diff --git a/tests/validation/CL/LSTMLayer.cpp b/tests/validation/CL/LSTMLayer.cpp
new file mode 100644
index 0000000..bd43678
--- /dev/null
+++ b/tests/validation/CL/LSTMLayer.cpp
@@ -0,0 +1,177 @@
+/*
+ * Copyright (c) 2018 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/CLLSTMLayer.h"
+#include "tests/CL/CLAccessor.h"
+#include "tests/PaddingCalculator.h"
+#include "tests/datasets/LSTMLayerDataset.h"
+#include "tests/framework/Asserts.h"
+#include "tests/framework/Macros.h"
+#include "tests/framework/datasets/Datasets.h"
+#include "tests/validation/Validation.h"
+#include "tests/validation/fixtures/LSTMLayerFixture.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace
+{
+RelativeTolerance<float> tolerance_f32(0.001f);
+RelativeTolerance<half>  tolerance_f16(half(0.1));
+} // namespace
+
+TEST_SUITE(CL)
+TEST_SUITE(LSTMLayer)
+
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip(zip(zip(
+               framework::dataset::make("InputInfo", { TensorInfo(TensorShape(8U, 2U), 1, DataType::U8, 0),      // Wrong data type
+                                                       TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Wrong input size
+                                                       TensorInfo(TensorShape(8U, 2U), 1, DataType::F32, 0),     // Wrong input weights size
+                                                       TensorInfo(TensorShape(8U, 2U), 1, DataType::F32, 0),     // Wrong recurrent weights size
+                                                       TensorInfo(TensorShape(8U, 2U), 1, DataType::F32, 0),     // Wrong cell bias size
+                                                       TensorInfo(TensorShape(8U, 2U), 1, DataType::F32, 0),     // Wrong cell state size
+                                                       TensorInfo(TensorShape(8U, 2U), 1, DataType::F32, 0),     // Wrong output size
+                                                       TensorInfo(TensorShape(8U, 2U), 1, DataType::F32, 0),     // Wrong scratch size
+               }),
+               framework::dataset::make("InputWeightsInfo", { TensorInfo(TensorShape(8U, 16U), 1, DataType::F32, 0),
+                                                       TensorInfo(TensorShape(8U, 16U), 1, DataType::F32, 0),
+                                                       TensorInfo(TensorShape(27U, 11U, 2U), 1, DataType::F32, 0),
+                                                       TensorInfo(TensorShape(8U, 16U), 1, DataType::F32, 0),
+                                                       TensorInfo(TensorShape(8U, 16U), 1, DataType::F32, 0),
+                                                       TensorInfo(TensorShape(8U, 16U), 1, DataType::F32, 0),
+                                                       TensorInfo(TensorShape(8U, 16U), 1, DataType::F32, 0),
+                                                       TensorInfo(TensorShape(8U, 16U), 1, DataType::F32, 0),
+               })),
+               framework::dataset::make("RecurrentWeightsInfo", { TensorInfo(TensorShape(16U, 16U), 1, DataType::F32, 0),
+                                                                  TensorInfo(TensorShape(16U, 16U), 1, DataType::F32, 0),
+                                                                  TensorInfo(TensorShape(16U, 16U), 1, DataType::F32, 0),
+                                                                  TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32, 0),
+                                                                  TensorInfo(TensorShape(16U, 16U), 1, DataType::F32, 0),
+                                                                  TensorInfo(TensorShape(16U, 16U), 1, DataType::F32, 0),
+                                                                  TensorInfo(TensorShape(16U, 16U), 1, DataType::F32, 0),
+                                                                  TensorInfo(TensorShape(16U, 16U), 1, DataType::F32, 0),
+               })),
+               framework::dataset::make("CellBiasInfo", { TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+                                                      TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+                                                      TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+                                                      TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+                                                      TensorInfo(TensorShape(30U), 1, DataType::F32, 0),
+                                                      TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+                                                      TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+                                                      TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+               })),
