COMPMID-1635: Optimize CLDeconvolutionLayer - Part III

Change-Id: Id2661e093a669ef3eaf2a5116cd278a80c1d5a89
Signed-off-by: giuros01 <giuseppe.rossini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/935
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Comments-Addressed: Gian Marco Iodice <gianmarco.iodice@arm.com>
Reviewed-by: Isabella Gottardi <isabella.gottardi@arm.com>
Comments-Addressed: Isabella Gottardi <isabella.gottardi@arm.com>
Tested-by: Isabella Gottardi <isabella.gottardi@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

4 files changed, 444 insertions, 9 deletions

diff --git a/src/runtime/CL/functions/CLDeconvolutionLayer.cpp b/src/runtime/CL/functions/CLDeconvolutionLayer.cpp
index 2c17473fc7..c6f79d341f 100644
--- a/src/runtime/CL/functions/CLDeconvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLDeconvolutionLayer.cpp
@@ -44,11 +44,29 @@ CLDeconvolutionLayer::CLDeconvolutionLayer(std::shared_ptr<IMemoryManager> memor
 void CLDeconvolutionLayer::configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &deconv_info,
                                      unsigned int inner_border_right, unsigned int inner_border_top, const WeightsInfo &weights_info)
 {
-    ARM_COMPUTE_UNUSED(inner_border_right, inner_border_top);
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
-    auto f = arm_compute::support::cpp14::make_unique<CLDirectDeconvolutionLayer>();
-    f->configure(input, weights, bias, output, deconv_info, weights_info);
-    _function = std::move(f);
+    ARM_COMPUTE_UNUSED(inner_border_right, inner_border_top);
+
+    switch(CLDeconvolutionLayer::get_deconvolution_method(input->info(), weights->info(), nullptr, output->info(), deconv_info, weights_info))
+    {
+        case DeconvolutionMethod::DIRECT:
+        {
+            auto f = arm_compute::support::cpp14::make_unique<CLDirectDeconvolutionLayer>();
+            f->configure(input, weights, bias, output, deconv_info, weights_info);
+            _function = std::move(f);
+            break;
+        }
+        case DeconvolutionMethod::GEMM:
+        {
+            auto f = arm_compute::support::cpp14::make_unique<CLGEMMDeconvolutionLayer>(_memory_manager);
+            f->configure(input, weights, bias, output, deconv_info);
+            _function = std::move(f);
+            break;
+        }
+        default:
+            ARM_COMPUTE_ERROR("Not supported.");
+            break;
+    }
 }
 
 Status CLDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &deconv_info,
@@ -56,10 +74,47 @@ Status CLDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInf
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
     ARM_COMPUTE_UNUSED(inner_border_right, inner_border_top);
-    ARM_COMPUTE_RETURN_ON_ERROR(CLDirectDeconvolutionLayer::validate(input, weights, bias, output, deconv_info, weights_info));
+
+    switch(CLDeconvolutionLayer::get_deconvolution_method(input, weights, bias, output, deconv_info, weights_info))
+    {
+        case DeconvolutionMethod::DIRECT:
+        {
+            // Validate direct convolution layer
+            ARM_COMPUTE_RETURN_ON_ERROR(CLDirectDeconvolutionLayer::validate(input, weights, bias, output, deconv_info, weights_info));
+            break;
+        }
+        case DeconvolutionMethod::GEMM:
+        {
+            // Validate gemm-based convolution layer
+            ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMDeconvolutionLayer::validate(input, weights, bias, output, deconv_info));
+            break;
+        }
+        default:
+            ARM_COMPUTE_ERROR("Not supported.");
+            break;
+    }
+
     return Status{};
 }
 
+DeconvolutionMethod CLDeconvolutionLayer::get_deconvolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &deconv_info,
+                                                                   const WeightsInfo &weights_info)
+{
+    ARM_COMPUTE_UNUSED(output, bias, weights_info);
+
+    const DataLayout data_layout = input->data_layout();
+
+    const size_t idx_w = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+    const size_t idx_h = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+
+    if(weights->dimension(idx_w) != deconv_info.stride().first || weights->dimension(idx_h) != deconv_info.stride().second)
+    {
+        return DeconvolutionMethod::DIRECT;
+    }
+
+    return DeconvolutionMethod::GEMM;
+}
+
 void CLDeconvolutionLayer::configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &deconv_info,
                                      const WeightsInfo &weights_info)
 {
diff --git a/src/runtime/CL/functions/CLDirectDeconvolutionLayer.cpp b/src/runtime/CL/functions/CLDirectDeconvolutionLayer.cpp
index 721054dcf3..6e14e26cbd 100644
--- a/src/runtime/CL/functions/CLDirectDeconvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLDirectDeconvolutionLayer.cpp
@@ -28,7 +28,6 @@
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/runtime/CL/CLScheduler.h"
-#include "utils/TypePrinter.h"
 
