COMPMID-3236: Implement CLQLSTMLayer

COMPMID-3081: Extend CLQLSTMLayer with enhancements

Change-Id: Idb7aaaacdba957e5ad61e94edeab2e898730a109
Signed-off-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/3057
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Sang-Hoon Park <sang-hoon.park@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

12 files changed, 1220 insertions, 18 deletions

diff --git a/Android.bp b/Android.bp
index b53c46482a..d2ac397395 100644
--- a/Android.bp
+++ b/Android.bp
@@ -530,6 +530,7 @@ cc_library_static {
         "src/runtime/CL/functions/CLPixelWiseMultiplication.cpp",
         "src/runtime/CL/functions/CLPoolingLayer.cpp",
         "src/runtime/CL/functions/CLPriorBoxLayer.cpp",
+        "src/runtime/CL/functions/CLQLSTMLayer.cpp",
         "src/runtime/CL/functions/CLQuantizationLayer.cpp",
         "src/runtime/CL/functions/CLRNNLayer.cpp",
         "src/runtime/CL/functions/CLROIAlignLayer.cpp",
diff --git a/arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.h b/arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.h
index cc3c5f5186..7beb5bb1c6 100644
--- a/arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.h
+++ b/arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.h
@@ -52,7 +52,7 @@ public:
     /** Initialise the kernel's input and output.
      *
      * @param[in]  input0             Input tensor containing the LHS matrix. Data type supported: QASYMM8/QASYMM8_SIGNED
-     * @param[in]  input1             Input tensor containing the RHS reshaped matrix. Data type supported: same as @p input0
+     * @param[in]  input1             Input tensor containing the RHS reshaped matrix. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8/QSYMM8_PER_CHANNEL
      * @param[out] output             Output tensor. Data type supported: QASYMM8/QASYMM8_SIGNED/S32.
      * @param[in]  gemm_info          GEMM information used to retrieve the original dimensions of the input matrices, output stage information and RHS/LHS info.
      *                                Only the following values are supported for LHS info:
@@ -105,7 +105,7 @@ public:
     /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel
      *
      * @param[in] input0             Input tensor info for the LHS matrix. Data type supported: QASYMM8/QASYMM8_SIGNED
-     * @param[in] input1             Input tensor info for the RHS reshaped matrix. Data type supported: same as @p input0
+     * @param[in] input1             Input tensor info for the RHS reshaped matrix. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8/QSYMM8_PER_CHANNEL
      * @param[in] output             Output tensor info. Data type supported: QASYMM8/QASYMM8_SIGNED/S32.
      * @param[in] gemm_info          GEMM information used to retrieve the original dimensions of the input matrices, output stage information and RHS/LHS info.
      *                               Only the following values are supported for LHS info:
diff --git a/arm_compute/runtime/CL/CLFunctions.h b/arm_compute/runtime/CL/CLFunctions.h
index a054ed13c3..007a40c651 100644
--- a/arm_compute/runtime/CL/CLFunctions.h
+++ b/arm_compute/runtime/CL/CLFunctions.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2016-2019 ARM Limited.
+ * Copyright (c) 2016-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -116,6 +116,7 @@
 #include "arm_compute/runtime/CL/functions/CLPixelWiseMultiplication.h"
 #include "arm_compute/runtime/CL/functions/CLPoolingLayer.h"
 #include "arm_compute/runtime/CL/functions/CLPriorBoxLayer.h"
+#include "arm_compute/runtime/CL/functions/CLQLSTMLayer.h"
 #include "arm_compute/runtime/CL/functions/CLQuantizationLayer.h"
 #include "arm_compute/runtime/CL/functions/CLRNNLayer.h"
 #include "arm_compute/runtime/CL/functions/CLROIAlignLayer.h"
diff --git a/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h b/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h
index b147001820..1d7013d328 100644
--- a/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h
+++ b/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h
@@ -65,7 +65,7 @@ public:
      *  -# Quantize to uint8 if gemm_info.gemmlowp_output_stage != NONE
      *
      * @param[in]  a         First input tensor  (Matrix A). Data type supported: QASYMM8/QASYMM8_SIGNED.
-     * @param[in]  b         Second input tensor (Matrix B). Data type supported: same as @p a
+     * @param[in]  b         Second input tensor (Matrix B). Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8/QSYMM8_PER_CHANNEL
      * @param[in]  c         Third input tensor  (Matrix C). It can be a nullptr. Data type supported: S32
      * @param[out] output    Output tensor. Data type supported: S32 or QASYMM8/QASYMM8_SIGNED if gemm_info.gemmlowp_output_stage != NONE
      * @param[in]  gemm_info (Optional) Specifies if the matrix A and/or matrix B have been reshaped and
@@ -75,7 +75,7 @@ public:
     /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpMatrixMultiplyCore
      *
      * @param[in] a         First input tensor info (Matrix A). Data type supported: QASYMM8.
-     * @param[in] b         Second input tensor info (Matrix B). Data type supported: same as @p a
+     * @param[in] b         Second input tensor info (Matrix B). Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8/QSYMM8_PER_CHANNEL
      * @param[in] c         Third input tensor info (Matrix C). It can be a nullptr. Data type supported: S32
      * @param[in] output    Output tensor info. Data type supported: S32 or QASYMM8/QASYMM8_SIGNED if gemm_info.gemmlowp_output_stage != NONE
      * @param[in] gemm_info (Optional) Specifies if the matrix A and/or matrix B have been reshaped and
diff --git a/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h b/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h
index 05cffa6680..4c11e51950 100644
--- a/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h
+++ b/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h
@@ -322,6 +322,7 @@ public:
  * -# @ref CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel
  * -# @ref CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel
  * -# @ref CLGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel
+ * -# @ref CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel
 */
 class CLGEMMLowpOutputStage : public ICLSimpleFunction
 {
diff --git a/arm_compute/runtime/CL/functions/CLQLSTMLayer.h b/arm_compute/runtime/CL/functions/CLQLSTMLayer.h
new file mode 100644
index 0000000000..ab34135ff5
--- /dev/null
+++ b/arm_compute/runtime/CL/functions/CLQLSTMLayer.h
@@ -0,0 +1,330 @@
+/*
+ * Copyright (c) 2020 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef ARM_COMPUTE_CLQLSTMLAYER_H
+#define ARM_COMPUTE_CLQLSTMLAYER_H
+
+#include "arm_compute/core/CL/kernels/CLElementwiseOperationKernel.h"
+#include "arm_compute/core/CL/kernels/CLGEMMLowpReductionKernel.h"
+#include "arm_compute/core/CL/kernels/CLPixelWiseMultiplicationKernel.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/runtime/CL/functions/CLActivationLayer.h"
+#include "arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h"
+#include "arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h"
+#include "arm_compute/runtime/CL/functions/CLTranspose.h"
+
+#include "arm_compute/runtime/common/LSTMParams.h"
+
+namespace arm_compute
+{
+// Forward declarations
+class ICLTensor;
+
+/** Basic function to run @ref CLQLSTMLayer
+ *
+ * This function calls the following CL functions/kernels:
+ *
+ * -# @ref CLActivationLayer                                     Activation functions (tanh and logistic)
+ * -# @ref CLSaturatedArithmeticOperationKernel                  Elementwise addition and subtraction
+ * -# @ref CLGEMMLowpMatrixMultiplyCore                          Quantized matrix multiplication core. Accumulators are 32-bit integers
+ * -# @ref CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPoint   Convert 32-bit integers into QSYMM16
+ * -# @ref CLGEMMLowpMatrixAReductionKernel                      For precomputing effective biases to use
+ * -# @ref CLPixelWiseMultiplicationKernel                       Elementwise multiplication
+ * -# @ref CLTranspose                                           Transpose function for reshaping the weights
+ * */
+class CLQLSTMLayer : public IFunction
+{
+public:
+    /** Default constructor */
+    CLQLSTMLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    CLQLSTMLayer(const CLQLSTMLayer &) = delete;
+    /** Default move constructor */
+    CLQLSTMLayer(CLQLSTMLayer &&) = default;
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    CLQLSTMLayer &operator=(const CLQLSTMLayer &) = delete;
+    /** Default move assignment operator */
+    CLQLSTMLayer &operator=(CLQLSTMLayer &&) = default;
+    /** Initialize function's tensors.
+     *
+     * @param[in]  input                       Source tensor. Input is a 2D tensor with dimensions [input_size, batch_size]. Data types supported: QASYMM8_SIGNED.
+     * @param[in]  input_to_forget_weights     2D weights tensor with dimensions [input_size, num_units]. Data type supported: QSYMM8.
+     * @param[in]  input_to_cell_weights       2D weights tensor with dimensions [input_size, num_units]. Data type supported: QSYMM8.
+     * @param[in]  input_to_output_weights     2D weights tensor with dimensions [input_size, num_units]. Data type supported: QSYMM8.
+     * @param[in]  recurrent_to_forget_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8.
+     * @param[in]  recurrent_to_cell_weights   2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8.
+     * @param[in]  recurrent_to_output_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8.
+     * @param[in]  forget_gate_bias            1D weights tensor with dimensions [num_units]. Data type supported: S32.
+     * @param[in]  cell_bias                   1D weights tensor with dimensions [num_units]. Data type supported: S32.
+     * @param[in]  output_gate_bias            1D weights tensor with dimensions [num_units]. Data type supported: S32.
+     * @param[in]  cell_state_in               2D tensor with dimensions [output_size, batch_size]. Data type supported:  QSYMM16.
+     * @param[in]  output_state_in             2D tensor with dimensions [num_units, batch_size]. Data type supported: Same as @p input.
+     * @param[out] cell_state_out              Destination tensor. Output is a 2D tensor with dimensions [output_size, batch_size]. Data type supported:  QSYMM16.
+     * @param[out] output_state_out            Destination tensor. Output is a 2D tensor with dimensions [num_units, batch_size].Data types supported: Same as @p input.
+     * @param[in]  lstm_params                 Weights tensors used in peephole, CIFG and layer normalization optimizations:
+     *                                         input_intermediate_scale   Scale of the intermediate result of matmul, i.e. input to layer normalization, at input gate.
+     *                                         forget_intermediate_scale  Scale of the intermediate result of matmul, i.e. input to layer normalization, at forget gate.
+     *                                         cell_intermediate_scale    Scale of the intermediate result of matmul, i.e. input to layer normalization, at cell gate.
+     *                                         output_intermediate_scale  Scale of the intermediate result of matmul, i.e. input to layer normalization, at output gate.
+     *                                         hidden_state_zero          The zero point of the hidden state.
+     *                                         hidden_state_scale         The scale of the hidden state.
+     *                                         input_to_input_weights     (Optional) 2D weights tensor with dimensions [input_size, num_units]. Data type supported: QSYMM8.
+     *                                         recurrent_to_input_weights (Optional) 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8.
+     *                                         cell_to_input_weights      (Optional) 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: QSYMM16.
+     *                                         cell_to_forget_weights     (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
+     *                                         cell_to_output_weights     (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
+     *                                         input_gate_bias            (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: S32.
+     *                                         projection_weights         (Optional) 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8.
+     *                                         projection_bias            (Optional) 1D weights tensor with dimensions [output_size]. S32.
+     *                                         input_layer_norm_weights   (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
+     *                                         forget_layer_norm_weights  (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
+     *                                         cell_layer_norm_weights    (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
+     *                                         output_layer_norm_weights  (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
+     *                                         cell_threshold             (Optional) The clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip].
+     *                                                                               If set to 0.0 then clipping is disabled.
+     *                                         projection_threshold       (Optional) The clipping threshold for the output from the projection layer, such that values are bound within
+     *                                                                               [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
+     */
+    void configure(const ICLTensor *input,
+                   const ICLTensor *input_to_forget_weights, const ICLTensor *input_to_cell_weights, const ICLTensor *input_to_output_weights,
+                   const ICLTensor *recurrent_to_forget_weights, const ICLTensor *recurrent_to_cell_weights, const ICLTensor *recurrent_to_output_weights,
+                   const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias,
+                   const ICLTensor *cell_state_in, const ICLTensor *output_state_in,
+                   ICLTensor *cell_state_out, ICLTensor *output_state_out,
+                   const LSTMParams<ICLTensor> &lstm_params);
+
+    /** Static function to check if given info will lead to a valid configuration of @ref CLQLSTMLayer
+     *
+     * @param[in]  input                       Source tensor info. Input is a 2D tensor info with dimensions [input_size, batch_size]. Data types supported: QASYMM8_SIGNED.
+     * @param[in]  input_to_forget_weights     2D weights tensor info with dimensions [input_size, num_units]. Data type supported: QSYMM8.
+     * @param[in]  input_to_cell_weights       2D weights tensor info with dimensions [input_size, num_units]. Data type supported: QSYMM8.
+     * @param[in]  input_to_output_weights     2D weights tensor info with dimensions [input_size, num_units]. Data type supported: QSYMM8.
+     * @param[in]  recurrent_to_forget_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: QSYMM8.
+     * @param[in]  recurrent_to_cell_weights   2D weights tensor info with dimensions [output_size, num_units]. Data type supported: QSYMM8.