+               framework::dataset::make("ProjectionBiasInfo", { TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+                                                      TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+                                                      TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+                                                      TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+                                                      TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+                                                      TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+                                                      TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+                                                      TensorInfo(TensorShape(16U), 1, DataType::F32, 0),
+               })),
+               framework::dataset::make("CellStateInfo", { TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(11U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+               })),
+               framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(11U, 13U), 1, DataType::F32, 0),
+                                                        TensorInfo(TensorShape(16U, 2U), 1, DataType::F32, 0),
+               })),
+               framework::dataset::make("ScratchInfo", { TensorInfo(TensorShape(64U, 2U), 1, DataType::F32, 0),
+                                                             TensorInfo(TensorShape(64U, 2U), 1, DataType::F32, 0),
+                                                             TensorInfo(TensorShape(64U, 2U), 1, DataType::F32, 0),
+                                                             TensorInfo(TensorShape(64U, 2U), 1, DataType::F32, 0),
+                                                             TensorInfo(TensorShape(64U, 2U), 1, DataType::F32, 0),
+                                                             TensorInfo(TensorShape(64U, 2U), 1, DataType::F32, 0),
+                                                             TensorInfo(TensorShape(64U, 2U), 1, DataType::F32, 0),
+                                                             TensorInfo(TensorShape(12U, 2U), 1, DataType::F32, 0),
+               })),
+               framework::dataset::make("ActivationInfo", { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+                                                            ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+                                                            ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+                                                            ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+                                                            ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+                                                            ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+                                                            ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+                                                            ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+               })),
+               framework::dataset::make("Expected", { false, false, false, false, false, false, false, false })),
+               input_info, input_weights_info, recurrent_weights_info, cell_bias_info, projection_bias_info, cell_state_info, output_info, scratch_info, info, expected)
+{
+    LSTMParams<ITensorInfo> lstm_params_info;
+    lstm_params_info.set_peephole_params(&cell_bias_info, &cell_bias_info, &cell_bias_info)
+                    .set_projection_params(&recurrent_weights_info, &projection_bias_info)
+                    .set_cifg_params(&input_weights_info, &recurrent_weights_info, &cell_bias_info, &cell_bias_info);
+
+    ARM_COMPUTE_EXPECT(bool(CLLSTMLayer::validate(&input_info.clone()->set_is_resizable(false), &input_weights_info.clone()->set_is_resizable(false), &input_weights_info.clone()->set_is_resizable(false),
+                                                  &input_weights_info.clone()->set_is_resizable(false), &recurrent_weights_info.clone()->set_is_resizable(false), &recurrent_weights_info.clone()->set_is_resizable(false),
+                                                  &recurrent_weights_info.clone()->set_is_resizable(false), &cell_bias_info.clone()->set_is_resizable(false), &cell_bias_info.clone()->set_is_resizable(false),
+                                                  &cell_bias_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), &cell_state_info.clone()->set_is_resizable(false),
