COMPMID-872 - Rework NEGEMMConvolutionLayer to use NEGEMM

Change-Id: I55f0018ac7214775ebbca63f58a3bf5c93732fec
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/142632
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

1 files changed, 293 insertions, 443 deletions

diff --git a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
index aace261e32..c0a5d0a436 100644
--- a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
@@ -23,10 +23,10 @@
  */
 #include "arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.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/misc/ShapeCalculator.h"
 #include "arm_compute/core/utils/quantization/AsymmHelpers.h"
 #include "arm_compute/runtime/NEON/NEScheduler.h"
 #include "support/ToolchainSupport.h"
@@ -34,96 +34,50 @@
 #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
+using namespace arm_compute;
+using namespace arm_compute::misc::shape_calculator;
 
-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)
+NEConvolutionLayerReshapeWeights::NEConvolutionLayerReshapeWeights()
+    : _weights_reshape_kernel()
 {
 }
 
-void NEConvolutionLayerReshapeWeights::configure(const ITensor *weights, const ITensor *biases, ITensor *output, bool transpose1xW)
+void NEConvolutionLayerReshapeWeights::configure(const ITensor *weights, const ITensor *biases, ITensor *output)
 {
     // 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));
+                                                                          output->info()));
 
-    // 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 bool     append_biases = (biases != nullptr) && !is_data_type_quantized_asymmetric(weights->info()->data_type());
     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);
-    }
+    _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)
+Status NEConvolutionLayerReshapeWeights::validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output)
 {
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(weights);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QASYMM8, 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);
-    }
-    // Check if bias are present, if yes they will be embedded to the weights matrix
-    const bool append_bias = (biases != nullptr);
 
-    if(append_bias)
+    if(biases != nullptr)
     {
+        const int idx_kernels = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::BATCHES);
         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(biases->dimension(0) != weights->dimension(3));
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(idx_kernels));
         ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
     }
 
-    // Checks performed when biases are present
-    if(append_bias)
+    if((output != nullptr) && (output->total_size() != 0))
     {
-        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);
-    }
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
 
-    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));
+        NEWeightsReshapeKernel::validate(weights, biases, output);
     }
 
     return Status{};
@@ -131,108 +85,21 @@ Status NEConvolutionLayerReshapeWeights::validate(const ITensorInfo *weights, co
 
 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,
-                                      const ActivationLayerInfo &act_info, DataType &dt,
-                                      bool &append_bias, bool &skip_im2col,
-                                      bool &are_weights_reshaped, unsigned int &kernel_width, unsigned int &kernel_height,
-                                      bool &is_fully_connected_convolution, bool &is_interleaved, bool &is_quantized, bool &is_activationlayer_enabled,
-                                      unsigned int &mat_weights_cols, unsigned int &mat_weights_rows,
-                                      unsigned int &conv_w, unsigned int &conv_h, const Size2D &dilation)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, weights);
-
-    DataLayout data_layout = input->data_layout();
-    const int  idx_width   = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
-    const int  idx_height  = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
-    const int  idx_channel = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
-
-    ARM_COMPUTE_RETURN_ERROR_ON(!weights_info.are_reshaped() && weights->dimension(idx_channel) != input->dimension(idx_channel));
-    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()));
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(data_layout == DataLayout::NHWC && input->data_type() != DataType::F32, "NHWC is only supported for FP32 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(!weights_info.are_reshaped() && biases->dimension(0) != weights->dimension(3));
-        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
-    }
-
-    // If we have 1x1 convolution and data layout is NHWC we can disable im2col
-    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(idx_width);
-    kernel_height        = (are_weights_reshaped) ? weights_info.kernel_size().second : weights->dimension(idx_height);
-    mat_weights_cols     = weights->dimension(3);
-    mat_weights_rows     = weights->dimension(idx_width) * weights->dimension(idx_height) * weights->dimension(idx_channel) + ((append_bias && !skip_im2col) ? 1 : 0);
-    skip_im2col          = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1);
-
-    std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(idx_width), input->dimension(idx_height), kernel_width, kernel_height,
-                                                 conv_info, dilation);
-
-    // Check if its a "fully connected" convolution
-    is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
-    is_interleaved                 = (!is_fully_connected_convolution && !is_quantized);
-    is_activationlayer_enabled     = act_info.enabled();
-
-    return Status{};
 }
-} // namespace
 