+     * @param[in]  recurrent_to_output_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: QSYMM8.
+     * @param[in]  forget_gate_bias            1D weights tensor info with dimensions [num_units]. Data type supported: S32.
+     * @param[in]  cell_bias                   1D weights tensor info with dimensions [num_units]. Data type supported: S32.
+     * @param[in]  output_gate_bias            1D weights tensor info with dimensions [num_units]. Data type supported: S32.
+     * @param[in]  cell_state_in               2D tensor info with dimensions [num_units, batch_size]. Data type supported:  QSYMM16.
+     * @param[in]  output_state_in             2D tensor info with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
+     * @param[out] cell_state_out              Destination tensor info. Output is a 2D tensor info with dimensions [num_units, batch_size]. Data type supported:  QSYMM16.
+     * @param[out] output_state_out            Destination tensor info. Output is a 2D tensor info with dimensions [output_size, batch_size].Data types supported: Same as @p input.
+     * @param[in]  lstm_params                 Weights tensors info used in peephole, CIFG and layer normalization optimizations:
+     *                                         input_intermediate_scale   Scale of the intermediate result of matmul, i.e. input to layer normalization, at input gate.
+     *                                         forget_intermediate_scale  Scale of the intermediate result of matmul, i.e. input to layer normalization, at forget gate.
+     *                                         cell_intermediate_scale    Scale of the intermediate result of matmul, i.e. input to layer normalization, at cell gate.
+     *                                         output_intermediate_scale  Scale of the intermediate result of matmul, i.e. input to layer normalization, at output gate.
+     *                                         hidden_state_zero          The zero point of the hidden state.
+     *                                         hidden_state_scale         The scale of the hidden state.
+     *                                         input_to_input_weights     (Optional) 2D weights tensor with dimensions [input_size, num_units]. Data type supported: QSYMM8.
+     *                                         recurrent_to_input_weights (Optional) 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8.
+     *                                         cell_to_input_weights      (Optional) 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: QSYMM16.
+     *                                         cell_to_forget_weights     (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
+     *                                         cell_to_output_weights     (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
+     *                                         input_gate_bias            (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: S32.
+     *                                         projection_weights         (Optional) 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8.
+     *                                         projection_bias            (Optional) 1D weights tensor with dimensions [output_size]. S32.
+     *                                         input_layer_norm_weights   (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
+     *                                         forget_layer_norm_weights  (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
+     *                                         cell_layer_norm_weights    (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
+     *                                         output_layer_norm_weights  (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
+     *                                         cell_threshold             (Optional) The clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip].
+     *                                                                               If set to 0.0 then clipping is disabled.
+     *                                         projection_threshold       (Optional) The clipping threshold for the output from the projection layer, such that values are bound within
+     *                                                                               [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input,
+                           const ITensorInfo *input_to_forget_weights, const ITensorInfo *input_to_cell_weights, const ITensorInfo *input_to_output_weights,
+                           const ITensorInfo *recurrent_to_forget_weights, const ITensorInfo *recurrent_to_cell_weights, const ITensorInfo *recurrent_to_output_weights,
+                           const ITensorInfo *forget_gate_bias, const ITensorInfo *cell_bias, const ITensorInfo *output_gate_bias,
+                           const ITensorInfo *cell_state_in, const ITensorInfo *output_state_in,
+                           const ITensorInfo *cell_state_out, const ITensorInfo *output_state_out,
+                           const LSTMParams<ITensorInfo> &lstm_params);
+
+    // Inherited methods overridden:
+    void run() override;
+    void prepare() override;
+
+private:
+    /** Internal method to configure matrix multiplication plus output stage of each gate.
+     *
+     * @param[in] mm             Matrix multiplication function to use.
+     * @param[in] outstage       Output stage function to use.
+     * @param[in] gemmlowp_info  GEMMLowp metadata to be used by the output stage.
+     * @param[in] mm_input       Input tensor to matrix multiplication function.
+     * @param[in] mm_weights     Weights tensor to matrix multiplication function.
+     * @param[in] bias           Bias tensor to matrix multiplication function.
+     * @param[in] outstage_res   Tensor to be used for storing the result of the output stage.
+     * @param[in] gemmlowp_scale Real multiplier to be used computing multiplier and shift for requantization.
+     * @param[in] mm_res_info    Tensor info to be used to initialize matrix multiplication result tensor.
+     * @param[in] mm_res_info    Tensor info to be used to initialize output stage result tensor.
+     *
+     */
+    void configure_mm(CLGEMMLowpMatrixMultiplyCore &mm, CLGEMMLowpOutputStage &outstage, GEMMLowpOutputStageInfo &gemmlowp_info,
+                      const ICLTensor *mm_input, const ICLTensor *mm_weights, const ICLTensor *bias, CLTensor *mm_res,
+                      CLTensor *outstage_res, float gemmlowp_scale,
+                      const TensorInfo &mm_res_info, const TensorInfo &outstage_tensor_info);
+
+    MemoryGroup _memory_group{};
+
+    // Functions used
+    CLTranspose                          _transpose_input_to_forget_weights{};
+    CLTranspose                          _transpose_input_to_cell_weights{};
+    CLTranspose                          _transpose_input_to_output_weights{};
+    CLTranspose                          _transpose_input_to_input_weights{};
+    CLTranspose                          _transpose_recurrent_to_forget_weights{};
+    CLTranspose                          _transpose_recurrent_to_cell_weights{};
+    CLTranspose                          _transpose_recurrent_to_output_weights{};
+    CLTranspose                          _transpose_recurrent_to_input_weights{};
+    CLTranspose                          _transpose_projection_weights{};
+    CLGEMMLowpMatrixAReductionKernel     _input_to_input_reduction{};
+    CLGEMMLowpMatrixAReductionKernel     _recurrent_to_input_reduction{};
+    CLGEMMLowpMatrixAReductionKernel     _input_to_forget_reduction{};
+    CLGEMMLowpMatrixAReductionKernel     _recurrent_to_forget_reduction{};
+    CLGEMMLowpMatrixAReductionKernel     _input_to_cell_reduction{};
+    CLGEMMLowpMatrixAReductionKernel     _recurrent_to_cell_reduction{};
+    CLGEMMLowpMatrixAReductionKernel     _input_to_output_reduction{};
+    CLGEMMLowpMatrixAReductionKernel     _recurrent_to_output_reduction{};
+    CLGEMMLowpMatrixAReductionKernel     _projection_reduction{};
+    CLSaturatedArithmeticOperationKernel _projection_bias_add{};
+    CLGEMMLowpMatrixMultiplyCore         _mm_input_to_forget{};
+    CLGEMMLowpMatrixMultiplyCore         _mm_recurrent_to_forget{};
+    CLPixelWiseMultiplicationKernel      _pixelwise_mul_cell_to_forget{};
+    CLGEMMLowpOutputStage                _input_to_forget_outstage{};
+    CLGEMMLowpOutputStage                _recurrent_to_forget_outstage{};
+    CLGEMMLowpOutputStage                _cell_to_forget_outstage{};
+    CLSaturatedArithmeticOperationKernel _accumulate_input_recurrent_forget{};
+    CLSaturatedArithmeticOperationKernel _accumulate_cell_forget{};
+    CLActivationLayer                    _forget_gate_sigmoid{};
+    CLGEMMLowpMatrixMultiplyCore         _mm_input_to_cell{};
+    CLGEMMLowpOutputStage                _input_to_cell_outstage{};
+    CLGEMMLowpMatrixMultiplyCore         _mm_recurrent_to_cell{};
+    CLGEMMLowpOutputStage                _recurrent_to_cell_outstage{};
+    CLSaturatedArithmeticOperationKernel _accumulate_input_recurrent_modulation{};
+    CLActivationLayer                    _cell_gate_tanh{};
+    CLSaturatedArithmeticOperationKernel _input_gate_sub{};
+    CLGEMMLowpMatrixMultiplyCore         _mm_input_to_input{};
+    CLGEMMLowpOutputStage                _input_to_input_outstage{};
+    CLGEMMLowpMatrixMultiplyCore         _mm_recurrent_to_input{};
+    CLGEMMLowpOutputStage                _recurrent_to_input_outstage{};
+    CLSaturatedArithmeticOperationKernel _accumulate_input_recurrent_input{};
+    CLPixelWiseMultiplicationKernel      _pixelwise_mul_cell_to_input{};
+    CLGEMMLowpOutputStage                _cell_to_input_outstage{};
+    CLSaturatedArithmeticOperationKernel _accumulate_cell_input{};
+    CLActivationLayer                    _input_gate_tanh{};
+    CLPixelWiseMultiplicationKernel      _pixelwise_mul_forget_cell{};
+    CLPixelWiseMultiplicationKernel      _pixelwise_mul_input_cell{};
+    CLSaturatedArithmeticOperationKernel _add_forget_cell{};
+    CLActivationLayer                    _cell_clip{};
+    CLGEMMLowpMatrixMultiplyCore         _mm_input_to_output{};
+    CLGEMMLowpOutputStage                _input_to_output_outstage{};
+    CLGEMMLowpMatrixMultiplyCore         _mm_recurrent_to_output{};
+    CLGEMMLowpOutputStage                _recurrent_to_output_outstage{};
+    CLSaturatedArithmeticOperationKernel _accumulate_input_recurrent_output{};
+    CLPixelWiseMultiplicationKernel      _pixelwise_mul_cell_to_output{};
+    CLSaturatedArithmeticOperationKernel _accumulate_cell_to_output{};
+    CLActivationLayer                    _output_gate_sigmoid{};
+    CLActivationLayer                    _hidden_tanh{};
+    CLPixelWiseMultiplicationKernel      _pixelwise_mul_hidden{};
+    CLGEMMLowpOutputStage                _hidden_outstage{};
+    CLGEMMLowpMatrixMultiplyCore         _mm_projection{};
+    CLGEMMLowpOutputStage                _projection_outstage{};
+    CLSaturatedArithmeticOperationKernel _accumulate_projection{};
+    CLActivationLayer                    _projection_clip{};
+
+    // Tensor pointers
+    const ICLTensor *_input_to_input_weights
+    {
+        nullptr
+    };
+    const ICLTensor *_recurrent_to_input_weights{ nullptr };
+    const ICLTensor *_projection_bias{ nullptr };
+    const ICLTensor *_input_to_forget_weights{ nullptr };
+    const ICLTensor *_input_to_cell_weights{ nullptr };
+    const ICLTensor *_input_to_output_weights{ nullptr };
+    const ICLTensor *_recurrent_to_forget_weights{ nullptr };
+    const ICLTensor *_recurrent_to_cell_weights{ nullptr };
+    const ICLTensor *_recurrent_to_output_weights{ nullptr };
+    const ICLTensor *_projection_weights{ nullptr };
+
+    // Temporary tensors
+    CLTensor _input_to_forget_weights_transposed{ nullptr };
+    CLTensor _input_to_cell_weights_transposed{ nullptr };
+    CLTensor _input_to_output_weights_transposed{ nullptr };
+    CLTensor _input_to_input_weights_transposed{ nullptr };
+    CLTensor _recurrent_to_forget_weights_transposed{ nullptr };
+    CLTensor _recurrent_to_cell_weights_transposed{ nullptr };
+    CLTensor _recurrent_to_output_weights_transposed{ nullptr };
+    CLTensor _recurrent_to_input_weights_transposed{ nullptr };
+    CLTensor _projection_weights_transposed{ nullptr };
+    CLTensor _input_to_input_eff_bias{ nullptr };
+    CLTensor _recurrent_to_input_eff_bias{ nullptr };
+    CLTensor _input_to_forget_eff_bias{ nullptr };
+    CLTensor _recurrent_to_forget_eff_bias{ nullptr };
+    CLTensor _input_to_cell_eff_bias{ nullptr };
+    CLTensor _recurrent_to_cell_eff_bias{ nullptr };
+    CLTensor _input_to_output_eff_bias{ nullptr };
+    CLTensor _recurrent_to_output_eff_bias{ nullptr };
+    CLTensor _projection_reduction_res{ nullptr };
+    CLTensor _projection_eff_bias{ nullptr };
+    CLTensor _mm_input_to_forget_res{ nullptr };
+    CLTensor _mm_recurrent_to_forget_res{ nullptr };
+    CLTensor _mul_cell_to_forget_res{ nullptr };
+    CLTensor _input_to_forget_outstage_res{ nullptr };
+    CLTensor _cell_to_forget_outstage_res{ nullptr };
+    CLTensor _recurrent_to_forget_outstage_res{ nullptr };
+    CLTensor _forget_gate{ nullptr };
+    CLTensor _mm_input_to_cell_res{ nullptr };
+    CLTensor _input_to_cell_outstage_res{ nullptr };
+    CLTensor _mm_recurrent_to_cell_res{ nullptr };
+    CLTensor _recurrent_to_cell_outstage_res{ nullptr };
+    CLTensor _cell_gate{ nullptr };
+    CLTensor _mul_input_cell_res{ nullptr };
+    CLTensor _mm_input_to_input_res{ nullptr };
+    CLTensor _input_to_input_outstage_res{ nullptr };
+    CLTensor _mm_recurrent_to_input_res{ nullptr };
+    CLTensor _mul_cell_to_input_res{ nullptr };
+    CLTensor _cell_to_input_outstage_res{ nullptr };
+    CLTensor _recurrent_to_input_outstage_res{ nullptr };
+    CLTensor _input_gate{ nullptr };
+    CLTensor _mm_input_to_output_res{ nullptr };
+    CLTensor _input_to_output_outstage_res{ nullptr };
+    CLTensor _mm_recurrent_to_output_res{ nullptr };
+    CLTensor _mul_cell_to_output_res{ nullptr };
+    CLTensor _recurrent_to_output_outstage_res{ nullptr };
+    CLTensor _output_gate{ nullptr };
+    CLTensor _hidden_mul_res{ nullptr };
+    CLTensor _mm_projection_res{ nullptr };
+    CLTensor _projection_outstage_res{ nullptr };
+    CLTensor _ones{ nullptr };
+
+    bool _is_prepared{ false };
+    bool _has_cifg{ false };
+    bool _has_cell_clipping{ false };
+    bool _has_projection{ false };
+    bool _has_projection_clipping{ false };
+    bool _has_peephole{ false };
+};
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_CLQLSTMLAYER_H */
diff --git a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.cpp b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.cpp
index dd4c55c2d8..ad675df7ea 100644
--- a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.cpp
@@ -56,7 +56,14 @@ Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1,
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1);
+    if(input0->data_type() == DataType::QASYMM8)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1);
+    }
+    else
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::QSYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8_PER_CHANNEL);
+    }
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(input0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3");
 