+                                                  &scratch_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), lstm_params_info, info, 0.05, 0.9)) == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
+template <typename T>
+using CLLSTMLayerFixture = LSTMLayerValidationFixture<CLTensor, CLAccessor, CLLSTMLayer, LSTMParams<ICLTensor>, T>;
+
+TEST_SUITE(FP32)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLLSTMLayerFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallLSTMLayerDataset(), framework::dataset::make("DataType",
+                                                                                                                 DataType::F32)),
+                                                                                                         framework::dataset::make("ProjectionOpt", { true, false })),
+                                                                                                 framework::dataset::make("PeepholeOpt", { true, false })))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_f32);
+}
+TEST_SUITE_END() // FP32
+
+TEST_SUITE(FP16)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLLSTMLayerFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallLSTMLayerDataset(), framework::dataset::make("DataType", DataType::F16)),
+                                                                                                        framework::dataset::make("ProjectionOpt", { true, false })),
+                                                                                                framework::dataset::make("PeepholeOpt", { true, false })))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_f16);
+}
+TEST_SUITE_END() // FP16
+TEST_SUITE_END() // LSTMLayer
+TEST_SUITE_END() // CL
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
diff --git a/tests/validation/fixtures/LSTMLayerFixture.h b/tests/validation/fixtures/LSTMLayerFixture.h
new file mode 100644
index 0000000..b7e43b3
--- /dev/null
+++ b/tests/validation/fixtures/LSTMLayerFixture.h
@@ -0,0 +1,404 @@
+/*
+ * Copyright (c) 2018 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_TEST_LSTM_LAYER_FIXTURE
+#define ARM_COMPUTE_TEST_LSTM_LAYER_FIXTURE
+
+#include "tests/Globals.h"
+#include "tests/framework/Asserts.h"
+#include "tests/framework/Fixture.h"
+#include "tests/validation/reference/ActivationLayer.h"
+#include "tests/validation/reference/ArithmeticAddition.h"
+#include "tests/validation/reference/ArithmeticSubtraction.h"
+#include "tests/validation/reference/FullyConnectedLayer.h"
+#include "tests/validation/reference/GEMM.h"
+#include "tests/validation/reference/PixelWiseMultiplication.h"
+#include "tests/validation/reference/Transpose.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+template <typename TensorType, typename AccessorType, typename FunctionType, typename FunctionParams, typename T>
+class LSTMLayerValidationFixture : public framework::Fixture
+{
+public:
+    template <typename...>
+    void setup(TensorShape input_shape, TensorShape input_weights_shape, TensorShape recurrent_weights_shape, TensorShape cell_bias_shape, TensorShape output_cell_shape, TensorShape output_shape,
+               TensorShape scratch_shape, ActivationLayerInfo info, float cell_threshold, float projection_threshold, DataType data_type, bool projection_opt, bool peephole_opt)
+    {
+        _target = compute_target(input_shape, input_weights_shape, recurrent_weights_shape, cell_bias_shape, output_cell_shape, output_shape, scratch_shape, info, cell_threshold, projection_threshold,
+                                 data_type, projection_opt, peephole_opt);
+        _reference = compute_reference(input_shape, input_weights_shape, recurrent_weights_shape, cell_bias_shape, output_cell_shape, output_shape, scratch_shape, info, cell_threshold, projection_threshold,
+                                       data_type, projection_opt, peephole_opt);
+    }
+
+protected:
+    template <typename U>
+    void fill(U &&tensor, int i)
+    {
+        std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
+        library->fill(tensor, distribution, i);
+    }
+    template <typename U>
+    void fill_custom_val(U &&tensor, float num, int i)
+    {
+        std::uniform_real_distribution<> distribution(num, num);
+        library->fill(tensor, distribution, i);
+    }
+    TensorType compute_target(const TensorShape &input_shape, const TensorShape &input_weights_shape, const TensorShape &recurrent_weights_shape, const TensorShape &cell_bias_shape,
+                              const TensorShape &output_cell_shape, const TensorShape &output_shape, const TensorShape &scratch_shape, ActivationLayerInfo info, float cell_threshold,
+                              float projection_threshold, DataType data_type, bool projection_opt, bool peephole_opt)
