInfo, outputStateOutData);
+    std::unique_ptr<Encoder<float>> cellStateOut   = MakeEncoder<float>(cellStateInfo, cellStateOutData);
+    std::unique_ptr<Decoder<float>> cellStateOutDecoder = MakeDecoder<float>(cellStateInfo, cellStateOutData);
+
+    TensorInfo lstmInputInfo = inputInfo;
+    TensorShape batchInputShape = TensorShape({batchSize, inputSize});
+    lstmInputInfo.SetShape(batchInputShape);
+
+    TensorInfo lstmOutputInfo = outputInfo;
+    lstmOutputInfo.SetShape({batchSize, outputSize});
+
+    const TensorShape& inputToOutputWeightsShape = m_InputToOutputWeightsTensor->GetShape();
+    const TensorShape& recurrentToOutputWeightsShape = m_RecurrentToOutputWeightsTensor->GetShape();
+    unsigned int nOutput = recurrentToOutputWeightsShape[1];
+    auto outputStateInData = inputs[1]->Map();
+    std::unique_ptr<Decoder<float>> outputStateIn = MakeDecoder<float>(outputStateInfo, outputStateInData);
+
+    auto cellStateInData = inputs[2]->Map();
+    std::unique_ptr<Decoder<float>> cellStateIn = MakeDecoder<float>(cellStateInfo, cellStateInData);
+
+    auto currentInputData = reinterpret_cast<float*>(inputs[0]->Map());
+    std::unique_ptr<Decoder<float>> inputData = MakeDecoder<float>(lstmInputInfo, currentInputData);
+    auto currentOutputData = reinterpret_cast<float*>(outputs[0]->Map());
+    std::unique_ptr<Encoder<float>> output = MakeEncoder<float>(lstmOutputInfo, currentOutputData);
+    std::unique_ptr<Decoder<float>> outputDecoder = MakeDecoder<float>(lstmOutputInfo, currentOutputData);
+
+    std::unique_ptr<Decoder<float>> inputToInputWeightsTensor;
+    std::unique_ptr<Decoder<float>> inputToForgetWeightsTensor = MakeDecoder<float>(
+        m_InputToForgetWeightsTensor->GetTensorInfo(), m_InputToForgetWeightsTensor->GetConstTensor<void>());
+    std::unique_ptr<Decoder<float>> inputToCellWeightsTensor = MakeDecoder<float>(
+        m_InputToCellWeightsTensor->GetTensorInfo(), m_InputToCellWeightsTensor->GetConstTensor<void>());
+    std::unique_ptr<Decoder<float>> inputToOutputWeightsTensor = MakeDecoder<float>(
+        m_InputToOutputWeightsTensor->GetTensorInfo(), m_InputToOutputWeightsTensor->GetConstTensor<void>());
+
+    std::unique_ptr<Decoder<float>> recurrentToInputWeightsTensor;
+    std::unique_ptr<Decoder<float>> recurrentToForgetWeightsTensor = MakeDecoder<float>(
+        m_RecurrentToForgetWeightsTensor->GetTensorInfo(), m_RecurrentToForgetWeightsTensor->GetConstTensor<void>());
+    std::unique_ptr<Decoder<float>> recurrentToCellWeightsTensor = MakeDecoder<float>(
+        m_RecurrentToCellWeightsTensor->GetTensorInfo(), m_RecurrentToCellWeightsTensor->GetConstTensor<void>());
+    std::unique_ptr<Decoder<float>> recurrentToOutputWeightsTensor = MakeDecoder<float>(
+        m_RecurrentToOutputWeightsTensor->GetTensorInfo(), m_RecurrentToOutputWeightsTensor->GetConstTensor<void>());
+
+    std::unique_ptr<Decoder<float>> inputGateBiasTensor;
+    std::unique_ptr<Decoder<float>> forgetGateBiasTensor = MakeDecoder<float>(
+        m_ForgetGateBiasTensor->GetTensorInfo(), m_ForgetGateBiasTensor->GetConstTensor<void>());
+    std::unique_ptr<Decoder<float>> cellBiasTensor = MakeDecoder<float>(
+        m_CellBiasTensor->GetTensorInfo(), m_CellBiasTensor->GetConstTensor<void>());
+    std::unique_ptr<Decoder<float>> outputGateBiasTensor = MakeDecoder<float>(
+        m_OutputGateBiasTensor->GetTensorInfo(), m_OutputGateBiasTensor->GetConstTensor<void>());
+
+    std::unique_ptr<Decoder<float>> cellToInputWeightsTensor;
+    std::unique_ptr<Decoder<float>> cellToForgetWeightsTensor;
+    std::unique_ptr<Decoder<float>> cellToOutputWeightsTensor;
+
+    std::unique_ptr<Decoder<float>> projectionWeightsTensor;
+    std::unique_ptr<Decoder<float>> projectionBiasTensor;
+
+    std::unique_ptr<Decoder<float>> inputLayerNormWeights;
+    std::unique_ptr<Decoder<float>> forgetLayerNormWeights;
+    std::unique_ptr<Decoder<float>> cellLayerNormWeights;
+    std::unique_ptr<Decoder<float>> outputLayerNormWeights;
+
+    if (useLayerNorm)
+    {
+        if (!useCifg)
+        {
+            inputLayerNormWeights = MakeDecoder<float>(
+                    m_InputLayerNormWeights->GetTensorInfo(), m_InputLayerNormWeights->GetConstTensor<void>());
