COMPMID-846: Create a ConvolutionLayer for NEON

Change-Id: I98bbef40bfac5b05134be4ef9fb54d14c0c9e8e8
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/118806
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

1 files changed, 652 insertions, 0 deletions

diff --git a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
new file mode 100644
index 0000000000..d0a16ef40d
--- /dev/null
+++ b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
@@ -0,0 +1,652 @@
+/*
+ * Copyright (c) 2017-2018 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/NEON/functions/NEGEMMConvolutionLayer.h"
+
+#include "arm_compute/core/NEON/kernels/arm32/NEGEMMAArch32Kernel.h"
+#include "arm_compute/core/NEON/kernels/arm64/NEGEMMAArch64Kernel.h"
+#include "arm_compute/core/NEON/kernels/arm64/NEGEMMAArch64NativeKernel.h"
+#include "arm_compute/core/PixelValue.h"
+#include "arm_compute/core/Size2D.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
+#include "arm_compute/runtime/NEON/NEScheduler.h"
+#include "support/ToolchainSupport.h"
+
+namespace arm_compute
+{
+#include "arm_compute/core/NEON/kernels/assembly/gemm_interleaved.hpp"
+#include "arm_compute/core/NEON/kernels/assembly/kernels/a32_sgemm_8x6.hpp"
+#include "arm_compute/core/NEON/kernels/assembly/kernels/a64_sgemm_12x8.hpp"
+} // namespace arm_compute
+
+#include <cmath>
+#include <tuple>
+
+namespace
+{
+arm_compute::TensorShape get_reshaped_weights_shape(const arm_compute::ITensorInfo *weights, bool append_bias)
+{
+    const unsigned int mat_weights_cols = weights->dimension(3);
+    const unsigned int mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (append_bias ? 1 : 0);
+    return arm_compute::TensorShape(mat_weights_cols, mat_weights_rows);
+}
+} // namespace
+
+namespace arm_compute
+{
+NEConvolutionLayerReshapeWeights::NEConvolutionLayerReshapeWeights(std::shared_ptr<IMemoryManager> memory_manager)
+    : _memory_group(std::move(memory_manager)), _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false)
+{
+}
+
+void NEConvolutionLayerReshapeWeights::configure(const ITensor *weights, const ITensor *biases, ITensor *output, bool transpose1xW)
+{
+    // Perform validation step
+    ARM_COMPUTE_ERROR_ON_NULLPTR(weights, output);
+    ARM_COMPUTE_ERROR_THROW_ON(NEConvolutionLayerReshapeWeights::validate(weights->info(),
+                                                                          (biases != nullptr) ? biases->info() : nullptr,
+                                                                          output->info(),
+                                                                          transpose1xW));
+
+    // Check if bias are present, if yes they will be embedded to the weights matrix
+    const bool append_biases = (biases != nullptr) && !is_data_type_quantized_asymmetric(weights->info()->data_type());
+    //const unsigned bias_element  = (append_biases) ? 1 : 0;
+    const ITensor *biases_to_use = (append_biases) ? biases : nullptr;
+
+    _transpose1xW = transpose1xW;
+
+    if(transpose1xW)
+    {
+        // Create tensor to store the reshaped weights
+        TensorInfo info_wr = weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(get_reshaped_weights_shape(weights->info(), append_biases));
+
+        _weights_reshaped.allocator()->init(info_wr);
+        _memory_group.manage(&_weights_reshaped);
+
+        _weights_reshape_kernel.configure(weights, biases, &_weights_reshaped);
+        _weights_transposed_kernel.configure(&_weights_reshaped, output);
+
+        _weights_reshaped.allocator()->allocate();
+    }
+    else
+    {
+        _weights_reshape_kernel.configure(weights, biases_to_use, output);
+    }
+
+    output->info()->set_quantization_info(weights->info()->quantization_info());
+}
+
+Status NEConvolutionLayerReshapeWeights::validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, bool transpose1xW)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
+    if(!is_data_type_quantized_asymmetric(weights->data_type()))
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output);
+    }
+    // Check if bias are present, if yes they will be embedded to the weights matrix