+    {
+        // Create projection bias shape
+        TensorShape projection_bias_shape{};
+        projection_bias_shape.set(0, output_shape.x());
+
+        // Create tensors
+        TensorType input                 = create_tensor<TensorType>(input_shape, data_type);
+        TensorType input_to_forget_w     = create_tensor<TensorType>(input_weights_shape, data_type);
+        TensorType input_to_cell_w       = create_tensor<TensorType>(input_weights_shape, data_type);
+        TensorType input_to_output_w     = create_tensor<TensorType>(input_weights_shape, data_type);
+        TensorType recurrent_to_forget_w = create_tensor<TensorType>(recurrent_weights_shape, data_type);
+        TensorType recurrent_to_cell_w   = create_tensor<TensorType>(recurrent_weights_shape, data_type);
+        TensorType recurrent_to_output_w = create_tensor<TensorType>(recurrent_weights_shape, data_type);
+        TensorType forget_gate_bias      = create_tensor<TensorType>(cell_bias_shape, data_type);
+        TensorType cell_bias             = create_tensor<TensorType>(cell_bias_shape, data_type);
+        TensorType output_gate_bias      = create_tensor<TensorType>(cell_bias_shape, data_type);
+        TensorType output_state          = create_tensor<TensorType>(output_shape, data_type);
+        TensorType cell_state            = create_tensor<TensorType>(output_cell_shape, data_type);
+        TensorType scratch               = create_tensor<TensorType>(scratch_shape, data_type);
+        TensorType output                = create_tensor<TensorType>(output_shape, data_type);
+        TensorType input_to_input_w;
+        TensorType recurrent_to_input_w;
+        TensorType cell_to_input_w;
+        TensorType cell_to_forget_w;
+        TensorType input_gate_bias;
+        TensorType cell_to_output_w;
+        TensorType projection_w;
+        TensorType projection_bias;
+
+        bool cifg_opt = scratch_shape.x() == cell_bias_shape.x() * 4 ? true : false;
+
+        FunctionParams lstm_params;
+
+        if(!cifg_opt)
+        {
+            input_to_input_w     = create_tensor<TensorType>(input_weights_shape, data_type);
+            recurrent_to_input_w = create_tensor<TensorType>(recurrent_weights_shape, data_type);
+            cell_to_input_w      = create_tensor<TensorType>(cell_bias_shape, data_type);
+            input_gate_bias      = create_tensor<TensorType>(cell_bias_shape, data_type);
+            lstm_params.set_cifg_params(&input_to_input_w, &recurrent_to_input_w, &cell_to_input_w, &input_gate_bias);
+        }
+
+        if(peephole_opt)
+        {
+            if(cifg_opt)
+            {
+                cell_to_input_w = create_tensor<TensorType>(cell_bias_shape, data_type);
+            }
+            cell_to_forget_w = create_tensor<TensorType>(cell_bias_shape, data_type);
+            cell_to_output_w = create_tensor<TensorType>(cell_bias_shape, data_type);
+            lstm_params.set_peephole_params(&cell_to_input_w, &cell_to_forget_w, &cell_to_output_w);
+        }
+
+        if(projection_opt)
+        {
+            projection_w    = create_tensor<TensorType>(recurrent_weights_shape, data_type);
+            projection_bias = create_tensor<TensorType>(projection_bias_shape, data_type);
+            lstm_params.set_projection_params(&projection_w, &projection_bias);
+        }
+
+        // Create and configure function
+        FunctionType lstm;
+        lstm.configure(&input, &input_to_forget_w, &input_to_cell_w, &input_to_output_w, &recurrent_to_forget_w,
+                       &recurrent_to_cell_w, &recurrent_to_output_w, &forget_gate_bias, &cell_bias, &output_gate_bias, &output_state, &cell_state,
+                       &scratch, &output, lstm_params, info, cell_threshold, projection_threshold);
+
+        ARM_COMPUTE_EXPECT(input.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(input_to_forget_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(input_to_cell_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(input_to_output_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(recurrent_to_forget_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(recurrent_to_cell_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(recurrent_to_output_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(forget_gate_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(cell_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(output_gate_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(output_state.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(cell_state.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(scratch.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(output.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+        // Allocate tensors