 NEGEMMConvolutionLayer::NEGEMMConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager)
-    : _memory_group(memory_manager), _asm_glue(memory_manager), _input_im2col_kernel(), _input_interleave_kernel(), _reshape_weights(), _mm_kernel(), _mm_gemmlowp(memory_manager),
-      _gemmlowp_output_stage(), _output_col2im_kernel(), _activationlayer_function(), _add_bias_kernel(), _original_weights(nullptr), _input_im2col_reshaped(), _input_interleaved_reshaped(),
-      _weights_reshaped(), _gemm_output(), _tmp_output(), _data_layout(DataLayout::NCHW), _append_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false), _is_quantized(false),
-      _is_interleaved(false), _is_activationlayer_enabled(false), _skip_im2col(false), _is_prepared(false)
+    : _memory_group(memory_manager), _reshape_weights(), _im2col_kernel(), _mm_gemm(), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), _col2im_kernel(), _activationlayer_function(),
+      _add_bias_kernel(), _original_weights(nullptr), _im2col_output(), _weights_reshaped(), _gemm_output(), _tmp_output(), _data_layout(DataLayout::NCHW), _append_bias(false), _skip_im2col(false),
+      _skip_col2im(false), _is_quantized(false), _is_activationlayer_enabled(false), _is_prepared(false)
 {
 }
 
-void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *weights, ITensor *output, bool is_interleaved, const GEMMReshapeInfo &reshape_info)
+void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *weights, ITensor *output, int gemm_3d_depth)
 {
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights);
+    ARM_COMPUTE_ERROR_THROW_ON(validate_mm(input->info(), weights->info(), output->info(), gemm_3d_depth, _skip_im2col));
+
     if(_is_quantized)
     {
         // Since we need negative offsets for computing convolution, we need to change QuantizationInfo()
@@ -251,129 +118,125 @@ void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *w
     }
     else
     {
-        _mm_kernel.configure(input, weights, output, 1.f, is_interleaved, reshape_info);
+        // Configure matrix multiply function
+        _mm_gemm.configure(input, weights, nullptr, output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/, gemm_3d_depth,
+                                                                                 _skip_im2col /* Reinterpret the input as 3D if im2col is skipped */));
     }
 }
 
-void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info,
-                                       const Size2D &dilation, const ActivationLayerInfo &act_info)
+Status NEGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, int gemm_3d_depth, bool skip_im2col)
 {
-    // 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;
-
-    _data_layout           = input->info()->data_layout();
-    const bool is_nhwc     = _data_layout == DataLayout::NHWC;
-    const int  idx_width   = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
-    const int  idx_height  = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
-    const int  idx_channel = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL);
-
-    Status status = validate_and_initialize_values(input->info(), weights->info(), (biases == nullptr) ? nullptr : biases->info(), conv_info, weights_info, act_info, dt, _append_bias, _skip_im2col,
-                                                   _are_weights_reshaped,
-                                                   kernel_width, kernel_height,
-                                                   _is_fully_connected_convolution, _is_interleaved, _is_quantized, _is_activationlayer_enabled,
-                                                   mat_weights_cols, mat_weights_rows, conv_w, conv_h, dilation);
-
-    ARM_COMPUTE_ERROR_THROW_ON(status);
-
-    _is_prepared                 = false;
-    _original_weights            = weights;
-    const ITensor *biases_to_use = (_append_bias) ? biases : nullptr;
+    const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
 
-    bool run_optimised = dt == DataType::F32;
-
-    // Reshape weights if needed
-    if(run_optimised)
+    const GEMMInfo &gemm_info = GEMMInfo(false, false, true /* Reshape weights only for the first run */, gemm_3d_depth, skip_im2col);
+    if(is_quantized)
     {
-        TensorShape reshaped_weights_shape{ mat_weights_cols, mat_weights_rows };
+        // 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->quantization_info();
+        const QuantizationInfo weights_quantization_info = weights->quantization_info();
+
+        std::unique_ptr<ITensorInfo> input_qa   = input->clone();
+        std::unique_ptr<ITensorInfo> weights_qa = weights->clone();
+        input_qa->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset));
+        weights_qa->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset));
 