diff --git a/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.cpp b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.cpp
index 00cef56db7..066307c4b2 100644
--- a/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.cpp
@@ -35,8 +35,6 @@
 
 #include "support/StringSupport.h"
 
-using namespace arm_compute;
-
 namespace arm_compute
 {
 namespace
@@ -98,9 +96,6 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen
 }
 } // namespace
 
-class Coordinates;
-} // namespace arm_compute
-
 CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel()
     : _input(nullptr), _bias(nullptr), _output(nullptr)
 {
@@ -184,3 +179,4 @@ void CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::run(const Window
     }
     while(collapsed.slide_window_slice_3D(slice));
 }
+} // namespace arm_compute
diff --git a/src/core/CL/kernels/CLGEMMLowpReductionKernel.cpp b/src/core/CL/kernels/CLGEMMLowpReductionKernel.cpp
index e81ab2ffba..9fa253a55a 100644
--- a/src/core/CL/kernels/CLGEMMLowpReductionKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMLowpReductionKernel.cpp
@@ -36,7 +36,7 @@ namespace
 Status validate_arguments_matrix_a_reduction(const ITensorInfo *input, const ITensorInfo *output)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8);
 
     if(output->total_size() > 0)
     {
@@ -49,7 +49,7 @@ Status validate_arguments_matrix_a_reduction(const ITensorInfo *input, const ITe
 Status validate_arguments_matrix_b_reduction(const ITensorInfo *input, const ITensorInfo *output)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8);
 
     if(output->total_size() > 0)
     {
@@ -63,6 +63,9 @@ std::pair<Status, Window> validate_and_configure_window_matrix_b_reduction(ITens
 {
     constexpr unsigned int num_elems_processed_per_iteration = 16;
 
+    // Output auto initialization if not yet initialized
+    auto_init_if_empty(*output, TensorShape(input->dimension(0)), 1, DataType::S32);
+
     // Configure kernel window
     Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration));
 
@@ -94,6 +97,9 @@ void CLGEMMLowpMatrixAReductionKernel::configure(CLCompileContext &compile_conte
     ARM_COMPUTE_ERROR_ON_NULLPTR(mtx_a, vector_sum_row);
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_matrix_a_reduction(mtx_a->info(), vector_sum_row->info()));
 