+        }
+        forgetLayerNormWeights = MakeDecoder<float>(
+                m_ForgetLayerNormWeights->GetTensorInfo(), m_ForgetLayerNormWeights->GetConstTensor<void>());
+        cellLayerNormWeights = MakeDecoder<float>(
+                m_CellLayerNormWeights->GetTensorInfo(), m_CellLayerNormWeights->GetConstTensor<void>());
+        outputLayerNormWeights = MakeDecoder<float>(
+                m_OutputLayerNormWeights->GetTensorInfo(), m_OutputLayerNormWeights->GetConstTensor<void>());
+    }
+
+    if (!useCifg)
+    {
+        inputToInputWeightsTensor = MakeDecoder<float>(
+            m_InputToInputWeightsTensor->GetTensorInfo(), m_InputToInputWeightsTensor->GetConstTensor<void>());
+        inputGateBiasTensor = MakeDecoder<float>(
+            m_InputGateBiasTensor->GetTensorInfo(), m_InputGateBiasTensor->GetConstTensor<void>());
+        recurrentToInputWeightsTensor = MakeDecoder<float>(
+            m_RecurrentToInputWeightsTensor->GetTensorInfo(), m_RecurrentToInputWeightsTensor->GetConstTensor<void>());
+    }
+
+    if (usePeephole)
+    {
+        cellToForgetWeightsTensor = MakeDecoder<float>(
+            m_CellToForgetWeightsTensor->GetTensorInfo(), m_CellToForgetWeightsTensor->GetConstTensor<void>());
+        cellToOutputWeightsTensor = MakeDecoder<float>(
+            m_CellToOutputWeightsTensor->GetTensorInfo(), m_CellToOutputWeightsTensor->GetConstTensor<void>());
+    }
+
+    if (!useCifg && usePeephole)
+    {
+        cellToInputWeightsTensor = MakeDecoder<float>(
+            m_CellToInputWeightsTensor->GetTensorInfo(), m_CellToInputWeightsTensor->GetConstTensor<void>());
+    }
+
+    if (m_Data.m_Parameters.m_ProjectionEnabled)
+    {
+        projectionWeightsTensor = MakeDecoder<float>(
+            m_ProjectionWeightsTensor->GetTensorInfo(), m_ProjectionWeightsTensor->GetConstTensor<void>());
+        if (m_ProjectionBiasTensor)
+        {
+            projectionBiasTensor = MakeDecoder<float>(
+                m_ProjectionBiasTensor->GetTensorInfo(), m_ProjectionBiasTensor->GetConstTensor<void>());
+        }
+    }
+
+    unsigned int batchInputSize = batchSize * inputSize;
+    unsigned int batchOutputSize = batchSize * nOutput;
+
+    for (unsigned int t = 0; t < maxTime; ++t)
+    {
+        LstmImpl(m_Data.m_Parameters,
+                 lstmInputInfo,
+                 lstmOutputInfo,
+                 inputToOutputWeightsShape,
+                 recurrentToOutputWeightsShape,
+                 inputData,
+                 outputStateIn,
+                 cellStateIn,
+                 outputStateOut,
+                 cellStateOut,
+                 output,
+                 cellStateOutDecoder,
+                 outputDecoder,
+                 inputToInputWeightsTensor,
+                 inputToForgetWeightsTensor,
+                 inputToCellWeightsTensor,
+                 inputToOutputWeightsTensor,
+                 recurrentToInputWeightsTensor,
+                 recurrentToForgetWeightsTensor,
+                 recurrentToCellWeightsTensor,
+                 recurrentToOutputWeightsTensor,
+                 cellToInputWeightsTensor,
+                 cellToForgetWeightsTensor,
+                 cellToOutputWeightsTensor,
+                 inputGateBiasTensor,
+                 forgetGateBiasTensor,
+                 cellBiasTensor,
+                 outputGateBiasTensor,
+                 projectionWeightsTensor,
+                 projectionBiasTensor,
+                 inputLayerNormWeights,
+                 forgetLayerNormWeights,
+                 cellLayerNormWeights,
+                 outputLayerNormWeights,
+                 inputGateScratch,
+                 cellScratch,
+                 forgetGateScratch,
+                 outputGateScratch,
+                 inputGateScratchDecoder,
+                 cellScratchDecoder,
+                 forgetGateScratchDecoder,
+                 outputGateScratchDecoder,
+                 m_LayerNormEpsilon);
+
+        currentInputData += batchInputSize;
+        inputData = MakeDecoder<float>(lstmInputInfo, currentInputData);
+        currentOutputData += batchOutputSize;
+        output = MakeEncoder<float>(lstmOutputInfo, currentOutputData);