-        // Create tensor to store the reshaped weights
-        _weights_reshaped.allocator()->init(TensorInfo(reshaped_weights_shape, 1, dt));
-        _reshape_weights.configure(weights, biases, &_weights_reshaped, false /* 1xW transpose */);
-        weights = &_weights_reshaped;
+        // Perform validation step on GEMMLowp
+        NEGEMMLowpMatrixMultiplyCore::validate(input_qa.get(), weights_qa.get(), output, gemm_info);
     }
     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(idx_height);
-            }
-            else
-            {
-                mat_weights_cols = weights_info.num_kernels();
-                mat_weights_rows = weights_info.kernel_size().first * weights_info.kernel_size().second * input->info()->dimension(idx_channel) + (_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));
-            _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, _is_interleaved /* 1xW transpose */);
-            weights = &_weights_reshaped;
-        }
+        // Perform validation step on Matrix multiply function
+        NEGEMM::validate(input, weights, nullptr, output, 1.0f, 0.0f, gemm_info);
     }
+    return Status{};
+}
+
+void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info,
+                                       const Size2D &dilation, const ActivationLayerInfo &act_info)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
 
-    // In case we skip im2col we have to add bias
+    ARM_COMPUTE_ERROR_THROW_ON(NEGEMMConvolutionLayer::validate(input->info(),
+                                                                weights->info(),
+                                                                biases != nullptr ? biases->info() : nullptr,
+                                                                output->info(),
+                                                                conv_info,
+                                                                weights_info,
+                                                                dilation,
+                                                                act_info));
+
+    const DataType   data_type   = input->info()->data_type();
+    const DataLayout data_layout = input->info()->data_layout();
+    const int        idx_width   = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+    const int        idx_height  = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+    const int        idx_channel = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
+    const int        idx_kernels = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES);
+
+    const unsigned int kernel_width  = weights->info()->dimension(idx_width);
+    const unsigned int kernel_height = weights->info()->dimension(idx_height);
+
+    _is_prepared      = weights_info.retain_internal_weights();
+    _original_weights = weights;
+    _is_quantized     = is_data_type_quantized_asymmetric(input->info()->data_type());
+    _data_layout      = data_layout;
+    _skip_im2col      = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1) && !_is_quantized;
+    _skip_col2im      = (data_layout == DataLayout::NHWC) && !_is_quantized;
+    _append_bias      = (biases != nullptr) && (!_is_quantized);
+
+    // TODO (giaiod01): Validate GEMM3D
+
+    const bool     is_nhwc                   = _data_layout == DataLayout::NHWC;
+    const ITensor *gemm_input_to_use         = input;
+    ITensor       *gemm_output_to_use        = output;
+    ITensor       *gemm_output_staged_to_use = output;
+
+    const unsigned bias_element  = (_append_bias && !_skip_im2col) ? 1 : 0;
+    const ITensor *biases_to_use = (_append_bias && !_skip_im2col) ? biases : nullptr;
+
+    // Get parameters from conv_info
+    unsigned int stride_x = 0;
+    unsigned int stride_y = 0;
+    std::tie(stride_x, stride_y) = conv_info.stride();
+
+    // Get convolved dimensions
+    unsigned int conv_w = 0;
+    unsigned int conv_h = 0;
+    std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(idx_width),
+                                                 input->info()->dimension(idx_height),
+                                                 kernel_width,
+                                                 kernel_height,
+                                                 conv_info,
+                                                 dilation);
+
+    unsigned int mat_weights_cols = weights->info()->dimension(idx_kernels);
+    unsigned int mat_weights_rows = weights->info()->dimension(idx_width) * weights->info()->dimension(idx_height) * weights->info()->dimension(idx_channel) + bias_element;
+
+    // _weights_reshaped will be auto configured in the kernel.
+    // Just append biases and do not transpose 1xW as it will be reshaped in NEGEMM
+    _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped);
+
+    weights = &_weights_reshaped;
+
+    // Create tensor to store im2col reshaped inputs
     if(!_skip_im2col)
     {
-        const unsigned int mat_input_cols = mat_weights_rows;
-        const unsigned int mat_input_rows = conv_w * conv_h;
-
-        // Create tensor to store im2col reshaped inputs
-        TensorShape shape_im2col(input->info()->tensor_shape());
-        shape_im2col.set(0, mat_input_cols);
-        shape_im2col.set(1, mat_input_rows);
+        // Calculate im2col shape
+        // For NEON the batch size is on the fourth dimension
+        // TODO (giaiod01): Use auto-init COMPMID-1277
+        TensorShape shape_im2col = input->info()->tensor_shape();
+        shape_im2col.set(0, mat_weights_rows);
+        shape_im2col.set(1, conv_w * conv_h);
         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 && !run_optimised && _is_interleaved)
-        {
-            TensorShape shape_interleaved(shape_im2col);
-            shape_interleaved.set(idx_width, shape_interleaved.x() * 4);
-            shape_interleaved.set(idx_height, std::ceil(shape_interleaved[idx_height] / 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);
-        }
+        _im2col_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
+        _memory_group.manage(&_im2col_output);
 
