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diff --git a/src/runtime/NEON/functions/NELocallyConnectedLayer.cpp b/src/runtime/NEON/functions/NELocallyConnectedLayer.cpp
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--- a/src/runtime/NEON/functions/NELocallyConnectedLayer.cpp
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-/*
- * Copyright (c) 2017-2020 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/NEON/functions/NELocallyConnectedLayer.h"
-
-#include "arm_compute/core/PixelValue.h"
-#include "arm_compute/core/Utils.h"
-#include "arm_compute/core/Validate.h"
-#include "arm_compute/runtime/NEON/NEScheduler.h"
-#include "src/core/NEON/kernels/NEIm2ColKernel.h"
-#include "src/core/NEON/kernels/NELocallyConnectedMatrixMultiplyKernel.h"
-#include "src/core/NEON/kernels/NEWeightsReshapeKernel.h"
-#include "support/MemorySupport.h"
-
-#include <cmath>
-#include <tuple>
-
-namespace arm_compute
-{
-namespace
-{
-void calculate_shapes(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
- TensorShape &shape_wr, TensorShape &shape_im2col, TensorShape &shape_gemm)
-{
- ARM_COMPUTE_UNUSED(output);
-
- const unsigned int kernel_width = weights->dimension(0);
- const unsigned int kernel_height = weights->dimension(1);
-
- bool has_bias = (biases != nullptr);
-
- // Get convolved dimensions
- unsigned int conv_w = 0;
- unsigned int conv_h = 0;
- std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height,
- conv_info);
-
- const size_t mat_weights_cols = weights->dimension(3);
- const size_t mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + ((has_bias) ? 1 : 0);
- const size_t mat_weights_num = weights->dimension(4);
-
- shape_wr = TensorShape(mat_weights_cols, mat_weights_rows, mat_weights_num);
-
- const size_t mat_input_cols = mat_weights_rows;
- const size_t mat_input_rows = conv_w * conv_h;
-
- shape_im2col = input->tensor_shape();
- shape_im2col.set(0, mat_input_cols);
- shape_im2col.set(1, mat_input_rows);
- shape_im2col.set(2, 1);
-
- shape_gemm = shape_im2col;
- shape_gemm.set(0, mat_weights_cols);
- shape_gemm.set(1, mat_input_rows);
-}
-} // namespace
-NELocallyConnectedLayer::~NELocallyConnectedLayer() = default;
-
-NELocallyConnectedLayer::NELocallyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(std::move(memory_manager)), _input_im2col(), _weights_reshape_kernel(), _mm_kernel(), _output_col2im(), _input_im2col_reshaped(), _weights_reshaped(), _gemm_output(),
- _is_prepared(false), _original_weights(nullptr)
-{
-}
-
-Status NELocallyConnectedLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
- ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(2) != input->dimension(2));
- ARM_COMPUTE_RETURN_ERROR_ON(!conv_info.padding_is_symmetric());
-
- bool has_bias = (biases != nullptr);
-
- if(has_bias)
- {
- ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
- ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 2);
- }
-
- const unsigned int kernel_width = weights->dimension(0);
- const unsigned int kernel_height = weights->dimension(1);
-
- // Get convolved dimensions
- unsigned int conv_w = 0;
- unsigned int conv_h = 0;
- std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height,
- conv_info);
-
- ARM_COMPUTE_RETURN_ERROR_ON_MSG((output->dimension(0) != conv_w) || (output->dimension(1) != conv_h), "Output shape does not match the expected one");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(4) != (conv_w * conv_h), "Weights shape does not match the expected one");
-
- // Calculate intermediate buffer shapes
- TensorShape shape_wr;
- TensorShape shape_im2col;
- TensorShape shape_gemm;
- calculate_shapes(input, weights, biases, output, conv_info, shape_wr, shape_im2col, shape_gemm);