+    // Output auto initialization if not yet initialized
+    auto_init_if_empty(*vector_sum_row->info(), TensorShape(mtx_a->info()->dimension(1)), 1, DataType::S32);
+
     _input  = mtx_a;
     _output = vector_sum_row;
 
diff --git a/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp
index ef17f110d0..3465da95b7 100644
--- a/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp
+++ b/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp
@@ -303,11 +303,9 @@ Status CLGEMMLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITenso
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
-    //DataType::QSYMM8_PER_CHANNEL supported only for weights
-    if(b->data_type() != DataType::QSYMM8_PER_CHANNEL)
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(a, b);
-    }
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(b, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8, DataType::QSYMM8_PER_CHANNEL);
+    ARM_COMPUTE_RETURN_ERROR_ON(a->data_type() == DataType::QASYMM8 && b->data_type() == DataType::QASYMM8_SIGNED);
+    ARM_COMPUTE_RETURN_ERROR_ON(a->data_type() == DataType::QASYMM8_SIGNED && b->data_type() == DataType::QASYMM8);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported");
 
diff --git a/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp b/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp
index 2114d39866..aff7f54a82 100644
--- a/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp
+++ b/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp
@@ -149,6 +149,13 @@ void CLGEMMLowpOutputStage::configure(const ICLTensor *input, const ICLTensor *b
                     _kernel = std::move(k);
                     break;
                 }
+                case DataType::QSYMM16:
+                {
+                    auto k = arm_compute::support::cpp14::make_unique<CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel>();
+                    k->configure(input, bias, output, info.gemmlowp_multiplier, info.gemmlowp_shift, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
+                    _kernel = std::move(k);
+                    break;
+                }
                 default:
                     ARM_COMPUTE_ERROR("Unsupported output data type.");
             }
@@ -188,6 +195,8 @@ Status CLGEMMLowpOutputStage::validate(const ITensorInfo *input, const ITensorIn
                     return CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(input, bias, output, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
                 case DataType::QASYMM8_SIGNED:
                     return CLGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::validate(input, bias, output, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
+                case DataType::QSYMM16:
+                    return CLGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::validate(input, bias, output, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
                 default:
                     return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Unsupported output data type.");
             }
diff --git a/src/runtime/CL/functions/CLQLSTMLayer.cpp b/src/runtime/CL/functions/CLQLSTMLayer.cpp
new file mode 100644
index 0000000000..4b994d47b2
--- /dev/null
+++ b/src/runtime/CL/functions/CLQLSTMLayer.cpp
@@ -0,0 +1,853 @@
+/*
+ * Copyright (c) 2020 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/runtime/CL/functions/CLQLSTMLayer.h"
+
+#include "arm_compute/core/KernelDescriptors.h"
+#include "arm_compute/core/QuantizationInfo.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/misc/InfoHelpers.h"
+#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
+#include "arm_compute/runtime/CL/CLScheduler.h"
+
+namespace arm_compute
+{
+using namespace arm_compute::utils::info_helpers;
+namespace
+{
+Status validate_mm(GEMMLowpOutputStageInfo &gemmlowp_info, const ITensorInfo *mm_input, const ITensorInfo *mm_weights, const ITensorInfo *bias,
+                   float gemmlowp_scale, const TensorInfo *mm_res_info, const TensorInfo *outstage_tensor_info)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixMultiplyCore::validate(mm_input, mm_weights, nullptr, mm_res_info));
+    ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(gemmlowp_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpOutputStage::validate(mm_res_info, bias, outstage_tensor_info, gemmlowp_info));
+    return Status{};
+}
+} // namespace
+
+CLQLSTMLayer::CLQLSTMLayer(std::shared_ptr<IMemoryManager> memory_manager)
+{
+    _memory_group = MemoryGroup(std::move(memory_manager));
+}
+
+void CLQLSTMLayer::configure_mm(CLGEMMLowpMatrixMultiplyCore &mm, CLGEMMLowpOutputStage &outstage, GEMMLowpOutputStageInfo &gemmlowp_info,
+                                const ICLTensor *mm_input, const ICLTensor *mm_weights, const ICLTensor *bias,
+                                CLTensor *mm_res, CLTensor *outstage_res, float gemmlowp_scale,
+                                const TensorInfo &mm_res_info, const TensorInfo &outstage_tensor_info)
+{
+    _memory_group.manage(mm_res);
+    _memory_group.manage(outstage_res);
+
+    mm_res->allocator()->init(mm_res_info);
+    outstage_res->allocator()->init(outstage_tensor_info);
+
+    // Configure matrix-multiplication
+    mm.configure(mm_input, mm_weights, nullptr, mm_res);
+
+    // Configure output stage
+    quantization::calculate_quantized_multiplier(gemmlowp_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift);
+    outstage.configure(mm_res, bias, outstage_res, gemmlowp_info);
+    mm_res->allocator()->allocate();
+}
+
+void CLQLSTMLayer::configure(const ICLTensor *input,
+                             const ICLTensor *input_to_forget_weights, const ICLTensor *input_to_cell_weights, const ICLTensor *input_to_output_weights,
+                             const ICLTensor *recurrent_to_forget_weights, const ICLTensor *recurrent_to_cell_weights, const ICLTensor *recurrent_to_output_weights,
+                             const ICLTensor *forget_gate_bias, const ICLTensor *cell_bias, const ICLTensor *output_gate_bias,
+                             const ICLTensor *cell_state_in, const ICLTensor *output_state_in,
+                             ICLTensor *cell_state_out, ICLTensor *output_state_out,
+                             const LSTMParams<ICLTensor> &lstm_params)
+{
+    ARM_COMPUTE_UNUSED(forget_gate_bias);
+    ARM_COMPUTE_UNUSED(cell_bias);
+    ARM_COMPUTE_UNUSED(output_gate_bias);
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, input_to_forget_weights, input_to_cell_weights, input_to_output_weights,
+                                 recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights,
+                                 forget_gate_bias, cell_bias, output_gate_bias, cell_state_in, output_state_in, cell_state_out, output_state_out);
+
+    // Set lstm parameters
+    LSTMParams<ITensorInfo> lstm_params_info{};
+    build_lstm_params_tensor_info(lstm_params, &lstm_params_info);
+
+    // Validate
+    ARM_COMPUTE_ERROR_THROW_ON(CLQLSTMLayer::validate(input->info(), input_to_forget_weights->info(), input_to_cell_weights->info(), input_to_output_weights->info(),
+                                                      recurrent_to_forget_weights->info(), recurrent_to_cell_weights->info(), recurrent_to_output_weights->info(),
+                                                      forget_gate_bias->info(), cell_bias->info(), output_gate_bias->info(),
+                                                      cell_state_in->info(), output_state_in->info(), cell_state_out->info(), output_state_out->info(), lstm_params_info));
+
+    const int batch_size = input->info()->dimension(1);
+    const int num_units  = input_to_output_weights->info()->dimension(1);
+
+    const UniformQuantizationInfo qinput           = input->info()->quantization_info().uniform();
+    const UniformQuantizationInfo qcell_state_in   = cell_state_in->info()->quantization_info().uniform();
+    const UniformQuantizationInfo qoutput_state_in = output_state_in->info()->quantization_info().uniform();
+
+    _projection_bias             = lstm_params.projection_bias();
+    _input_to_forget_weights     = input_to_forget_weights;
+    _input_to_cell_weights       = input_to_cell_weights;
+    _input_to_output_weights     = input_to_output_weights;
+    _recurrent_to_forget_weights = recurrent_to_forget_weights;
+    _recurrent_to_cell_weights   = recurrent_to_cell_weights;
+    _recurrent_to_output_weights = recurrent_to_output_weights;
+    _projection_weights          = lstm_params.projection_weights();
+
+    _has_cifg       = lstm_params.has_cifg_opt();
+    _has_projection = lstm_params.has_projection();
+    _has_peephole   = lstm_params.has_peephole_opt();
+
+    // Calculate and decompose effective scales for optimizing matmul calculation
+    const int32_t cell_shift = log2(qcell_state_in.scale);
+
+    // Calculate quantized parameters for clipping.
+    int16_t quantized_cell_clip = 0;
+    if(lstm_params.cell_clip() > 0.0f)
+    {
+        quantized_cell_clip = quantize_qsymm16(lstm_params.cell_clip(), qcell_state_in);
+    }
+    _has_cell_clipping = quantized_cell_clip > 0;
+
+    // Precompute effective bias for optimizing the matmul computations.
+    if(!_has_cifg)
+    {
+        _input_to_input_weights     = lstm_params.input_to_input_weights();
+        _recurrent_to_input_weights = lstm_params.recurrent_to_input_weights();
+
+        _input_to_input_reduction.configure(_input_to_input_weights, &_input_to_input_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
+        _recurrent_to_input_reduction.configure(_recurrent_to_input_weights, &_recurrent_to_input_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
+    }
+    _input_to_forget_reduction.configure(input_to_forget_weights, &_input_to_forget_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
+    _recurrent_to_forget_reduction.configure(recurrent_to_forget_weights, &_recurrent_to_forget_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
+    _input_to_cell_reduction.configure(input_to_cell_weights, &_input_to_cell_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
+    _recurrent_to_cell_reduction.configure(recurrent_to_cell_weights, &_recurrent_to_cell_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
+    _input_to_output_reduction.configure(input_to_output_weights, &_input_to_output_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true));
+    _recurrent_to_output_reduction.configure(recurrent_to_output_weights, &_recurrent_to_output_eff_bias, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true));
+    if(_projection_bias != nullptr)
+    {
+        _projection_reduction.configure(_projection_weights, &_projection_reduction_res, GEMMLowpReductionKernelInfo(num_units, false, lstm_params.hidden_state_zero(), true));
+        _projection_bias_add.configure(ArithmeticOperation::ADD, _projection_bias, &_projection_reduction_res, &_projection_eff_bias, ConvertPolicy::SATURATE);
+    }
+
+    // Pre-transpose weights to be used in GEMM.
+    _transpose_input_to_forget_weights.configure(input_to_forget_weights, &_input_to_forget_weights_transposed);
+    _transpose_input_to_cell_weights.configure(input_to_cell_weights, &_input_to_cell_weights_transposed);
+    _transpose_input_to_output_weights.configure(input_to_output_weights, &_input_to_output_weights_transposed);
+    _transpose_recurrent_to_forget_weights.configure(recurrent_to_forget_weights, &_recurrent_to_forget_weights_transposed);