 #include <memory>
 #include <tuple>
@@ -161,8 +160,16 @@ void CLDirectDeconvolutionLayer::configure(ICLTensor *input, ICLTensor *weights,
     _flip_axis.allocator()->allocate();
     _flip_axis.map(true);
     auto axis_data = reinterpret_cast<uint32_t *>(_flip_axis.buffer());
-    axis_data[0]   = 0;
-    axis_data[1]   = 1;
+    if(weights->info()->data_layout() == DataLayout::NHWC)
+    {
+        axis_data[0] = 1;
+        axis_data[1] = 2;
+    }
+    else
+    {
+        axis_data[0] = 0;
+        axis_data[1] = 1;
+    }
     _flip_axis.unmap();
 }
 
diff --git a/src/runtime/CL/functions/CLGEMM.cpp b/src/runtime/CL/functions/CLGEMM.cpp
index 60bfbf24e5..492709f0d0 100644
--- a/src/runtime/CL/functions/CLGEMM.cpp
+++ b/src/runtime/CL/functions/CLGEMM.cpp
@@ -206,7 +206,7 @@ void CLGEMM::configure_reshaped_v2(const ICLTensor *a, const ICLTensor *b, const
     _reshape_lhs_kernel.set_target(gpu_target);
     _mm_kernel.set_target(gpu_target);
 
-    GEMMReshapeInfo reshape_info(m, n, k, 1, 1, depth_output_gemm3d, reinterpret_input_as_3d);
+    GEMMReshapeInfo reshape_info(m, n, k, 1, 1, depth_output_gemm3d, false);
 