-        // 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);
-        info_gemm.set_quantization_info(output->info()->quantization_info());
-        _gemm_output.allocator()->init(info_gemm);
-        _memory_group.manage(&_gemm_output);
+        // Configure and tune im2col
+        _im2col_kernel.configure(input, &_im2col_output, Size2D(kernel_width, kernel_height), conv_info, _append_bias, false, false, dilation);
 
-        // Configure im2col
-        _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias, false, false, dilation);
+        // Update GEMM input
+        gemm_input_to_use = &_im2col_output;
     }
     else if(_append_bias)
     {
@@ -381,124 +244,165 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig
         _add_bias_kernel.configure(output, biases, output, ConvertPolicy::SATURATE);
     }
 
-    // Configure matrix multiply
-    if(run_optimised)
-    {
-        _asm_glue.configure(_skip_im2col ? input : &_input_im2col_reshaped, weights, is_nhwc ? output : &_gemm_output, 1.f, 0.f, true);
-        if(!_asm_glue.is_configured())
-        {
-            ARM_COMPUTE_ERROR("setup_assembly_kernel failed.");
-        }
-    }
-    else
+    // Create GEMM output tensor
+    if(!is_nhwc || _is_quantized)
     {
-        if(_is_interleaved)
-        {
-            // Configure GEMMInterleave4x4. _input_interleaved_reshaped will be auto configured in the kernel
-            _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
+        // Calculate GEMM output shape
+        TensorShape shape_gemm = _im2col_output.info()->tensor_shape();
+        shape_gemm.set(0, mat_weights_cols);
+        shape_gemm.set(1, conv_w * conv_h);
 
-            // Configure GEMM
-            configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output, _is_interleaved, GEMMReshapeInfo(_input_im2col_reshaped.info()->dimension(idx_height), 0 /* no transpose */,
-                                                                                                                _input_im2col_reshaped.info()->dimension(idx_width)));
-            _input_interleaved_reshaped.allocator()->allocate();
-        }
-        else
-        {
-            configure_mm(&_input_im2col_reshaped, weights, &_gemm_output, _is_interleaved);
-        }
+        // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input.
+        const DataType gemm_data_type = _is_quantized ? DataType::S32 : data_type;
+        // FIXME: input->clone() doesn't work with subtensors for grouped convolutions.
+        TensorInfo info_gemm(shape_gemm, 1, gemm_data_type);
+        info_gemm.set_quantization_info(output->info()->quantization_info());
+        _gemm_output.allocator()->init(info_gemm);
+        _memory_group.manage(&_gemm_output);
+
+        // Update GEMM output
+        gemm_output_to_use = &_gemm_output;
     }
 
+    // Configure and tune GEMM
+    configure_mm(gemm_input_to_use, weights, gemm_output_to_use, (data_layout == DataLayout::NHWC) ? conv_h : 1);
+
     if(!_skip_im2col)
     {
-        _input_im2col_reshaped.allocator()->allocate();
+        _im2col_output.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();
+    // 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);
-        }
+        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);
 