+    _transpose_recurrent_to_cell_weights.configure(recurrent_to_cell_weights, &_recurrent_to_cell_weights_transposed);
+    _transpose_recurrent_to_output_weights.configure(recurrent_to_output_weights, &_recurrent_to_output_weights_transposed);
+    if(!_has_cifg)
+    {
+        _transpose_input_to_input_weights.configure(lstm_params.input_to_input_weights(), &_input_to_input_weights_transposed);
+        _transpose_recurrent_to_input_weights.configure(lstm_params.recurrent_to_input_weights(), &_recurrent_to_input_weights_transposed);
+    }
+    if(_has_projection)
+    {
+        _transpose_projection_weights.configure(_projection_weights, &_projection_weights_transposed);
+    }
+
+    GEMMLowpOutputStageInfo gemmlowp_info;
+    gemmlowp_info.type               = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
+    gemmlowp_info.gemmlowp_min_bound = std::numeric_limits<int16_t>::lowest();
+    gemmlowp_info.gemmlowp_max_bound = std::numeric_limits<int16_t>::max();
+    gemmlowp_info.output_data_type   = DataType::QSYMM16;
+
+    const TensorInfo mm_out_info(TensorShape(num_units, batch_size), 1, DataType::S32);
+    // Forget gate.
+    const TensorInfo forget_gate_outstage_info(mm_out_info.tensor_shape(), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.forget_intermediate_scale(), 0));
+    const float      input_to_forget_scale = input_to_forget_weights->info()->quantization_info().uniform().scale * qinput.scale / lstm_params.forget_intermediate_scale();
+    configure_mm(_mm_input_to_forget, _input_to_forget_outstage, gemmlowp_info,
+                 input, &_input_to_forget_weights_transposed, &_input_to_forget_eff_bias,
+                 &_mm_input_to_forget_res, &_input_to_forget_outstage_res, input_to_forget_scale,
+                 mm_out_info, forget_gate_outstage_info);
+
+    const float recurrent_to_forget_scale = recurrent_to_forget_weights->info()->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.forget_intermediate_scale();
+    configure_mm(_mm_recurrent_to_forget, _recurrent_to_forget_outstage, gemmlowp_info,
+                 output_state_in, &_recurrent_to_forget_weights_transposed, &_recurrent_to_forget_eff_bias,
+                 &_mm_recurrent_to_forget_res, &_recurrent_to_forget_outstage_res, recurrent_to_forget_scale,
+                 mm_out_info, forget_gate_outstage_info);
+
+    _accumulate_input_recurrent_forget.configure(ArithmeticOperation::ADD, &_input_to_forget_outstage_res, &_recurrent_to_forget_outstage_res, &_recurrent_to_forget_outstage_res, ConvertPolicy::SATURATE);
+    _input_to_forget_outstage_res.allocator()->allocate();
+
+    if(_has_peephole)
+    {
+        _memory_group.manage(&_mul_cell_to_forget_res);
+        _pixelwise_mul_cell_to_forget.configure(cell_state_in, lstm_params.cell_to_forget_weights(), &_mul_cell_to_forget_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
+        _cell_to_forget_outstage_res.allocator()->init(TensorInfo(_mul_cell_to_forget_res.info()->tensor_shape(), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.forget_intermediate_scale(), 0)));
+        _memory_group.manage(&_cell_to_forget_outstage_res);
+        const float cell_to_forget_scale = std::pow(2, cell_shift) * lstm_params.cell_to_forget_weights()->info()->quantization_info().uniform().scale / lstm_params.forget_intermediate_scale();
+        quantization::calculate_quantized_multiplier(cell_to_forget_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift);
+        _cell_to_forget_outstage.configure(&_mul_cell_to_forget_res, nullptr, &_cell_to_forget_outstage_res, gemmlowp_info);
+        _mul_cell_to_forget_res.allocator()->allocate();
+        _accumulate_cell_forget.configure(ArithmeticOperation::ADD, &_recurrent_to_forget_outstage_res, &_cell_to_forget_outstage_res, &_recurrent_to_forget_outstage_res, ConvertPolicy::SATURATE);
+        _cell_to_forget_outstage_res.allocator()->allocate();
+    }
+
+    // Output quantization info of Sigmoid and Tanh activations
+    const QuantizationInfo sigmoid_tanh_outqinfo(1.f / 32768.f, 0);
+
+    const TensorInfo forget_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
+    _memory_group.manage(&_forget_gate);
+    _forget_gate.allocator()->init(forget_gate_info);
+    _forget_gate_sigmoid.configure(&_recurrent_to_forget_outstage_res, &_forget_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
+    _recurrent_to_forget_outstage_res.allocator()->allocate();
+
+    // Modulation gate.
+    const TensorInfo cell_outstage_info(mm_out_info.tensor_shape(), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.cell_intermediate_scale(), 0));
+    const float      input_to_cell_scale = input_to_cell_weights->info()->quantization_info().uniform().scale * qinput.scale / lstm_params.cell_intermediate_scale();
+    configure_mm(_mm_input_to_cell, _input_to_cell_outstage, gemmlowp_info,
+                 input, &_input_to_cell_weights_transposed, &_input_to_cell_eff_bias,
+                 &_mm_input_to_cell_res, &_input_to_cell_outstage_res, input_to_cell_scale,
+                 mm_out_info, cell_outstage_info);
+
+    const float recurrent_to_cell_scale = recurrent_to_cell_weights->info()->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.cell_intermediate_scale();
+    configure_mm(_mm_recurrent_to_cell, _recurrent_to_cell_outstage, gemmlowp_info,
+                 output_state_in, &_recurrent_to_cell_weights_transposed, &_recurrent_to_cell_eff_bias,
+                 &_mm_recurrent_to_cell_res, &_recurrent_to_cell_outstage_res, recurrent_to_cell_scale,
+                 mm_out_info, cell_outstage_info);
+
+    _accumulate_input_recurrent_modulation.configure(ArithmeticOperation::ADD, &_input_to_cell_outstage_res, &_recurrent_to_cell_outstage_res, &_recurrent_to_cell_outstage_res, ConvertPolicy::SATURATE);
+    _input_to_cell_outstage_res.allocator()->allocate();
+
+    const TensorInfo cell_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
+    _memory_group.manage(&_cell_gate);
+    _cell_gate.allocator()->init(cell_gate_info);
+    _cell_gate_tanh.configure(&_recurrent_to_cell_outstage_res, &_cell_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f));
+    _recurrent_to_cell_outstage_res.allocator()->allocate();
+
+    // Input gate.
+    const TensorInfo input_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
+    _input_gate.allocator()->init(input_gate_info);
+    _memory_group.manage(&_input_gate);
+    if(_has_cifg)
+    {
+        _ones.allocator()->init(*_forget_gate.info());
+        _input_gate_sub.configure(ArithmeticOperation::SUB, &_ones, &_forget_gate, &_input_gate, ConvertPolicy::SATURATE);
+        _ones.allocator()->allocate();
+    }
+    else
+    {
+        const TensorInfo input_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.input_intermediate_scale(), 0));
+        const float      input_to_input_scale = _input_to_input_weights->info()->quantization_info().uniform().scale * qinput.scale / lstm_params.input_intermediate_scale();
+        configure_mm(_mm_input_to_input, _input_to_input_outstage, gemmlowp_info,
+                     input, &_input_to_input_weights_transposed, &_input_to_input_eff_bias,
+                     &_mm_input_to_input_res, &_input_to_input_outstage_res, input_to_input_scale,
+                     mm_out_info, input_outstage_info);
+
+        const float recurrent_to_input_scale = _recurrent_to_input_weights->info()->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.input_intermediate_scale();
+        configure_mm(_mm_recurrent_to_input, _recurrent_to_input_outstage, gemmlowp_info,
+                     input, &_recurrent_to_input_weights_transposed, &_recurrent_to_input_eff_bias,
+                     &_mm_recurrent_to_input_res, &_recurrent_to_input_outstage_res, recurrent_to_input_scale,
+                     mm_out_info, input_outstage_info);
+        _accumulate_input_recurrent_input.configure(ArithmeticOperation::ADD, &_input_to_input_outstage_res, &_recurrent_to_input_outstage_res, &_recurrent_to_input_outstage_res, ConvertPolicy::SATURATE);
+        _input_to_input_outstage_res.allocator()->allocate();
+
+        if(_has_peephole)
+        {
+            _memory_group.manage(&_mul_cell_to_input_res);
+            _pixelwise_mul_cell_to_input.configure(cell_state_in, lstm_params.cell_to_input_weights(), &_mul_cell_to_input_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
+            const float cell_to_input_scale = std::pow(2, cell_shift) * lstm_params.cell_to_input_weights()->info()->quantization_info().uniform().scale / lstm_params.input_intermediate_scale();
+            quantization::calculate_quantized_multiplier(cell_to_input_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift);
+            _cell_to_input_outstage_res.allocator()->init(TensorInfo(_mul_cell_to_input_res.info()->tensor_shape(), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.input_intermediate_scale(), 0)));
+            _memory_group.manage(&_cell_to_input_outstage_res);
+            _cell_to_input_outstage.configure(&_mul_cell_to_input_res, nullptr, &_cell_to_input_outstage_res, gemmlowp_info);
+            _mul_cell_to_input_res.allocator()->allocate();
+            _accumulate_cell_input.configure(ArithmeticOperation::ADD, &_recurrent_to_input_outstage_res, &_cell_to_input_outstage_res, &_recurrent_to_input_outstage_res, ConvertPolicy::SATURATE);
+            _cell_to_input_outstage_res.allocator()->allocate();
+        }
+
+        _input_gate_tanh.configure(&_recurrent_to_input_outstage_res, &_input_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f));
+        _recurrent_to_input_outstage_res.allocator()->allocate();
+    }
+    // Cell.
+    // TODO(COMPMID-3396): Perform multiplication in the quantized domain in CLPixelWiseMultiplicationKernel
+    _pixelwise_mul_forget_cell.configure(&_forget_gate, cell_state_in, &_forget_gate, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
+    const float      cell_gate_scale      = _cell_gate.info()->quantization_info().uniform().scale;
+    const float      mul_input_cell_scale = cell_gate_scale * std::pow(2, 15 + cell_shift);
+    const TensorInfo mul_input_cell_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(mul_input_cell_scale, 0));
+    _memory_group.manage(&_mul_input_cell_res);
+    _mul_input_cell_res.allocator()->init(mul_input_cell_info);
+    _pixelwise_mul_input_cell.configure(&_input_gate, &_cell_gate, &_mul_input_cell_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
+    _cell_gate.allocator()->allocate();
+    _add_forget_cell.configure(ArithmeticOperation::ADD, &_forget_gate, &_mul_input_cell_res, cell_state_out, ConvertPolicy::SATURATE);
+    _mul_input_cell_res.allocator()->allocate();
+    _forget_gate.allocator()->allocate();
+    if(_has_cell_clipping)
+    {
+        _cell_clip.configure(cell_state_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -quantized_cell_clip, quantized_cell_clip));
+    }
+    // Output gate.
+    const TensorInfo output_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.output_intermediate_scale(), 0));
+    const float      input_to_output_scale = input_to_output_weights->info()->quantization_info().uniform().scale * qinput.scale / lstm_params.output_intermediate_scale();
+    configure_mm(_mm_input_to_output, _input_to_output_outstage, gemmlowp_info,
+                 input, &_input_to_output_weights_transposed, &_input_to_output_eff_bias,
+                 &_mm_input_to_output_res, &_input_to_output_outstage_res, input_to_output_scale,