+        input.allocator()->allocate();
+        input_to_forget_w.allocator()->allocate();
+        input_to_cell_w.allocator()->allocate();
+        input_to_output_w.allocator()->allocate();
+        recurrent_to_forget_w.allocator()->allocate();
+        recurrent_to_cell_w.allocator()->allocate();
+        recurrent_to_output_w.allocator()->allocate();
+        forget_gate_bias.allocator()->allocate();
+        cell_bias.allocator()->allocate();
+        output_gate_bias.allocator()->allocate();
+        output_state.allocator()->allocate();
+        cell_state.allocator()->allocate();
+        scratch.allocator()->allocate();
+        output.allocator()->allocate();
+
+        ARM_COMPUTE_EXPECT(!input.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!input_to_forget_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!input_to_cell_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!input_to_output_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!recurrent_to_forget_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!recurrent_to_cell_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!recurrent_to_output_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!forget_gate_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!cell_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!output_gate_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!output_state.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!cell_state.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!scratch.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!output.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+        // Fill tensors
+        fill(AccessorType(input), 0);
+        fill(AccessorType(input_to_forget_w), 1);
+        fill(AccessorType(input_to_cell_w), 2);
+        fill(AccessorType(input_to_output_w), 3);
+        fill(AccessorType(recurrent_to_forget_w), 4);
+        fill(AccessorType(recurrent_to_cell_w), 5);
+        fill(AccessorType(recurrent_to_output_w), 6);
+        fill(AccessorType(forget_gate_bias), 7);
+        fill(AccessorType(cell_bias), 8);
+        fill(AccessorType(output_gate_bias), 9);
+        fill(AccessorType(output_state), 10);
+        fill(AccessorType(cell_state), 11);
+        fill(AccessorType(scratch), 12);
+
+        if(!cifg_opt)
+        {
+            ARM_COMPUTE_EXPECT(input_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+            ARM_COMPUTE_EXPECT(recurrent_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+            ARM_COMPUTE_EXPECT(cell_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+            ARM_COMPUTE_EXPECT(input_gate_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+            input_to_input_w.allocator()->allocate();
+            recurrent_to_input_w.allocator()->allocate();
+            cell_to_input_w.allocator()->allocate();
+            input_gate_bias.allocator()->allocate();
+            ARM_COMPUTE_EXPECT(!input_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+            ARM_COMPUTE_EXPECT(!recurrent_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+            ARM_COMPUTE_EXPECT(!cell_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+            ARM_COMPUTE_EXPECT(!input_gate_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+            fill(AccessorType(input_to_input_w), 13);
+            fill(AccessorType(recurrent_to_input_w), 14);
+            fill(AccessorType(cell_to_input_w), 15);
+            fill(AccessorType(recurrent_to_input_w), 16);
+            fill(AccessorType(input_gate_bias), 17);
+        }
+
+        if(peephole_opt)
+        {
+            if(cifg_opt)
+            {
+                ARM_COMPUTE_EXPECT(cell_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+                cell_to_input_w.allocator()->allocate();
+                ARM_COMPUTE_EXPECT(!cell_to_input_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+                fill(AccessorType(cell_to_input_w), 15);
+            }
+            ARM_COMPUTE_EXPECT(cell_to_forget_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+            ARM_COMPUTE_EXPECT(cell_to_output_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+            cell_to_forget_w.allocator()->allocate();
+            cell_to_output_w.allocator()->allocate();
+            ARM_COMPUTE_EXPECT(!cell_to_forget_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+            ARM_COMPUTE_EXPECT(!cell_to_output_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+            fill(AccessorType(cell_to_output_w), 18);
+        }
+
+        if(projection_opt)
+        {
+            ARM_COMPUTE_EXPECT(projection_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+            ARM_COMPUTE_EXPECT(projection_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+            projection_w.allocator()->allocate();