     // Manage intermediate buffers
     _memory_group.manage(&_tmp_a);
diff --git a/src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp
new file mode 100644
index 0000000000..bcb91e052c
--- /dev/null
+++ b/src/runtime/CL/functions/CLGEMMDeconvolutionLayer.cpp
@@ -0,0 +1,373 @@
+/*
+ * Copyright (c) 2019 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/CLGEMMDeconvolutionLayer.h"
+
+#include "arm_compute/core/Helpers.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/CL/CLScheduler.h"
+#include "utils/TypePrinter.h"
+
+#include <memory>
+#include <tuple>
+
+namespace arm_compute
+{
+namespace
+{
+std::pair<Coordinates, Coordinates> compute_start_end_slice_coordinates(const ITensorInfo &output_info, const PadStrideInfo &deconv_info, bool is_nchw)
+{
+    Coordinates start;
+    Coordinates end;
+
+    if(is_nchw)
+    {
+        start.set(0, deconv_info.pad_left());
+        start.set(1, deconv_info.pad_top());
+        end.set(0, output_info.dimension(0) - deconv_info.pad_right());
+        end.set(1, output_info.dimension(1) - deconv_info.pad_bottom());
+    }
+    else
+    {
+        start.set(0, 0);
+        start.set(1, deconv_info.pad_left());
+        start.set(2, deconv_info.pad_top());
+
+        end.set(0, output_info.dimension(0));
+        end.set(1, output_info.dimension(1) - deconv_info.pad_right());
+        end.set(2, output_info.dimension(2) - deconv_info.pad_bottom());
+    }
+
+    return { start, end };
+}
+} // namespace
+
+CLGEMMDeconvolutionLayer::CLGEMMDeconvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) // NOLINT
+    : _memory_group(std::move(memory_manager)),
+      _mm_gemm(),
+      _mm_gemmlowp(),
+      _gemmlowp_output_stage(),
+      _permute_input_to_nhwc(),
+      _permute_weights_to_nhwc(),
+      _reshape_weights(),
+      _transpose_weights(),
+      _deconv_reshape(),
+      _slice_gemm(),
+      _gemmlowp_final(),
+      _reshaped_weights(),
+      _reshaped_weights_t(),
+      _permuted_input(),
+      _permuted_weights(),
+      _gemm_output(),
+      _slice_gemm_input(),
+      _original_weights(),
+      _is_prepared(false),
+      _padded_input(false),
+      _is_nchw(false),
+      _is_quantized(false)
+{
+}
+
+Status CLGEMMDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, const ITensorInfo *output, const PadStrideInfo &deconv_info)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::F16, DataType::QASYMM8);
+    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 bool padded_input = deconv_info.pad_bottom() > 0 || deconv_info.pad_left() > 0 || deconv_info.pad_right() > 0 || deconv_info.pad_top() > 0;
+    const bool is_nchw      = input->data_layout() == DataLayout::NCHW;
+    const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
+
+    const size_t idx_w = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+    const size_t idx_h = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+    const size_t idx_b = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES);
+
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) != deconv_info.stride().first);
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_h) != deconv_info.stride().second);
+
+    TensorShape nhwc_weights_shape = weights->tensor_shape();
+    TensorShape nhwc_input_shape   = input->tensor_shape();
+
+    if(is_nchw)
+    {
+        permute(nhwc_weights_shape, PermutationVector(2, 0, 1));
+        permute(nhwc_input_shape, PermutationVector(2, 0, 1));
+
+        TensorInfo nhwc_input_info = input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(nhwc_input_shape).set_data_layout(DataLayout::NCHW);
+
+        TensorInfo nhwc_weights_info = weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(nhwc_weights_shape).set_data_layout(DataLayout::NCHW);
+
+        CLPermute::validate(weights, &nhwc_weights_info, PermutationVector(2, 0, 1));
+        CLPermute::validate(input, &nhwc_input_info, PermutationVector(2, 0, 1));
+    }
+
+    const TensorShape reshaped_shape = TensorShape(nhwc_weights_shape[0], nhwc_weights_shape[1] * nhwc_weights_shape[2] * nhwc_weights_shape[3]);
+    const TensorInfo  reshaped_info  = weights->clone()->set_tensor_shape(reshaped_shape).set_data_layout(DataLayout::NCHW).set_is_resizable(true);
+    ARM_COMPUTE_RETURN_ON_ERROR(CLReshapeLayer::validate(weights, &reshaped_info));
+
+    TensorShape      transposed_shape(reshaped_shape[1], reshaped_shape[0]);
+    const TensorInfo reshaped_t_info = reshaped_info.clone()->set_is_resizable(true).set_tensor_shape(transposed_shape);
+    ARM_COMPUTE_RETURN_ON_ERROR(CLTranspose::validate(&reshaped_info, &reshaped_t_info));
+
+    TensorShape gemm_output_shape(weights->dimension(idx_w) * weights->dimension(idx_h) * weights->dimension(idx_b),
+                                  input->dimension(idx_w),
+                                  input->dimension(idx_h),
+                                  input->dimension(idx_b));
+
+    TensorInfo gemm_output_info = reshaped_t_info.clone()->set_tensor_shape(gemm_output_shape).set_is_resizable(true);
+    GEMMInfo   gemm_info(false, false, true, input->dimension(idx_h), true);
+
+    if(is_quantized)
+    {
+        ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixMultiplyCore::validate(&input->clone()->set_tensor_shape(nhwc_input_shape), &reshaped_t_info, nullptr, &gemm_output_info.set_data_type(DataType::S32),
+                                                                           gemm_info));
+    }
+    else
+    {
+        ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(&input->clone()->set_tensor_shape(nhwc_input_shape).set_is_resizable(true), &reshaped_t_info, nullptr, &gemm_output_info, 1.0f, 0.0f, gemm_info));
+    }
+
+    auto out_dims = deconvolution_output_dimensions(input->dimension(idx_w), input->dimension(idx_h), weights->dimension(idx_w), weights->dimension(idx_h),
+                                                    0, 0, deconv_info.stride().first, deconv_info.stride().second);
+    const TensorShape deconv_shape       = misc::shape_calculator::compute_deconvolution_output_shape(out_dims, *input, *weights);
+    TensorInfo        col2im_output_info = gemm_output_info.clone()->set_tensor_shape(deconv_shape).set_is_resizable(true);
+
+    if(padded_input && is_quantized)
+    {