+    const bool append_bias = (biases != nullptr);
+
+    if(append_bias)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(weights->data_type()));
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(weights, biases);
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
+    }
+
+    // Checks performed when biases are present
+    if(append_bias)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
+    }
+
+    if(transpose1xW)
+    {
+        TensorInfo weights_reshaped = weights->clone()->set_tensor_shape(get_reshaped_weights_shape(weights, append_bias));
+        ARM_COMPUTE_RETURN_ON_ERROR(NEWeightsReshapeKernel::validate(weights, biases, &weights_reshaped));
+        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMTranspose1xWKernel::validate(&weights_reshaped, output));
+    }
+    else
+    {
+        ARM_COMPUTE_RETURN_ON_ERROR(NEWeightsReshapeKernel::validate(weights, biases, output));
+    }
+
+    return Status{};
+}
+
+void NEConvolutionLayerReshapeWeights::run()
+{
+    _memory_group.acquire();
+
+    NEScheduler::get().schedule(&_weights_reshape_kernel, 3);
+
+    if(_transpose1xW)
+    {
+        NEScheduler::get().schedule(&_weights_transposed_kernel, Window::DimY);
+    }
+
+    _memory_group.release();
+}
+
+namespace
+{
+TensorShape get_reshaped_weights_shape_conv(const ITensorInfo *weights, bool append_bias, bool is_fully_connected_convolution)
+{
+    unsigned int mat_weights_cols = weights->dimension(3);
+    unsigned int mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (append_bias ? 1 : 0);
+
+    if(is_fully_connected_convolution)
+    {
+        // Create tensor to store the reshaped weights
+        return TensorShape(mat_weights_cols, mat_weights_rows);
+    }
+    else
+    {
+        // Create tensor to store transposed weights
+        const float transpose_width = 16.0f / weights->element_size();
+        return TensorShape(mat_weights_rows * static_cast<unsigned int>(transpose_width), static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)));
+    }
+}
+
+Status validate_and_initialize_values(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, DataType &dt,
+                                      bool &append_bias,
+                                      bool &are_weights_reshaped, unsigned int &kernel_width, unsigned int &kernel_height,
+                                      bool &is_fully_connected_convolution, bool &is_interleaved_transposed, bool &is_quantized,
+                                      unsigned int &mat_weights_cols, unsigned int &mat_weights_rows,
+                                      unsigned int &conv_w, unsigned int &conv_h)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights);
+    ARM_COMPUTE_RETURN_ERROR_ON(!weights_info.are_reshaped() && weights->dimension(2) != input->dimension(2));
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
+    ARM_COMPUTE_RETURN_ERROR_ON(weights_info.are_reshaped() && is_data_type_quantized_asymmetric(input->data_type()));
+
+    dt           = input->data_type();
+    is_quantized = is_data_type_quantized_asymmetric(dt);
+
+    if(biases != nullptr)
+    {
+        if(is_quantized)
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
+        }
+        else
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
+        }
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases);
+        ARM_COMPUTE_RETURN_ERROR_ON(!weights_info.are_reshaped() && biases->dimension(0) != weights->dimension(3));
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
+    }
+
+    append_bias          = (biases != nullptr) && (!is_quantized);
+    are_weights_reshaped = weights_info.are_reshaped();
+    kernel_width         = (are_weights_reshaped) ? weights_info.kernel_size().first : weights->dimension(0);
+    kernel_height        = (are_weights_reshaped) ? weights_info.kernel_size().second : weights->dimension(1);
+    mat_weights_cols     = weights->dimension(3);
+    mat_weights_rows     = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (append_bias ? 1 : 0);
+
+    std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height,