+            projection_bias.allocator()->allocate();
+
+            ARM_COMPUTE_EXPECT(!projection_w.info()->is_resizable(), framework::LogLevel::ERRORS);
+            ARM_COMPUTE_EXPECT(!projection_bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+            fill(AccessorType(projection_w), 19);
+            fill(AccessorType(projection_bias), 20);
+        }
+
+        // Compute function
+        lstm.run();
+
+        return output;
+    }
+
+    SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &input_weights_shape, const TensorShape &recurrent_weights_shape, const TensorShape &cell_bias_shape,
+                                      const TensorShape &output_cell_shape, const TensorShape &output_shape, const TensorShape &scratch_shape, ActivationLayerInfo info, float cell_threshold,
+                                      float projection_threshold, DataType data_type, bool projection_opt, bool peephole_opt)
+    {
+        // Create projection bias shape
+        TensorShape projection_bias_shape{};
+        projection_bias_shape.set(0, output_shape.x());
+
+        TensorShape     gemm_shape{ 1, output_shape.y() };
+        SimpleTensor<T> gemm_out{ gemm_shape, data_type };
+
+        // Create reference
+        SimpleTensor<T> input{ input_shape, data_type };
+        SimpleTensor<T> input_to_input_w{ input_weights_shape, data_type };
+        SimpleTensor<T> input_to_forget_w{ input_weights_shape, data_type };
+        SimpleTensor<T> input_to_cell_w{ input_weights_shape, data_type };
+        SimpleTensor<T> input_to_output_w{ input_weights_shape, data_type };
+        SimpleTensor<T> recurrent_to_input_w{ recurrent_weights_shape, data_type };
+        SimpleTensor<T> recurrent_to_forget_w{ recurrent_weights_shape, data_type };
+        SimpleTensor<T> recurrent_to_cell_w{ recurrent_weights_shape, data_type };
+        SimpleTensor<T> recurrent_to_output_w{ recurrent_weights_shape, data_type };
+        SimpleTensor<T> cell_to_input_w{ cell_bias_shape, data_type };
+        SimpleTensor<T> cell_to_forget_w{ cell_bias_shape, data_type };
+        SimpleTensor<T> cell_to_output_w{ cell_bias_shape, data_type };
+        SimpleTensor<T> input_gate_bias{ cell_bias_shape, data_type };
+        SimpleTensor<T> forget_gate_bias{ cell_bias_shape, data_type };
+        SimpleTensor<T> cell_bias{ cell_bias_shape, data_type };
+        SimpleTensor<T> output_gate_bias{ cell_bias_shape, data_type };
+        SimpleTensor<T> projection_w{ recurrent_weights_shape, data_type };
+        SimpleTensor<T> projection_bias{ projection_bias_shape, data_type };
+        SimpleTensor<T> output_state{ output_shape, data_type };
+        SimpleTensor<T> cell_state{ output_cell_shape, data_type };
+        SimpleTensor<T> scratch{ scratch_shape, data_type };
+        SimpleTensor<T> output{ output_shape, data_type };
+
+        // Fill reference
+        fill(input, 0);
+        fill(input_to_forget_w, 1);
+        fill(input_to_cell_w, 2);
+        fill(input_to_output_w, 3);
+        fill(recurrent_to_forget_w, 4);
+        fill(recurrent_to_cell_w, 5);
+        fill(recurrent_to_output_w, 6);
+        fill(forget_gate_bias, 7);
+        fill(cell_bias, 8);
+        fill(output_gate_bias, 9);
+        fill(output_state, 10);
+        fill(cell_state, 11);
+        fill(scratch, 12);
+        fill(input_to_input_w, 13);
+        fill(recurrent_to_input_w, 14);
+        fill(cell_to_input_w, 15);
+        fill(recurrent_to_input_w, 16);
+        fill(input_gate_bias, 17);
+        fill(cell_to_output_w, 18);
+        fill(projection_w, 19);
+        fill(projection_bias, 20);
+
+        bool cifg_opt = scratch_shape.x() == cell_bias_shape.x() * 4 ? true : false;
+
+        // Compute forget_gate
+        SimpleTensor<T> fully_connected_forget = reference::fully_connected_layer(input, input_to_forget_w, forget_gate_bias, output_cell_shape);
+        SimpleTensor<T> transposed_weights     = reference::transpose(recurrent_to_forget_w);
+        SimpleTensor<T> gemm                   = reference::gemm(output_state, transposed_weights, cell_state, 1.f, 0.f);
+        SimpleTensor<T> forget_gate            = reference::arithmetic_addition(fully_connected_forget, gemm, data_type, ConvertPolicy::SATURATE);
+
+        if(peephole_opt)
+        {
+            transposed_weights = reference::transpose(cell_to_forget_w);
+            gemm               = reference::gemm(cell_state, transposed_weights, gemm_out, 1.f, 0.f);
+            forget_gate        = reference::arithmetic_addition(forget_gate, gemm, data_type, ConvertPolicy::SATURATE);
+        }
+
+        forget_gate = reference::activation_layer(forget_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