+        outputDecoder = MakeDecoder<float>(lstmOutputInfo, currentOutputData);
+
+        // Assign output state out to the next output state in
+        outputStateIn = MakeDecoder<float>(outputStateInfo, outputStateOutData);
+
+        // Assign cell state out to the next cell state in
+        cellStateIn = MakeDecoder<float>(cellStateInfo, cellStateOutData);
+    }
+
+    if (!m_Data.m_Parameters.m_TimeMajor)
+    {
+        // Permute Output back to batch major
+        const PermutationVector& mappings = {1U, 0U, 2U};
+        auto outputData = reinterpret_cast<float*>(outputs[0]->Map());
+        std::vector<float> outputValue(outputData, outputData + outputInfo.GetNumElements());
+        outputShape = armnnUtils::Permuted(outputInfo.GetShape(), mappings);
+        outputInfo.SetShape(outputShape);
+        armnnUtils::Permute(outputShape, mappings, outputValue.data(), outputData, sizeof(float));
+    }
+}
+
+} //namespace armnn
diff --git a/src/backends/reference/workloads/RefUnidirectionalSequenceLstmWorkload.hpp b/src/backends/reference/workloads/RefUnidirectionalSequenceLstmWorkload.hpp
new file mode 100644
index 0000000000..8ba7bdc0c6
--- /dev/null
+++ b/src/backends/reference/workloads/RefUnidirectionalSequenceLstmWorkload.hpp
@@ -0,0 +1,56 @@
+//
+// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include <armnn/TypesUtils.hpp>
+
+#include <backendsCommon/Workload.hpp>
+#include <backendsCommon/WorkloadData.hpp>
+
+#include "Encoders.hpp"
+#include "Decoders.hpp"
+
+namespace armnn
+{
+
+class RefUnidirectionalSequenceLstmWorkload : public BaseWorkload<UnidirectionalSequenceLstmQueueDescriptor>
+{
+public:
+    explicit RefUnidirectionalSequenceLstmWorkload(const UnidirectionalSequenceLstmQueueDescriptor& descriptor,
+                                                   const WorkloadInfo& info);
+
+    void Execute() const override;
+    void ExecuteAsync(WorkingMemDescriptor& workingMemDescriptor)  override;
+
+
+private:
+    void Execute(std::vector<ITensorHandle*> inputs, std::vector<ITensorHandle*> outputs) const;
+    std::unique_ptr<ScopedTensorHandle> m_InputToInputWeightsTensor;
+    std::unique_ptr<ScopedTensorHandle> m_InputToForgetWeightsTensor;
+    std::unique_ptr<ScopedTensorHandle> m_InputToCellWeightsTensor;
+    std::unique_ptr<ScopedTensorHandle> m_InputToOutputWeightsTensor;
+    std::unique_ptr<ScopedTensorHandle> m_RecurrentToInputWeightsTensor;
+    std::unique_ptr<ScopedTensorHandle> m_RecurrentToForgetWeightsTensor;
+    std::unique_ptr<ScopedTensorHandle> m_RecurrentToCellWeightsTensor;
+    std::unique_ptr<ScopedTensorHandle> m_RecurrentToOutputWeightsTensor;
+    std::unique_ptr<ScopedTensorHandle> m_CellToInputWeightsTensor;
+    std::unique_ptr<ScopedTensorHandle> m_CellToForgetWeightsTensor;
+    std::unique_ptr<ScopedTensorHandle> m_CellToOutputWeightsTensor;
+    std::unique_ptr<ScopedTensorHandle> m_InputGateBiasTensor;
+    std::unique_ptr<ScopedTensorHandle> m_ForgetGateBiasTensor;
+    std::unique_ptr<ScopedTensorHandle> m_CellBiasTensor;
+    std::unique_ptr<ScopedTensorHandle> m_OutputGateBiasTensor;
+    std::unique_ptr<ScopedTensorHandle> m_ProjectionWeightsTensor;
+    std::unique_ptr<ScopedTensorHandle> m_ProjectionBiasTensor;
+    std::unique_ptr<ScopedTensorHandle> m_InputLayerNormWeights;
+    std::unique_ptr<ScopedTensorHandle> m_ForgetLayerNormWeights;
+    std::unique_ptr<ScopedTensorHandle> m_CellLayerNormWeights;
+    std::unique_ptr<ScopedTensorHandle> m_OutputLayerNormWeights;
+
+    float m_LayerNormEpsilon = static_cast<float>(1e-8);
+};
+
+} //namespace armnn
diff --git a/src/backends/reference/workloads/RefWorkloads.hpp b/src/backends/reference/workloads/RefWorkloads.hpp
index afe63d13c0..d3ae58ea15 100644
--- a/src/backends/reference/workloads/RefWorkloads.hpp
+++ b/src/backends/reference/workloads/RefWorkloads.hpp
@@ -69,6 +69,7 @@
 #include "RefSpaceToDepthWorkload.hpp"
 #include "RefTransposeConvolution2dWorkload.hpp"
 #include "RefTransposeWorkload.hpp"
+#include "RefUnidirectionalSequenceLstmWorkload.hpp"
 #include "RefWorkloadUtils.hpp"
 #include "Resize.hpp"
 #include "Softmax.hpp"
-- 
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