-        // Configure Col2Im
-        if(!is_nhwc)
-        {
-            _output_col2im_kernel.configure(_is_quantized ? &_tmp_output : &_gemm_output, output, Size2D(conv_w, conv_h));
-        }
+        _memory_group.manage(&_tmp_output);
+        gemm_output_staged_to_use = &_tmp_output;
 
-        if(_is_quantized)
-        {
-            _tmp_output.allocator()->allocate();
-        }
+        _gemmlowp_output_stage.configure(gemm_output_to_use, biases, gemm_output_staged_to_use, output_multiplier, output_shift, output_quant_info.offset);
+    }
+
+    if(!_skip_col2im)
+    {
+        // Configure and tune Col2Im
+        _col2im_kernel.configure(_is_quantized ? gemm_output_staged_to_use : gemm_output_to_use, output, Size2D(conv_w, conv_h));
+    }
+
+    if(!is_nhwc || _is_quantized)
+    {
+        _tmp_output.allocator()->allocate();
         _gemm_output.allocator()->allocate();
     }
 
-    ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(idx_width) != conv_w) || (output->info()->dimension(idx_height) != conv_h), "Output shape does not match the expected one");
+    ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(idx_width) != conv_w) || (output->info()->dimension(idx_height) != conv_h),
+                             "Output shape does not match the expected one");
 
     //Configure Activation Layer
+    _is_activationlayer_enabled = act_info.enabled();
+
     if(_is_activationlayer_enabled)
     {
         _activationlayer_function.configure(output, nullptr, act_info);
     }
+
+    ARM_COMPUTE_UNUSED(weights_info);
 }
 
 Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
                                         const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info)
 {
-    ARM_COMPUTE_UNUSED(output);
-
-    DataType     dt{};
-    bool         append_bias{};
-    bool         skip_im2col{};
-    bool         are_weights_reshaped{};
-    bool         is_fully_connected_convolution{};
-    bool         is_interleaved{};
-    bool         is_quantized{};
-    bool         is_activationlayer_enabled{};
-    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;
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights_info.are_reshaped(), "Weights already reshaped are not supported!");
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, weights);
 
     const DataLayout data_layout = input->data_layout();
-    const bool       is_nhwc     = data_layout == DataLayout::NHWC;
+    const DataType   data_type   = input->data_type();
     const int        idx_width   = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
     const int        idx_height  = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+    const int        idx_channel = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
+    const int        idx_kernels = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES);
 
-    Status status = validate_and_initialize_values(input, weights, biases, conv_info, weights_info, act_info, dt, append_bias, skip_im2col, are_weights_reshaped, kernel_width, kernel_height,
-                                                   is_fully_connected_convolution, is_interleaved, is_quantized, is_activationlayer_enabled, mat_weights_cols, mat_weights_rows,
-                                                   conv_w, conv_h, dilation);
+    const unsigned int kernel_width  = weights->dimension(idx_width);
+    const unsigned int kernel_height = weights->dimension(idx_height);
 
-    const Size2D kernel_weights = Size2D(kernel_width, kernel_height);
+    TensorInfo         im2col_reshaped_info, info_gemm, tmp_info, weights_reshaped_info;
+    const ITensorInfo *gemm_input_to_use         = input;
+    const ITensorInfo *gemm_output_to_use        = output;
+    const ITensorInfo *gemm_output_staged_to_use = output;
+    const ITensorInfo *weights_to_use            = weights;
 
-    ARM_COMPUTE_RETURN_ON_ERROR(status);
+    const bool     is_nhwc      = data_layout == DataLayout::NHWC;
+    const bool     is_quantized = is_data_type_quantized_asymmetric(data_type);
+    bool           skip_im2col  = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1) && !is_quantized;
+    const bool     append_bias  = (biases != nullptr) && (!is_quantized);
+    const unsigned bias_element = (append_bias && !skip_im2col) ? 1 : 0;
 
-    std::unique_ptr<ITensorInfo> reshaped_weights = weights->clone();
-    bool                         optimised_kernel = false;
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_channel) != input->dimension(idx_channel));
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
 
-    if(dt == DataType::F32)
+    // Validate biases
+    if(biases != nullptr)
     {
-        optimised_kernel = true;
+        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(biases->dimension(0) != weights->dimension(idx_kernels));
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
     }
 