+                 mm_out_info, output_outstage_info);
+
+    const float recurrent_to_output_scale = recurrent_to_output_weights->info()->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.output_intermediate_scale();
+    configure_mm(_mm_recurrent_to_output, _recurrent_to_output_outstage, gemmlowp_info,
+                 output_state_in, &_recurrent_to_output_weights_transposed, &_recurrent_to_output_eff_bias,
+                 &_mm_recurrent_to_output_res, &_recurrent_to_output_outstage_res, recurrent_to_output_scale,
+                 mm_out_info, output_outstage_info);
+
+    _accumulate_input_recurrent_output.configure(ArithmeticOperation::ADD, &_recurrent_to_output_outstage_res, &_input_to_output_outstage_res, &_recurrent_to_output_outstage_res, ConvertPolicy::SATURATE);
+    _input_to_output_outstage_res.allocator()->allocate();
+
+    if(_has_peephole)
+    {
+        // TODO(COMPMID-3396): Perform multiplication in the quantized domain in CLPixelWiseMultiplicationKernel
+        // Here we are not using the output stage because all operations are done in float
+        // const float cell_to_output_scale = std::pow(2, cell_shift) * lstm_params.cell_to_output_weights()->info()->quantization_info().uniform().scale / lstm_params.output_intermediate_scale();
+        // quantization::calculate_quantized_multiplier(cell_to_output_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift);
+        _memory_group.manage(&_mul_cell_to_output_res);
+        _pixelwise_mul_cell_to_output.configure(cell_state_out, lstm_params.cell_to_output_weights(), &_mul_cell_to_output_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
+        _accumulate_cell_to_output.configure(ArithmeticOperation::ADD, &_recurrent_to_output_outstage_res, &_mul_cell_to_output_res, &_recurrent_to_output_outstage_res, ConvertPolicy::SATURATE);
+        _mul_cell_to_output_res.allocator()->allocate();
+    }
+
+    const TensorInfo output_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
+    _memory_group.manage(&_output_gate);
+    _output_gate.allocator()->init(output_gate_info);
+    _output_gate_sigmoid.configure(&_recurrent_to_output_outstage_res, &_output_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
+    _recurrent_to_output_outstage_res.allocator()->allocate();
+
+    // Hidden.
+    _hidden_tanh.configure(cell_state_out, &_input_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f));
+    // TODO(COMPMID-3396): Perform multiplication in the quantized domain in CLPixelWiseMultiplicationKernel
+    _memory_group.manage(&_hidden_mul_res);
+    const TensorInfo hidden_mul_res(_input_gate.info()->tensor_shape(), 1, DataType::S32);
+    _hidden_mul_res.allocator()->init(hidden_mul_res);
+    _pixelwise_mul_hidden.configure(&_output_gate, &_input_gate, &_hidden_mul_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO);
+    _output_gate.allocator()->allocate();
+    _input_gate.allocator()->allocate();
+    const float hidden_state_scale = std::pow(2, -15) / lstm_params.hidden_state_scale() * std::pow(2, -15);
+    quantization::calculate_quantized_multiplier(hidden_state_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift, /* ignore_epsilon */ true);
+    gemmlowp_info.gemmlowp_offset  = lstm_params.hidden_state_zero();
+    gemmlowp_info.output_data_type = output_state_in->info()->data_type();
+    _hidden_outstage.configure(&_hidden_mul_res, nullptr, output_state_out, gemmlowp_info);
+    _hidden_mul_res.allocator()->allocate();
+
+    // Projection.
+    if(_has_projection)
+    {
+        const TensorInfo              projection_outstage_info(*output_state_out->info());
+        const UniformQuantizationInfo qprojection      = _projection_weights->info()->quantization_info().uniform();
+        const float                   projection_scale = qprojection.scale * lstm_params.hidden_state_scale() / qoutput_state_in.scale;
+        gemmlowp_info.gemmlowp_offset                  = qoutput_state_in.offset;
+        gemmlowp_info.gemmlowp_min_bound               = std::numeric_limits<int8_t>::lowest();
+        gemmlowp_info.gemmlowp_max_bound               = std::numeric_limits<int8_t>::max();
+        gemmlowp_info.output_data_type                 = DataType::QASYMM8_SIGNED;
+
+        configure_mm(_mm_projection, _projection_outstage, gemmlowp_info,
+                     output_state_out, &_projection_weights_transposed, &_projection_eff_bias,
+                     &_mm_projection_res, &_projection_outstage_res, projection_scale,
+                     mm_out_info, projection_outstage_info);
+
+        _accumulate_projection.configure(ArithmeticOperation::ADD, &_projection_outstage_res, output_state_out, output_state_out, ConvertPolicy::SATURATE);
+        _projection_outstage_res.allocator()->allocate();
+
+        int8_t quantized_projection_clip{ 0 };
+        if(lstm_params.projection_clip() > 0.0f)
+        {
+            quantized_projection_clip = utility::clamp<int8_t>(lstm_params.projection_clip() / qprojection.scale, -128, 127);
+        }
+
+        if(quantized_projection_clip > 0)
+        {
+            _projection_clip.configure(output_state_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -quantized_projection_clip, quantized_projection_clip));
+            _has_projection_clipping = true;
+        }
+    }
+}
+
+Status CLQLSTMLayer::validate(const ITensorInfo *input,
+                              const ITensorInfo *input_to_forget_weights, const ITensorInfo *input_to_cell_weights, const ITensorInfo *input_to_output_weights,
+                              const ITensorInfo *recurrent_to_forget_weights, const ITensorInfo *recurrent_to_cell_weights, const ITensorInfo *recurrent_to_output_weights,
+                              const ITensorInfo *forget_gate_bias, const ITensorInfo *cell_bias, const ITensorInfo *output_gate_bias,
+                              const ITensorInfo *cell_state_in, const ITensorInfo *output_state_in,
+                              const ITensorInfo *cell_state_out, const ITensorInfo *output_state_out,
+                              const LSTMParams<ITensorInfo> &lstm_params)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_forget_weights, recurrent_to_cell_weights,
+                                        recurrent_to_output_weights, forget_gate_bias, cell_bias, output_gate_bias, cell_state_in, output_state_in, cell_state_out, output_state_out);
+
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8_SIGNED);
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->num_dimensions() != 2, "Input must have exactly 2 dimensions");
+
+    const unsigned int input_size  = input->dimension(0);
+    const unsigned int batch_size  = input->dimension(1);
+    const unsigned int num_units   = input_to_output_weights->dimension(1);
+    const unsigned int output_size = recurrent_to_output_weights->dimension(0);
+
+    ARM_COMPUTE_RETURN_ERROR_ON(input_to_output_weights->num_dimensions() != 2);
+    ARM_COMPUTE_RETURN_ERROR_ON(input_to_output_weights->dimension(0) != input_size);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input_to_output_weights, input_to_forget_weights, input_to_cell_weights);
+    ARM_COMPUTE_RETURN_ERROR_ON(recurrent_to_output_weights->num_dimensions() != 2);
+    ARM_COMPUTE_RETURN_ERROR_ON(recurrent_to_output_weights->dimension(1) != num_units);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(recurrent_to_output_weights, recurrent_to_forget_weights, recurrent_to_cell_weights);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input_to_forget_weights, 1, DataType::QSYMM8);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_to_forget_weights, input_to_cell_weights, input_to_output_weights,
+                                                       recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights);
+
+    ARM_COMPUTE_RETURN_ERROR_ON(forget_gate_bias->num_dimensions() != 1);
+    ARM_COMPUTE_RETURN_ERROR_ON(forget_gate_bias->dimension(0) != num_units);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(forget_gate_bias, cell_bias, output_gate_bias);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(forget_gate_bias, 1, DataType::S32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(forget_gate_bias, cell_bias, output_gate_bias);
+
+    ARM_COMPUTE_RETURN_ERROR_ON(cell_state_in->num_dimensions() != 2);
+    ARM_COMPUTE_RETURN_ERROR_ON(cell_state_in->dimension(0) != num_units);
+    ARM_COMPUTE_RETURN_ERROR_ON(cell_state_in->dimension(1) != batch_size);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(cell_state_in, 1, DataType::QSYMM16);
+
+    ARM_COMPUTE_RETURN_ERROR_ON(output_state_in->num_dimensions() != 2);
+    ARM_COMPUTE_RETURN_ERROR_ON(output_state_in->dimension(0) != output_size);
+    ARM_COMPUTE_RETURN_ERROR_ON(output_state_in->dimension(1) != batch_size);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output_state_in);
+
+    // Check whether peephole weights are all there or none
+    if(lstm_params.has_peephole_opt())
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.cell_to_forget_weights(), lstm_params.cell_to_output_weights());
+        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lstm_params.cell_to_forget_weights(), 1, DataType::QSYMM16);
+        ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_to_forget_weights()->num_dimensions() != 1);
+        ARM_COMPUTE_RETURN_ERROR_ON(lstm_params.cell_to_forget_weights()->dimension(0) != num_units);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lstm_params.cell_to_forget_weights(), lstm_params.cell_to_output_weights());
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(lstm_params.cell_to_forget_weights(), lstm_params.cell_to_output_weights());
+
+        if(!lstm_params.has_cifg_opt())
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.cell_to_input_weights());
+            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lstm_params.cell_to_forget_weights(), lstm_params.cell_to_input_weights());
+            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(lstm_params.cell_to_forget_weights(), lstm_params.cell_to_input_weights());
+        }
+    }
+
+    const UniformQuantizationInfo qinput           = input->quantization_info().uniform();
+    const UniformQuantizationInfo qcell_state_in   = cell_state_in->quantization_info().uniform();
+    const UniformQuantizationInfo qoutput_state_in = output_state_in->quantization_info().uniform();
+
+    // Calculate and decompose effective scales for optimizing matmul calculation
+    const int32_t cell_shift = log2(qcell_state_in.scale);
+    ARM_COMPUTE_RETURN_ERROR_ON(cell_shift > -9);
+
+    // Calculate quantized parameters for clipping.
+    int16_t quantized_cell_clip = 0;
+    if(lstm_params.cell_clip() > 0.0f)
+    {
+        quantized_cell_clip = quantize_qsymm16(lstm_params.cell_clip(), qcell_state_in);
+    }
+
+    // Precompute effective bias for optimizing the matmul computations.
+    const TensorInfo eff_bias_info(TensorShape(num_units), 1, DataType::S32);
+    if(!lstm_params.has_cifg_opt())
+    {
+        ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixAReductionKernel::validate(lstm_params.input_to_input_weights(), &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)));
+        ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixAReductionKernel::validate(lstm_params.recurrent_to_input_weights(), &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset,
+                                                                               true)));
+    }