+                                                 conv_info);
+
+    // Check if its a "fully connected" convolution
+    is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
+    is_interleaved_transposed      = (!is_fully_connected_convolution && !is_quantized);
+
+    return Status{};
+}
+} // namespace
+
+NEGEMMConvolutionLayer::NEGEMMConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager)
+    : _memory_group(memory_manager), _input_im2col_kernel(), _input_interleave_kernel(), _reshape_weights(), _mm_kernel(), _mm_optimised_kernel(nullptr), _mm_gemmlowp(memory_manager),
+      _gemmlowp_output_stage(), _output_col2im_kernel(), _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _gemm_output(), _tmp_output(), _workspace(), _append_bias(false),
+      _is_fully_connected_convolution(false), _are_weights_reshaped(false), _is_quantized(false), _is_interleaved_transposed(false)
+{
+}
+
+void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *weights, ITensor *output)
+{
+    if(_is_quantized)
+    {
+        // Since we need negative offsets for computing convolution, we need to change QuantizationInfo()
+        // Extract and negate input and weights offset
+        const QuantizationInfo input_quantization_info   = input->info()->quantization_info();
+        const QuantizationInfo weights_quantization_info = weights->info()->quantization_info();
+
+        input->info()->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset));
+        weights->info()->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset));
+
+        _mm_gemmlowp.configure(input, weights, output, GEMMInfo(false, false, true /* Reshape weights only for the first run*/));
+
+        // Revert back QuantizatioInfo as input and weights could be used in other convolution layers
+        input->info()->set_quantization_info(input_quantization_info);
+        weights->info()->set_quantization_info(weights_quantization_info);
+    }
+    else
+    {
+        _mm_kernel.configure(input, weights, output, 1.f);
+    }
+}
+
+void NEGEMMConvolutionLayer::configure_asm_mm(const struct CPUInfo &ci, int M, int N, int K)
+{
+    ARM_COMPUTE_UNUSED(ci);
+    ARM_COMPUTE_UNUSED(M);
+    ARM_COMPUTE_UNUSED(N);
+    ARM_COMPUTE_UNUSED(K);
+#if defined(__arm__) || defined(__aarch64__)
+#if defined(__arm__)
+    GemmInterleaved<sgemm_8x6, float, float> gemm(&ci, M, N, K, false, false);
+#elif defined(__aarch64__)
+    GemmInterleaved<sgemm_12x8, float, float> gemm(&ci, M, N, K, false, false);
+#endif /* defined(__arm__) || defined(__aarch64__) */
+
+    constexpr size_t alignment = 4096;
+    _workspace.allocator()->init(TensorInfo(TensorShape{ (gemm.get_working_size() + alignment - 1) * NEScheduler::get().num_threads() }, 1, DataType::U8));
+    _memory_group.manage(&_workspace);
+#endif /* defined(__arm__) || defined(__aarch64__) */
+}
+
+void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
+{
+    // Perform validate step
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
+
+    DataType     dt{};
+    unsigned int kernel_width     = 0;
+    unsigned int kernel_height    = 0;
+    unsigned int mat_weights_cols = 0;
+    unsigned int mat_weights_rows = 0;
+    unsigned int conv_w           = 0;
+    unsigned int conv_h           = 0;
+
+    Status status = validate_and_initialize_values(input->info(), weights->info(), (biases == nullptr) ? nullptr : biases->info(), conv_info, weights_info, dt, _append_bias, _are_weights_reshaped,
+                                                   kernel_width, kernel_height,
+                                                   _is_fully_connected_convolution, _is_interleaved_transposed, _is_quantized,
+                                                   mat_weights_cols, mat_weights_rows, conv_w, conv_h);
+
+    ARM_COMPUTE_ERROR_THROW_ON(status);
+
+    const unsigned int fixed_point_position = input->info()->fixed_point_position();
+    const ITensor     *biases_to_use        = (_append_bias) ? biases : nullptr;
+
+#if defined(__arm__)
+    if(NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && dt == DataType::F32)