-    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);
+    if(act_info.enabled())
+    {
+        ARM_COMPUTE_ERROR_ON(act_info.b() > act_info.a());
+    }
+
+    // Get convolved dimensions
+    unsigned int conv_w = 0;
+    unsigned int conv_h = 0;
+
+    std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(idx_width),
+                                                 input->dimension(idx_height),
+                                                 kernel_width,
+                                                 kernel_height,
+                                                 conv_info,
+                                                 dilation);
+
+    unsigned int mat_weights_cols = weights->dimension(idx_kernels);
+    unsigned int mat_weights_rows = weights->dimension(idx_width) * weights->dimension(idx_height) * weights->dimension(idx_channel) + bias_element;
+
+    // Output tensor auto inizialization if not yet initialized
+    ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayerReshapeWeights::validate(weights, is_quantized ? nullptr : biases, nullptr));
+    weights_reshaped_info = TensorInfo(compute_weights_reshaped_shape(*weights, append_bias), 1, data_type);
+    weights_to_use        = &weights_reshaped_info;
+
+    // TODO (giaiod01): Validate GEMM3D
 
     if(!skip_im2col)
     {
-        // Validate im2col
-        ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, &im2_col_info, kernel_weights, conv_info, append_bias, false, false, dilation));
+        // Create tensor info for im2col reshaped inputs
+        // For NEON the batch size is on the fourth dimension
+        // TODO (giaiod01): Use auto-init COMPMID-1277
+        TensorShape shape_im2col = input->tensor_shape();
+        shape_im2col.set(0, mat_weights_rows);
+        shape_im2col.set(1, conv_w * conv_h);
+        shape_im2col.set(2, 1);
+
+        im2col_reshaped_info = TensorInfo(shape_im2col, 1, data_type);
+        im2col_reshaped_info.set_quantization_info(input->quantization_info());
+
+        ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, &im2col_reshaped_info, Size2D(kernel_width, kernel_height), conv_info, append_bias, false, false, dilation));
+        gemm_input_to_use = &im2col_reshaped_info;
     }
     else if(append_bias)
     {
@@ -507,65 +411,44 @@ Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI
     }
 
     // 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);
-
-    // Reshape weights if needed
-    if(optimised_kernel)
+    if(!is_nhwc || is_quantized)
     {
-        ARM_COMPUTE_RETURN_ERROR_ON(are_weights_reshaped);
+        TensorShape shape_gemm = gemm_input_to_use->tensor_shape();
+        shape_gemm.set(0, mat_weights_cols);
+        shape_gemm.set(1, conv_w * conv_h);
+        const DataType gemm_data_type = is_quantized ? DataType::S32 : data_type;
+        // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input.
+        info_gemm = TensorInfo(shape_gemm, 1, gemm_data_type);
+        info_gemm.set_quantization_info(output->quantization_info());
 
-        // 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 */));
+        gemm_output_to_use = &info_gemm;
     }
-    else if(!is_quantized)
-    {
-        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)) };
-        }
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, gemm_output_to_use, (data_layout == DataLayout::NHWC) ? conv_h : 1, skip_im2col));
 
-        // 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();
+    if(is_quantized)
+    {
+        float multiplier = input->quantization_info().scale * weights_to_use->quantization_info().scale / output->quantization_info().scale;
+        int   output_multiplier, output_shift;
+        quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
 