-
- TensorInfo weights_reshaped_info(shape_wr, 1, weights->data_type());
- TensorInfo input_im2col_reshaped_info(shape_im2col, 1, input->data_type());
- TensorInfo gemm_output_info(shape_gemm, 1, input->data_type());
-
- ARM_COMPUTE_RETURN_ON_ERROR(NEIm2Col::validate(input, &input_im2col_reshaped_info, Size2D(kernel_width, kernel_height), conv_info, has_bias));
- ARM_COMPUTE_RETURN_ON_ERROR(NEWeightsReshapeKernel::validate(weights, biases, &weights_reshaped_info));
- ARM_COMPUTE_RETURN_ON_ERROR(NELocallyConnectedMatrixMultiplyKernel::validate(&input_im2col_reshaped_info, &weights_reshaped_info, &gemm_output_info));
- ARM_COMPUTE_RETURN_ON_ERROR(NECol2Im::validate(&gemm_output_info, output, Size2D(conv_w, conv_h)));
-
- return Status{};
-}
-
-void NELocallyConnectedLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info)
-{
- ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
- ARM_COMPUTE_ERROR_THROW_ON(NELocallyConnectedLayer::validate(input->info(), weights->info(), biases == nullptr ? nullptr : biases->info(), output->info(), conv_info));
-
- bool _has_bias = (biases != nullptr);
- _is_prepared = false;
- _original_weights = weights;
-
- const unsigned int kernel_width = weights->info()->dimension(0);
- const unsigned int kernel_height = weights->info()->dimension(1);
-
- // Get convolved dimensions
- unsigned int conv_w = 0;
- unsigned int conv_h = 0;
- std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_width, kernel_height,
- conv_info);
-
- // Calculate intermediate buffer shapes
- TensorShape shape_wr;
- TensorShape shape_im2col;
- TensorShape shape_gemm;
- calculate_shapes(input->info(), weights->info(), biases == nullptr ? nullptr : biases->info(), output->info(), conv_info, shape_wr, shape_im2col, shape_gemm);
-
- _weights_reshaped.allocator()->init(TensorInfo(shape_wr, 1, weights->info()->data_type()));
- _input_im2col_reshaped.allocator()->init(TensorInfo(shape_im2col, 1, input->info()->data_type()));
- _gemm_output.allocator()->init(TensorInfo(shape_gemm, 1, input->info()->data_type()));
-
- // Manage intermediate buffers
- _memory_group.manage(&_input_im2col_reshaped);
- _memory_group.manage(&_gemm_output);
-
- // Configure kernels
- _input_im2col.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _has_bias);
- _weights_reshape_kernel = arm_compute::support::cpp14::make_unique<NEWeightsReshapeKernel>();
- _weights_reshape_kernel->configure(weights, biases, &_weights_reshaped);
- _mm_kernel = arm_compute::support::cpp14::make_unique<NELocallyConnectedMatrixMultiplyKernel>();
- _mm_kernel->configure(&_input_im2col_reshaped, &_weights_reshaped, &_gemm_output);
- _output_col2im.configure(&_gemm_output, output, Size2D(conv_w, conv_h));
-
- // Allocate intermediate tensors
- _input_im2col_reshaped.allocator()->allocate();
- _gemm_output.allocator()->allocate();
-}
-
-void NELocallyConnectedLayer::run()
-{
- prepare();
-
- MemoryGroupResourceScope scope_mg(_memory_group);
-
- // Run input reshaping
- _input_im2col.run();
-
- // Runs GEMM on reshaped matrices
- NEScheduler::get().schedule(_mm_kernel.get(), Window::DimX);
-
- // Reshape output matrix
- _output_col2im.run();
-}
-
-void NELocallyConnectedLayer::prepare()
-{
- if(!_is_prepared)
- {
- ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
-
- // Run weights reshaping and mark original weights tensor as unused
- _weights_reshaped.allocator()->allocate();
- NEScheduler::get().schedule(_weights_reshape_kernel.get(), 3);
- _original_weights->mark_as_unused();
-
- _is_prepared = true;
- }
-}
-} // namespace arm_compute
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