+    {
+        _mm_optimised_kernel = support::cpp14::make_unique<NEGEMMAArch32Kernel>();
+    }
+#elif defined(__aarch64__)
+    if(NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && dt == DataType::F32)
+    {
+        _mm_optimised_kernel = support::cpp14::make_unique<NEGEMMAArch64Kernel>();
+    }
+#endif /* defined(__arm__) || defined(__aarch64__) */
+
+    // Reshape weights if needed
+    if(_mm_optimised_kernel != nullptr)
+    {
+        if(_are_weights_reshaped)
+        {
+            mat_weights_cols = weights_info.num_kernels();
+            mat_weights_rows = weights->info()->dimension(1);
+        }
+        else
+        {
+            TensorShape reshaped_weights_shape{ mat_weights_cols, mat_weights_rows };
+
+            // Create tensor to store the reshaped weights
+            _weights_reshaped.allocator()->init(TensorInfo(reshaped_weights_shape, 1, dt, fixed_point_position));
+            _reshape_weights.configure(weights, biases, &_weights_reshaped, false /* 1xW transpose */);
+            weights = &_weights_reshaped;
+        }
+    }
+    else
+    {
+        if(_are_weights_reshaped)
+        {
+            if(_is_fully_connected_convolution || _is_quantized)
+            {
+                mat_weights_cols = weights_info.num_kernels();
+                mat_weights_rows = weights->info()->dimension(1);
+            }
+            else
+            {
+                const unsigned int transpose_width = 16 / input->info()->element_size();
+                mat_weights_cols                   = weights_info.num_kernels();
+                mat_weights_rows                   = weights->info()->dimension(0) / transpose_width + (_append_bias ? 1 : 0);
+            }
+        }
+        else
+        {
+            TensorShape reshaped_weights_shape;
+
+            if(_is_fully_connected_convolution || _is_quantized)
+            {
+                reshaped_weights_shape = TensorShape{ mat_weights_cols, mat_weights_rows };
+            }
+            else
+            {
+                // Create tensor to store transposed weights
+                const float transpose_width = 16.0f / input->info()->element_size();
+                reshaped_weights_shape      = TensorShape{ mat_weights_rows *static_cast<unsigned int>(transpose_width),
+                                                           static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)) };
+            }
+
+            // Create tensor to store the reshaped weights
+            _weights_reshaped.allocator()->init(TensorInfo(reshaped_weights_shape, 1, dt, fixed_point_position));
+            _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, _is_interleaved_transposed /* 1xW transpose */);
+            weights = &_weights_reshaped;
+        }
+    }
+
+    // Create tensor to store im2col reshaped inputs
+    const unsigned int mat_input_cols = mat_weights_rows;
+    const unsigned int mat_input_rows = conv_w * conv_h;
+
+    TensorShape shape_im2col(input->info()->tensor_shape());
+    shape_im2col.set(0, mat_input_cols);
+    shape_im2col.set(1, mat_input_rows);
+    shape_im2col.set(2, 1);
+    _input_im2col_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
+    _memory_group.manage(&_input_im2col_reshaped);
+
+    // Create tensor (interleave) to prepare input tensor for GEMM
+    if(!_is_fully_connected_convolution && _mm_optimised_kernel == nullptr)
+    {
+        TensorShape shape_interleaved(shape_im2col);
+        shape_interleaved.set(0, shape_interleaved.x() * 4);
+        shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
+        _input_interleaved_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_interleaved));
+        _memory_group.manage(&_input_interleaved_reshaped);
+    }
+
+    // Create GEMM output tensor
+    TensorShape shape_gemm(_input_im2col_reshaped.info()->tensor_shape());
+    shape_gemm.set(0, mat_weights_cols);
+    shape_gemm.set(1, mat_input_rows);
+    const DataType gemm_data_type = _is_quantized ? DataType::S32 : dt;
+    // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input.