+    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixAReductionKernel::validate(input_to_forget_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixAReductionKernel::validate(recurrent_to_forget_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true)));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixAReductionKernel::validate(input_to_cell_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixAReductionKernel::validate(recurrent_to_cell_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true)));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixAReductionKernel::validate(input_to_output_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qinput.offset, true)));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixAReductionKernel::validate(recurrent_to_output_weights, &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, -qoutput_state_in.offset, true)));
+    if(lstm_params.projection_bias() != nullptr)
+    {
+        ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixAReductionKernel::validate(lstm_params.projection_weights(), &eff_bias_info, GEMMLowpReductionKernelInfo(num_units, false, lstm_params.hidden_state_zero(),
+                                                                               true)));
+        ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::ADD, lstm_params.projection_bias(), &eff_bias_info, &eff_bias_info, ConvertPolicy::SATURATE));
+    }
+
+    const TensorInfo input_weights_transposed(TensorShape(num_units, input_size), 1, input_to_forget_weights->data_type(), input_to_forget_weights->quantization_info());
+    const TensorInfo recurrent_weights_transposed(TensorShape(num_units, output_size), 1, recurrent_to_forget_weights->data_type(), recurrent_to_forget_weights->quantization_info());
+
+    // Validate weights transpose
+    ARM_COMPUTE_RETURN_ON_ERROR(CLTranspose::validate(input_to_forget_weights, &input_weights_transposed));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLTranspose::validate(input_to_cell_weights, &input_weights_transposed));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLTranspose::validate(input_to_output_weights, &input_weights_transposed));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLTranspose::validate(recurrent_to_forget_weights, &recurrent_weights_transposed));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLTranspose::validate(recurrent_to_cell_weights, &recurrent_weights_transposed));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLTranspose::validate(recurrent_to_output_weights, &recurrent_weights_transposed));
+    if(!lstm_params.has_cifg_opt())
+    {
+        ARM_COMPUTE_RETURN_ON_ERROR(CLTranspose::validate(lstm_params.input_to_input_weights(), &input_weights_transposed));
+        ARM_COMPUTE_RETURN_ON_ERROR(CLTranspose::validate(lstm_params.recurrent_to_input_weights(), &recurrent_weights_transposed));
+    }
+    if(lstm_params.has_projection())
+    {
+        ARM_COMPUTE_RETURN_ON_ERROR(CLTranspose::validate(lstm_params.projection_weights(), &recurrent_weights_transposed));
+    }
+
+    GEMMLowpOutputStageInfo gemmlowp_info;
+    gemmlowp_info.type               = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
+    gemmlowp_info.gemmlowp_min_bound = std::numeric_limits<int16_t>::lowest();
+    gemmlowp_info.gemmlowp_max_bound = std::numeric_limits<int16_t>::max();
+    gemmlowp_info.output_data_type   = DataType::QSYMM16;
+
+    // Forget gate.
+    const TensorInfo forget_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.forget_intermediate_scale(), 0));
+    const TensorInfo mm_out_info(TensorShape(num_units, batch_size), 1, DataType::S32);
+    const float      input_to_forget_scale = input_to_forget_weights->quantization_info().uniform().scale * qinput.scale / lstm_params.forget_intermediate_scale();
+    validate_mm(gemmlowp_info, input, &input_weights_transposed, &eff_bias_info, input_to_forget_scale, &mm_out_info, &forget_outstage_info);
+
+    const float recurrent_to_forget_scale = recurrent_to_forget_weights->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.forget_intermediate_scale();
+    validate_mm(gemmlowp_info, input, &recurrent_weights_transposed, &eff_bias_info, recurrent_to_forget_scale, &mm_out_info, &forget_outstage_info);
+
+    ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::ADD, &forget_outstage_info, &forget_outstage_info, &forget_outstage_info, ConvertPolicy::SATURATE));
+
+    if(lstm_params.has_peephole_opt())
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lstm_params.cell_to_forget_weights(), 1, DataType::QSYMM16);
+        ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(cell_state_in, lstm_params.cell_to_forget_weights(), &mm_out_info, 1.f, ConvertPolicy::SATURATE,
+                                                                              RoundingPolicy::TO_ZERO));
+        const float cell_to_forget_scale = std::pow(2, cell_shift) * lstm_params.cell_to_forget_weights()->quantization_info().uniform().scale / lstm_params.forget_intermediate_scale();
+        ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(cell_to_forget_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift));
+        ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpOutputStage::validate(&mm_out_info, nullptr, &forget_outstage_info, gemmlowp_info));
+        ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::ADD, &forget_outstage_info, &forget_outstage_info, &forget_outstage_info, ConvertPolicy::SATURATE));
+    }
+
+    // Output quantization info of Sigmoid and Tanh activations
+    const QuantizationInfo sigmoid_tanh_outqinfo(1.f / 32768.f, 0);
+
+    const TensorInfo forget_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
+    ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(&forget_outstage_info, &forget_gate_info, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)));
+
+    // Modulation gate.
+    const TensorInfo cell_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.cell_intermediate_scale(), 0));
+    const float      input_to_cell_scale = input_to_cell_weights->quantization_info().uniform().scale * qinput.scale / lstm_params.cell_intermediate_scale();
+    validate_mm(gemmlowp_info, input, &input_weights_transposed, &eff_bias_info, input_to_cell_scale, &mm_out_info, &cell_outstage_info);
+
+    const float recurrent_to_cell_scale = recurrent_to_cell_weights->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.cell_intermediate_scale();
+    validate_mm(gemmlowp_info, input, &input_weights_transposed, &eff_bias_info, recurrent_to_cell_scale, &mm_out_info, &cell_outstage_info);
+
+    ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::ADD, &cell_outstage_info, &cell_outstage_info, &cell_outstage_info, ConvertPolicy::SATURATE));
+
+    const TensorInfo cell_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
+    ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(&cell_outstage_info, &cell_gate_info, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f)));
+
+    // Input gate.
+    const TensorInfo input_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
+    if(lstm_params.has_cifg_opt())
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MSG(lstm_params.input_gate_bias() != nullptr, "Input gate bias must not be present when CIFG is used");
+        ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::SUB, &input_gate_info, &forget_gate_info, &forget_gate_info, ConvertPolicy::SATURATE));
+    }
+    else
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lstm_params.input_to_input_weights(), lstm_params.recurrent_to_input_weights(), lstm_params.input_gate_bias());
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_to_forget_weights, lstm_params.input_to_input_weights(), lstm_params.recurrent_to_input_weights());
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input_to_forget_weights, lstm_params.input_to_input_weights());
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(recurrent_to_forget_weights, lstm_params.recurrent_to_input_weights());
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(forget_gate_bias, lstm_params.input_gate_bias());
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(forget_gate_bias, lstm_params.input_gate_bias());
+
+        ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixMultiplyCore::validate(input, lstm_params.input_to_input_weights(), nullptr, &mm_out_info));
+        const TensorInfo input_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.input_intermediate_scale(), 0));
+        const float      input_to_input_scale = lstm_params.input_to_input_weights()->quantization_info().uniform().scale * qinput.scale / lstm_params.input_intermediate_scale();
+        validate_mm(gemmlowp_info, input, lstm_params.input_to_input_weights(), &eff_bias_info, input_to_input_scale, &mm_out_info, &input_outstage_info);
+
+        const float recurrent_to_input_scale = lstm_params.recurrent_to_input_weights()->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.input_intermediate_scale();
+        validate_mm(gemmlowp_info, input, lstm_params.recurrent_to_input_weights(), &eff_bias_info, recurrent_to_input_scale, &mm_out_info, &input_outstage_info);
+
+        ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::ADD, &input_outstage_info, &input_outstage_info, &input_outstage_info, ConvertPolicy::SATURATE));
+
+        if(lstm_params.has_peephole_opt())
+        {
+            ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(cell_state_in, lstm_params.cell_to_input_weights(), &input_outstage_info, 1.f, ConvertPolicy::SATURATE,
+                                                                                  RoundingPolicy::TO_ZERO));
+            const float cell_to_input_scale = std::pow(2, cell_shift) * lstm_params.cell_to_input_weights()->quantization_info().uniform().scale / lstm_params.input_intermediate_scale();
+            ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(cell_to_input_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift));
+            ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpOutputStage::validate(&input_outstage_info, &eff_bias_info, &input_outstage_info, gemmlowp_info));
+            ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::ADD, &input_outstage_info, &input_outstage_info, &input_outstage_info, ConvertPolicy::SATURATE));
+        }
+
+        ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(&input_outstage_info, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f)));
+    }
+    // Cell.
+    ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(&forget_gate_info, cell_state_in, &forget_gate_info, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(&input_gate_info, cell_state_in, &cell_gate_info, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::ADD, &forget_gate_info, &cell_gate_info, cell_state_out, ConvertPolicy::SATURATE));
+    if(quantized_cell_clip > 0)
+    {
+        ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(cell_state_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -quantized_cell_clip,
+                                                                                                             quantized_cell_clip)));
+    }
+    // Output gate.
+    const TensorInfo output_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.output_intermediate_scale(), 0));
+    const float      input_to_output_scale = input_to_output_weights->quantization_info().uniform().scale * qinput.scale / lstm_params.output_intermediate_scale();