-        // Validate GEMM interleave and multiply
-        if(is_interleaved)
-        {
-            TensorShape shape_interleaved = shape_im2col;
-            shape_interleaved.set(idx_width, shape_interleaved.x() * 4);
-            shape_interleaved.set(idx_height, 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, 1.f, is_interleaved, GEMMReshapeInfo(shape_im2col[1],            // m
-                                                                             weights->tensor_shape()[0], // n
-                                                                             shape_im2col[0]) /* k */));
-        }
-        else
-        {
-            ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&im2_col_info, weights, &gemm_output_info, 1.f, is_interleaved, GEMMReshapeInfo()));
-        }
+        tmp_info = TensorInfo(gemm_output_to_use->tensor_shape(), 1, DataType::QASYMM8);
+        tmp_info.set_quantization_info(output->quantization_info());
+        gemm_output_staged_to_use = &tmp_info;
+
+        // Validate output stage for quantized case
+        NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(gemm_output_to_use, biases, gemm_output_staged_to_use, output->quantization_info().offset);
     }
-    if(!is_nhwc)
+
+    // Validate Col2Im
+    if(!is_nhwc || is_quantized)
     {
-        ARM_COMPUTE_RETURN_ON_ERROR(NECol2ImKernel::validate(&gemm_output_info, output, Size2D(conv_w, conv_h)));
+        ARM_COMPUTE_RETURN_ON_ERROR(NECol2ImKernel::validate(is_quantized ? gemm_output_staged_to_use : gemm_output_to_use,
+                                                             output,
+                                                             Size2D(conv_w, conv_h)));
     }
 
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG((output->dimension(idx_width) != conv_w) || (output->dimension(idx_height) != conv_h), "Output shape does not match the expected one");
-
+    //Validate Activation Layer
     if(act_info.enabled())
     {
         ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(output, nullptr, act_info));
@@ -583,32 +466,23 @@ void NEGEMMConvolutionLayer::run()
     if(!_skip_im2col)
     {
         // Run input reshaping
-        unsigned int _y_dim = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
-        NEScheduler::get().schedule(&_input_im2col_kernel, _y_dim);
+        unsigned int y_dim = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
+        NEScheduler::get().schedule(&_im2col_kernel, y_dim);
     }
 
-    // Runs matrix multiply on reshaped matrices
-    if(_asm_glue.is_configured())
+    // Runs NEGEMM or NEGEMMLowpMatrixMultiplyCore functions
+    if(_is_quantized)
     {
-        _asm_glue.run();
+        // Run gemmlowp
+        _mm_gemmlowp.run();
+
+        // Run output stage
+        _gemmlowp_output_stage.run();
     }
     else
     {
-        if(_is_interleaved)
-        {
-            // 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 gemm
+        _mm_gemm.run();
     }
 
     if(_skip_im2col && _append_bias)
@@ -616,16 +490,10 @@ void NEGEMMConvolutionLayer::run()
         NEScheduler::get().schedule(&_add_bias_kernel, Window::DimY);
     }
 
-    // Run output stage for quantized case
-    if(_is_quantized)
-    {
-        _gemmlowp_output_stage.run();
-    }
-
     // Reshape output matrix
-    if(_data_layout == DataLayout::NCHW)
+    if(!_skip_col2im)
     {
-        NEScheduler::get().schedule(&_output_col2im_kernel, Window::DimY);
+        NEScheduler::get().schedule(&_col2im_kernel, Window::DimY);
     }
 
     if(_is_activationlayer_enabled)
@@ -640,32 +508,15 @@ void NEGEMMConvolutionLayer::prepare()
 {
     if(!_is_prepared)
     {
-        // Run weights reshaping (Runs once for every configure)
-        if(!_are_weights_reshaped)
-        {
-            ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
-
-            _weights_reshaped.allocator()->allocate();
-            _reshape_weights.run();
-            _reshape_weights = NEConvolutionLayerReshapeWeights();
-            _original_weights->mark_as_unused();
-            _are_weights_reshaped = true;
-        }
+        ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
 
-        // Run GEMM prepare stage
-        if(_asm_glue.is_configured())
-        {
-            _asm_glue.prepare();
-        }
-        else
-        {
-            if(_is_quantized)
-            {
-                _mm_gemmlowp.prepare();
-            }
-        }
+        // Run weights reshaping and mark original weights tensor as unused
+        _weights_reshaped.allocator()->allocate();
+        _reshape_weights.run();
+        _original_weights->mark_as_unused();
 
-        // Release weights in case buffer is pretransposed
+        // Prepare GEMM
+        _is_quantized ? _mm_gemmlowp.prepare() : _mm_gemm.prepare();
         if(!_weights_reshaped.is_used())
         {
             _weights_reshaped.allocator()->free();
@@ -674,4 +525,3 @@ void NEGEMMConvolutionLayer::prepare()
         _is_prepared = true;
     }
 }
-} // namespace arm_compute