+    TensorInfo info_gemm(shape_gemm, 1, gemm_data_type, input->info()->fixed_point_position());
+    info_gemm.set_quantization_info(output->info()->quantization_info());
+    _gemm_output.allocator()->init(info_gemm);
+    _memory_group.manage(&_gemm_output);
+
+    // Configure kernels
+    // Configure im2col
+    _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias);
+
+    // Configure matrix multiply
+    if(_mm_optimised_kernel != nullptr)
+    {
+        struct CPUInfo ci = NEScheduler::get().cpu_info();
+
+        const int M = _gemm_output.info()->tensor_shape().y();
+        const int N = _gemm_output.info()->tensor_shape().x();
+        const int K = _input_im2col_reshaped.info()->tensor_shape().x();
+
+#if defined(__aarch64__)
+        if((N <= 128) && (K <= 128))
+        {
+            _mm_optimised_kernel = support::cpp14::make_unique<NEGEMMAArch64NativeKernel>();
+        }
+        else
+#endif /* defined(__aarch64__) */
+        {
+            configure_asm_mm(ci, M, N, K);
+        }
+
+        // Configure matrix multiplication kernel
+        _mm_optimised_kernel->configure(&_input_im2col_reshaped, weights, &_gemm_output, &_workspace);
+
+        _workspace.allocator()->allocate();
+    }
+    else
+    {
+        if(_is_interleaved_transposed)
+        {
+            // Configure GEMMInterleave4x4. _input_interleaved_reshaped will be auto configured in the kernel
+            _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
+
+            // Configure GEMM
+            configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output);
+            _input_interleaved_reshaped.allocator()->allocate();
+        }
+        else
+        {
+            configure_mm(&_input_im2col_reshaped, weights, &_gemm_output);
+        }
+    }
+
+    _input_im2col_reshaped.allocator()->allocate();
+
+    // Configure output stage for quantized case
+    if(_is_quantized)
+    {
+        const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input->info()->quantization_info() : output->info()->quantization_info();
+
+        float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output_quant_info.scale;
+        int   output_multiplier, output_shift;
+        quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
+        _memory_group.manage(&_tmp_output);
+        _gemmlowp_output_stage.configure(&_gemm_output, biases, &_tmp_output, output_multiplier, output_shift, output_quant_info.offset);
+    }
+
+    // Configure Col2Im
+    _output_col2im_kernel.configure(_is_quantized ? &_tmp_output : &_gemm_output, output, Size2D(conv_w, conv_h));
+    if(_is_quantized)
+    {
+        _tmp_output.allocator()->allocate();
+    }
+    _gemm_output.allocator()->allocate();
+
+    ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) || (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one");
+
+    // Allocate intermediate tensor
+    if(!_are_weights_reshaped)
+    {
+        _weights_reshaped.allocator()->allocate();
+    }
+}
+
+Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
+                                        const WeightsInfo &weights_info)
+{
+    DataType     dt{};
+    bool         append_bias{};
+    bool         are_weights_reshaped{};
+    bool         is_fully_connected_convolution{};
+    bool         is_interleaved_transposed{};
+    bool         is_quantized{};
+    unsigned int kernel_width     = 0;
+    unsigned int kernel_height    = 0;
+    unsigned int mat_weights_cols = 0;
+    unsigned int mat_weights_rows = 0;
+    unsigned int conv_w           = 0;
+    unsigned int conv_h           = 0;
+
+    Status status = validate_and_initialize_values(input, weights, biases, conv_info, weights_info, dt, append_bias, are_weights_reshaped, kernel_width, kernel_height,
+                                                   is_fully_connected_convolution, is_interleaved_transposed, is_quantized, mat_weights_cols, mat_weights_rows,
+                                                   conv_w, conv_h);
+
+    ARM_COMPUTE_RETURN_ON_ERROR(status);
+
+    std::unique_ptr<ITensorInfo> reshaped_weights = weights->clone();
+    bool                         optimised_kernel = false;
+
+#if defined(__arm__)
+    if(NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && dt == DataType::F32)
+    {
+        optimised_kernel = true;
+    }
+#elif defined(__aarch64__)
+    if(NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && dt == DataType::F32)
+    {
+        optimised_kernel = true;
+    }
+#endif /* defined(__arm__) || defined(__aarch64__) */
+
+    // Reshape weights if needed
+    if(optimised_kernel)
+    {
+        if(are_weights_reshaped)
+        {
+            mat_weights_cols = weights_info.num_kernels();
+            mat_weights_rows = weights->dimension(1);
+        }
+        else
+        {
+            TensorShape reshaped_weights_shape{ mat_weights_cols, mat_weights_rows };
+
+            // Create tensor to store the reshaped weights