+
+        // Compute input_gate
+        SimpleTensor<T> input_gate;
+        if(cifg_opt)
+        {
+            SimpleTensor<T> ones{ cell_bias_shape, data_type };
+            fill_custom_val(ones, 1.f, 0);
+            input_gate = reference::arithmetic_subtraction<T, T, T>(ones, forget_gate, data_type, ConvertPolicy::SATURATE);
+        }
+        else
+        {
+            SimpleTensor<T> fully_connected_input = reference::fully_connected_layer(input, input_to_input_w, input_gate_bias, output_cell_shape);
+            transposed_weights                    = reference::transpose(recurrent_to_input_w);
+            gemm                                  = reference::gemm(output_state, transposed_weights, cell_state, 1.f, 0.f);
+            input_gate                            = reference::arithmetic_addition(fully_connected_input, gemm, data_type, ConvertPolicy::SATURATE);
+            transposed_weights                    = reference::transpose(cell_to_input_w);
+            gemm                                  = reference::gemm(cell_state, transposed_weights, gemm_out, 1.f, 0.f);
+            input_gate                            = reference::arithmetic_addition(input_gate, gemm, data_type, ConvertPolicy::SATURATE);
+            input_gate                            = reference::activation_layer(input_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
+        }
+
+        // Compute cell_state
+        SimpleTensor<T> fully_connected_cell_state = reference::fully_connected_layer(input, input_to_cell_w, cell_bias, output_cell_shape);
+        transposed_weights                         = reference::transpose(recurrent_to_cell_w);
+        gemm                                       = reference::gemm(output_state, transposed_weights, cell_state, 1.f, 0.f);
+        SimpleTensor<T> pixelwise_mul              = reference::pixel_wise_multiplication(cell_state, forget_gate, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
+        cell_state                                 = reference::arithmetic_addition(fully_connected_cell_state, gemm, data_type, ConvertPolicy::SATURATE);
+        cell_state                                 = reference::activation_layer(cell_state, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
+        cell_state                                 = reference::pixel_wise_multiplication(cell_state, input_gate, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
+        cell_state                                 = reference::arithmetic_addition(cell_state, pixelwise_mul, data_type, ConvertPolicy::SATURATE);
+        if(cell_threshold != 0.f)
+        {
+            cell_state = reference::activation_layer(cell_state, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -cell_threshold, cell_threshold));
+        }
+
+        // Compute output
+        SimpleTensor<T> fully_connected_output = reference::fully_connected_layer(input, input_to_output_w, output_gate_bias, output_cell_shape);
+        transposed_weights                     = reference::transpose(recurrent_to_output_w);
+        gemm                                   = reference::gemm(output_state, transposed_weights, cell_state, 1.f, 0.f);
+        output                                 = reference::arithmetic_addition(fully_connected_output, gemm, data_type, ConvertPolicy::SATURATE);
+        if(peephole_opt)
+        {
+            transposed_weights = reference::transpose(cell_to_output_w);
+            gemm               = reference::gemm(cell_state, transposed_weights, gemm_out, 1.f, 0.f);
+            output             = reference::arithmetic_addition(output, gemm, data_type, ConvertPolicy::SATURATE);
+        }
+        output = reference::activation_layer(output, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
+
+        // Compute output state
+        SimpleTensor<T> cell_state_activation = reference::activation_layer(cell_state, info);
+        output_state                          = reference::pixel_wise_multiplication(output, cell_state_activation, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN);
+
+        if(projection_opt)
+        {
+            SimpleTensor<T> fully_connected_projection = reference::fully_connected_layer(output_state, projection_w, projection_bias, output_cell_shape);
+            if(projection_threshold != 0.f)
+            {
+                output_state = reference::activation_layer(fully_connected_projection, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, -projection_threshold, projection_threshold));
+            }
+        }
+        return output_state;
+    }
+
+    TensorType      _target{};
+    SimpleTensor<T> _reference{};
+};
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_TEST_LSTM_LAYER_FIXTURE */