+    validate_mm(gemmlowp_info, input, &input_weights_transposed, &eff_bias_info, input_to_output_scale, &mm_out_info, &output_outstage_info);
+
+    const float recurrent_to_output_scale = recurrent_to_output_weights->quantization_info().uniform().scale * qoutput_state_in.scale / lstm_params.output_intermediate_scale();
+    validate_mm(gemmlowp_info, output_state_in, &recurrent_weights_transposed, &eff_bias_info, recurrent_to_output_scale, &mm_out_info, &output_outstage_info);
+
+    ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::ADD, &output_outstage_info, &output_outstage_info, &output_outstage_info, ConvertPolicy::SATURATE));
+    if(lstm_params.has_peephole_opt())
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lstm_params.cell_to_output_weights(), 1, DataType::QSYMM16);
+        // TODO(COMPMID-3395): Perform multiplication in the quantized domain in NEPixelWiseMultiplicationKernel
+        // Here we are not using the output stage because all operations are done in float
+        // const float cell_to_output_scale = std::pow(2, cell_shift) * lstm_params.cell_to_output_weights()->quantization_info().uniform().scale / lstm_params.output_intermediate_scale();
+        // ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(cell_to_output_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift));
+        ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(cell_state_out, lstm_params.cell_to_output_weights(), &output_outstage_info, 1.f, ConvertPolicy::SATURATE,
+                                                                              RoundingPolicy::TO_ZERO));
+        ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::ADD, &output_outstage_info, &output_outstage_info, &output_outstage_info, ConvertPolicy::SATURATE));
+    }
+
+    const TensorInfo output_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
+    ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(&output_outstage_info, &output_gate_info, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)));
+
+    // Hidden.
+    ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(cell_state_out, &input_gate_info, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f)));
+    const TensorInfo hidden_mul_res(TensorShape(num_units, batch_size), 1, DataType::S32);
+    ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(&output_gate_info, &input_gate_info, &hidden_mul_res, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_ZERO));
+    const float hidden_state_scale = std::pow(2, -15) / lstm_params.hidden_state_scale() * std::pow(2, -15);
+    ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(hidden_state_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift, /* ignore_epsilon */ true));
+    gemmlowp_info.gemmlowp_offset = lstm_params.hidden_state_zero();
+    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpOutputStage::validate(&hidden_mul_res, nullptr, output_state_out, gemmlowp_info));
+
+    // Projection.
+    if(lstm_params.has_projection())
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(recurrent_to_forget_weights, lstm_params.projection_weights());
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(forget_gate_bias, lstm_params.projection_bias());
+
+        const UniformQuantizationInfo qprojection      = lstm_params.projection_weights()->quantization_info().uniform();
+        const float                   projection_scale = qprojection.scale * lstm_params.hidden_state_scale() / qoutput_state_in.scale;
+        ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(projection_scale, &gemmlowp_info.gemmlowp_multiplier, &gemmlowp_info.gemmlowp_shift));
+        gemmlowp_info.gemmlowp_offset    = qoutput_state_in.offset;
+        gemmlowp_info.gemmlowp_min_bound = std::numeric_limits<int8_t>::lowest();
+        gemmlowp_info.gemmlowp_max_bound = std::numeric_limits<int8_t>::max();
+        gemmlowp_info.output_data_type   = DataType::QASYMM8_SIGNED;
+
+        const TensorInfo projection_outstage_info(*output_state_out);
+        validate_mm(gemmlowp_info, output_state_out, &recurrent_weights_transposed, &eff_bias_info, input_to_output_scale, &mm_out_info, &projection_outstage_info);
+
+        ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::ADD, output_state_out, output_state_out, output_state_out, ConvertPolicy::SATURATE));
+
+        int8_t quantized_projection_clip{ 0 };
+        if(lstm_params.projection_clip() > 0.0f)
+        {
+            quantized_projection_clip = quantize_qasymm8_signed(lstm_params.projection_clip(), qprojection);
+        }
+
+        if(quantized_projection_clip > 0)
+        {
+            ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(output_state_out, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -quantized_projection_clip,
+                                                                                                                   quantized_projection_clip)));
+        }
+    }
+
+    if(cell_state_out->total_size() > 0)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(cell_state_in, cell_state_out);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(cell_state_in, cell_state_out);
+    }
+
+    if(output_state_out->total_size() > 0)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output_state_out);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output_state_in, output_state_out);
+    }
+
+    return Status{};
+}
+
+void CLQLSTMLayer::run()
+{
+    prepare();
+
+    // Acquire all the temporaries
+    MemoryGroupResourceScope scope_mg(_memory_group);
+
+    // Forget gate.
+    _mm_input_to_forget.run();
+    _input_to_forget_outstage.run();
+
+    _mm_recurrent_to_forget.run();
+    _recurrent_to_forget_outstage.run();
+    CLScheduler::get().enqueue(_accumulate_input_recurrent_forget);
+
+    if(_has_peephole)
+    {
+        CLScheduler::get().enqueue(_pixelwise_mul_cell_to_forget);
+        _cell_to_forget_outstage.run();
+        CLScheduler::get().enqueue(_accumulate_cell_forget);
+    }
+
+    _forget_gate_sigmoid.run();
+
+    // Modulation gate.
+    _mm_input_to_cell.run();
+    _input_to_cell_outstage.run();
+
+    _mm_recurrent_to_cell.run();
+    _recurrent_to_cell_outstage.run();
+    CLScheduler::get().enqueue(_accumulate_input_recurrent_modulation);
+
+    _cell_gate_tanh.run();
+
+    // Input gate
+    if(_has_cifg)
+    {
+        CLScheduler::get().enqueue(_input_gate_sub);
+    }
+    else
+    {
+        _mm_input_to_input.run();
+        _input_to_input_outstage.run();
+        _mm_recurrent_to_input.run();
+        _recurrent_to_input_outstage.run();
+        CLScheduler::get().enqueue(_accumulate_input_recurrent_input);
+
+        if(_has_peephole)
+        {
+            CLScheduler::get().enqueue(_pixelwise_mul_cell_to_input);
+            _cell_to_input_outstage.run();
+            CLScheduler::get().enqueue(_accumulate_cell_input);
+        }
+
+        _input_gate_tanh.run();
+    }
+
+    // Cell.
+    CLScheduler::get().enqueue(_pixelwise_mul_forget_cell);
+    CLScheduler::get().enqueue(_pixelwise_mul_input_cell);
+    CLScheduler::get().enqueue(_add_forget_cell);
+    if(_has_cell_clipping)
+    {
+        _cell_clip.run();
+    }
+
+    // Output gate.
+    _mm_input_to_output.run();
+    _input_to_output_outstage.run();
+    _mm_recurrent_to_output.run();
+    _recurrent_to_output_outstage.run();
+    CLScheduler::get().enqueue(_accumulate_input_recurrent_output);
+    if(_has_peephole)
+    {
+        CLScheduler::get().enqueue(_pixelwise_mul_cell_to_output);
+        CLScheduler::get().enqueue(_accumulate_cell_to_output);
+    }
+
+    _output_gate_sigmoid.run();
+
+    // Hidden.
+    _hidden_tanh.run();
+    CLScheduler::get().enqueue(_pixelwise_mul_hidden);
+    _hidden_outstage.run();
+
+    // Projection.
+    if(_has_projection)
+    {
+        _mm_projection.run();
+        _projection_outstage.run();
+        CLScheduler::get().enqueue(_accumulate_projection);
+        if(_has_projection_clipping)
+        {
+            _projection_clip.run();
+        }
+    }
+}
+
+void CLQLSTMLayer::prepare()
+{
+    if(!_is_prepared)
+    {
+        // Pre-transpose weights to be used in GEMM.
+        _input_to_forget_weights_transposed.allocator()->allocate();
+        _input_to_cell_weights_transposed.allocator()->allocate();
+        _input_to_output_weights_transposed.allocator()->allocate();
+        _recurrent_to_forget_weights_transposed.allocator()->allocate();
+        _recurrent_to_cell_weights_transposed.allocator()->allocate();
+        _recurrent_to_output_weights_transposed.allocator()->allocate();
+        _transpose_input_to_forget_weights.run();
+        _transpose_input_to_cell_weights.run();
+        _transpose_input_to_output_weights.run();
+        _transpose_recurrent_to_forget_weights.run();
+        _transpose_recurrent_to_cell_weights.run();
+        _transpose_recurrent_to_output_weights.run();
+
+        // Precompute effective biases
+        if(_has_cifg)
+        {
+            _ones.map(true);
+            std::fill_n(reinterpret_cast<int16_t *>(_ones.buffer()), _ones.info()->total_size() / _ones.info()->element_size(), 32767);
+            _ones.unmap();
+        }
+        else
+        {
+            _input_to_input_eff_bias.allocator()->allocate();
+            _recurrent_to_input_eff_bias.allocator()->allocate();
+            CLScheduler::get().enqueue(_input_to_input_reduction);
+            CLScheduler::get().enqueue(_recurrent_to_input_reduction);
+
+            _input_to_input_weights_transposed.allocator()->allocate();
+            _recurrent_to_input_weights_transposed.allocator()->allocate();
+            _transpose_input_to_input_weights.run();
+            _transpose_recurrent_to_input_weights.run();
+            _input_to_input_weights->mark_as_unused();
+            _recurrent_to_input_weights->mark_as_unused();
+        }
+        _input_to_forget_eff_bias.allocator()->allocate();
+        _recurrent_to_forget_eff_bias.allocator()->allocate();
+        _input_to_cell_eff_bias.allocator()->allocate();
+        _recurrent_to_cell_eff_bias.allocator()->allocate();
+        _input_to_output_eff_bias.allocator()->allocate();
+        _recurrent_to_output_eff_bias.allocator()->allocate();
+        CLScheduler::get().enqueue(_input_to_forget_reduction);
+        CLScheduler::get().enqueue(_recurrent_to_forget_reduction);
+        CLScheduler::get().enqueue(_input_to_cell_reduction);
+        CLScheduler::get().enqueue(_recurrent_to_cell_reduction);
+        CLScheduler::get().enqueue(_input_to_output_reduction);
+        CLScheduler::get().enqueue(_recurrent_to_output_reduction);
+
+        if(_has_projection)
+        {
+            if(_projection_bias != nullptr)
+            {
+                _projection_eff_bias.allocator()->allocate();
+                CLScheduler::get().enqueue(_projection_reduction);
+                _projection_bias->mark_as_unused();
+            }
+
+            _projection_weights_transposed.allocator()->allocate();
+            _transpose_projection_weights.run();
+            _projection_weights->mark_as_unused();
+        }
+
+        // Mark weights as unused
+        _input_to_forget_weights->mark_as_unused();
+        _input_to_cell_weights->mark_as_unused();
+        _input_to_output_weights->mark_as_unused();
+        _recurrent_to_forget_weights->mark_as_unused();
+        _recurrent_to_cell_weights->mark_as_unused();
+        _recurrent_to_output_weights->mark_as_unused();
+
+        CLScheduler::get().queue().finish();
+        _is_prepared = true;
+    }
+}
+
+} // namespace arm_compute