+            reshaped_weights->set_tensor_shape(get_reshaped_weights_shape_conv(weights, append_bias, is_fully_connected_convolution));
+            ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayerReshapeWeights::validate(weights, biases, reshaped_weights.get(), !is_fully_connected_convolution /* 1xW transpose */));
+            weights = reshaped_weights.get();
+        }
+    }
+    else
+    {
+        if(are_weights_reshaped)
+        {
+            const unsigned int transpose_width = 16 / input->element_size();
+            mat_weights_cols                   = weights_info.num_kernels();
+            mat_weights_rows                   = weights->dimension(0) / transpose_width + (append_bias ? 1 : 0);
+        }
+        else
+        {
+            TensorShape reshaped_weights_shape;
+
+            if(is_fully_connected_convolution || is_quantized)
+            {
+                reshaped_weights_shape = TensorShape{ mat_weights_cols, mat_weights_rows };
+            }
+            else
+            {
+                // Create tensor to store transposed weights
+                const float transpose_width = 16.0f / input->element_size();
+                reshaped_weights_shape      = TensorShape{ mat_weights_rows *static_cast<unsigned int>(transpose_width),
+                                                           static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)) };
+            }
+
+            // Create tensor to store the reshaped weights
+            reshaped_weights->set_tensor_shape(get_reshaped_weights_shape_conv(weights, append_bias, is_fully_connected_convolution));
+            ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayerReshapeWeights::validate(weights, biases, reshaped_weights.get(), !is_fully_connected_convolution /* 1xW transpose */));
+            weights = reshaped_weights.get();
+        }
+    }
+
+    // Validate im2col
+    const unsigned int mat_input_cols = mat_weights_rows;
+    const unsigned int mat_input_rows = conv_w * conv_h;
+    TensorShape        shape_im2col   = input->tensor_shape();
+    shape_im2col.set(0, mat_input_cols);
+    shape_im2col.set(1, mat_input_rows);
+    shape_im2col.set(2, 1);
+    TensorInfo im2_col_info = input->clone()->set_tensor_shape(shape_im2col);
+    ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, &im2_col_info, Size2D(weights->dimension(0), weights->dimension(1)), conv_info, append_bias));
+
+    // Create GEMM output tensor
+    TensorShape shape_gemm(im2_col_info.tensor_shape());
+    shape_gemm.set(0, mat_weights_cols);
+    shape_gemm.set(1, mat_input_rows);
+    TensorInfo gemm_output_info = input->clone()->set_tensor_shape(shape_gemm);
+
+    // Validate GEMM interleave and multiply
+    if(is_interleaved_transposed)
+    {
+        TensorShape shape_interleaved = shape_im2col;
+        shape_interleaved.set(0, shape_interleaved.x() * 4);
+        shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
+        TensorInfo input_interleaved_info = input->clone()->set_tensor_shape(shape_interleaved);
+        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMInterleave4x4Kernel::validate(&im2_col_info, &input_interleaved_info));
+        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&input_interleaved_info, weights, &gemm_output_info));
+    }
+    else
+    {
+        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&im2_col_info, weights, &gemm_output_info));
+    }
+
+    ARM_COMPUTE_RETURN_ON_ERROR(NECol2ImKernel::validate(&gemm_output_info, output, Size2D(conv_w, conv_h)));
+
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG((output->dimension(0) != conv_w) || (output->dimension(1) != conv_h), "Output shape does not match the expected one");
+
+    return Status{};
+}
+
+void NEGEMMConvolutionLayer::run()
+{
+    // Run weights reshaping (Runs once for every configure)
+    if(!_are_weights_reshaped)
+    {
+        _are_weights_reshaped = true;
+        _reshape_weights.run();
+    }
+
+    _memory_group.acquire();
+
+    // Run input reshaping
+    NEScheduler::get().schedule(&_input_im2col_kernel, Window::DimY);
+
+    // Runs matrix multiply on reshaped matrices
+    if(_mm_optimised_kernel != nullptr)
+    {
+        NEScheduler::get().schedule(_mm_optimised_kernel.get(), Window::DimY);
+    }
+    else
+    {
+        if(_is_interleaved_transposed)
+        {
+            // Run interleave
+            NEScheduler::get().schedule(&_input_interleave_kernel, Window::DimY);
+        }
+
+        // Runs matrix multiply on reshaped matrices
+        if(_is_quantized)
+        {
+            _mm_gemmlowp.run();
+        }
+        else
+        {
+            NEScheduler::get().schedule(&_mm_kernel, Window::DimY);
+        }
+    }
+
+    // Run output stage for quantized case
+    if(_is_quantized)
+    {
+        _gemmlowp_output_stage.run();
+    }
+
+    // Reshape output matrix
+    NEScheduler::get().schedule(&_output_col2im_kernel, Window::DimY);
+
+    _memory_group.release();
+}
+} // namespace arm_compute