+        const auto start_end = compute_start_end_slice_coordinates(col2im_output_info, deconv_info, is_nchw);
+        ARM_COMPUTE_RETURN_ON_ERROR(CLDeconvolutionReshapeOutputKernel::validate(&gemm_output_info, bias, &col2im_output_info, input, weights, deconv_info));
+        ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(&col2im_output_info, nullptr,
+                                                                                                  &col2im_output_info.clone()->set_is_resizable(true).set_data_type(DataType::QASYMM8)));
+        ARM_COMPUTE_RETURN_ON_ERROR(CLSlice::validate(&col2im_output_info.clone()->set_is_resizable(true).set_data_type(DataType::QASYMM8), output, start_end.first, start_end.second));
+    }
+    else if(padded_input)
+    {
+        const auto start_end = compute_start_end_slice_coordinates(col2im_output_info, deconv_info, is_nchw);
+        ARM_COMPUTE_RETURN_ON_ERROR(CLDeconvolutionReshapeOutputKernel::validate(&gemm_output_info, bias, &col2im_output_info, input, weights, deconv_info));
+        ARM_COMPUTE_RETURN_ON_ERROR(CLSlice::validate(&col2im_output_info, output, start_end.first, start_end.second));
+    }
+    else if(is_quantized)
+    {
+        ARM_COMPUTE_RETURN_ON_ERROR(CLDeconvolutionReshapeOutputKernel::validate(&gemm_output_info, bias, &col2im_output_info, input, weights, deconv_info));
+        ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(&col2im_output_info, nullptr, output));
+    }
+    else
+    {
+        ARM_COMPUTE_RETURN_ON_ERROR(CLDeconvolutionReshapeOutputKernel::validate(&gemm_output_info, bias, output, input, weights, deconv_info));
+    }
+
+    return Status{};
+}
+
+void CLGEMMDeconvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &deconv_info)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
+    ARM_COMPUTE_ERROR_THROW_ON(CLGEMMDeconvolutionLayer::validate(input->info(),
+                                                                  weights->info(),
+                                                                  bias != nullptr ? bias->info() : nullptr,
+                                                                  output->info(),
+                                                                  deconv_info));
+
+    _original_weights = weights;
+    _padded_input     = deconv_info.pad_bottom() > 0 || deconv_info.pad_left() > 0 || deconv_info.pad_right() > 0 || deconv_info.pad_top() > 0;
+    _is_nchw          = input->info()->data_layout() == DataLayout::NCHW;
+    _is_quantized     = is_data_type_quantized_asymmetric(input->info()->data_type());
+
+    const ICLTensor *input_to_use   = input;
+    const ICLTensor *weights_to_use = weights;
+
+    // If the data layout is NCHW, transform everything in NHWC. Another alternative could be to
+    // do an outer product in NCHW and then an accumulation through a reduction. This would have two
+    // drawbacks: first, the outer product is less efficient than a full GEMM. Second, the reduction
+    // might be slower than GEMM.
+    if(_is_nchw)
+    {
+        _memory_group.manage(&_permuted_input);
+        _permute_input_to_nhwc.configure(input, &_permuted_input, PermutationVector(2U, 0U, 1U));
+
+        _permute_weights_to_nhwc.configure(weights, &_permuted_weights, PermutationVector(2U, 0U, 1U));
+
+        input_to_use   = &_permuted_input;
+        weights_to_use = &_permuted_weights;
+    }
+
+    // Reshape the input weights. The weights will be reshaped only once during the call to prepare()
+    _reshaped_weights.allocator()->init(TensorInfo(TensorShape(weights_to_use->info()->dimension(0),
+                                                               weights_to_use->info()->dimension(1) * weights_to_use->info()->dimension(2) * weights_to_use->info()->dimension(3)),
+                                                   1,
+                                                   input->info()->data_type(), weights->info()->quantization_info()));
+
+    _reshape_weights.configure(weights_to_use, &_reshaped_weights);
+    _transpose_weights.configure(&_reshaped_weights, &_reshaped_weights_t);
+
+    const size_t idx_h = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT);
+    GEMMInfo     gemm_info(false, false, true, input->info()->dimension(idx_h), true);
+
+    // Configure output stage for asymmetric quantized types
+    if(_is_quantized)
+    {
+        _mm_gemmlowp.configure(input_to_use, &_reshaped_weights_t, nullptr, &_gemm_output, gemm_info);
+    }
+    else
+    {
+        _mm_gemm.configure(input_to_use, &_reshaped_weights_t, nullptr, &_gemm_output, 1.f, 0.0f, gemm_info);
+    }
+
+    if(_is_nchw)
+    {
+        _permuted_input.allocator()->allocate();
+    }
+
+    ICLTensor *deconv_reshape_output = nullptr;
+    ICLTensor *slice_output          = nullptr;
+    ICLTensor *output_stage_output   = nullptr;
+
+    if(_padded_input && _is_quantized)
+    {
+        _memory_group.manage(&_slice_gemm_input);
+        _memory_group.manage(&_gemmlowp_final);
+        deconv_reshape_output = &_gemmlowp_final;
+        output_stage_output   = &_slice_gemm_input;
+        slice_output          = output;
+    }
+    else if(_padded_input)
+    {
+        _memory_group.manage(&_slice_gemm_input);
+        deconv_reshape_output = &_slice_gemm_input;
+        slice_output          = output;
+    }
+    else if(_is_quantized)
+    {
+        _memory_group.manage(&_gemmlowp_final);
+        deconv_reshape_output = &_gemmlowp_final;
+        output_stage_output   = output;
+    }
+    else
+    {
+        deconv_reshape_output = output;
+    }
+
+    // Configure a Col2Im call to reshape the output of GEMM
+    _deconv_reshape.configure(&_gemm_output, bias, deconv_reshape_output, input->info(), weights->info(), deconv_info);
+    _gemm_output.allocator()->allocate();
+
+    if(_is_quantized)
+    {
+        float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / _gemmlowp_final.info()->quantization_info().scale;
+        int   output_multiplier(0);
+        int   output_shift(0);
+        quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
+        _gemmlowp_output_stage.configure(&_gemmlowp_final, nullptr, output_stage_output, output_multiplier, output_shift, _gemmlowp_final.info()->quantization_info().offset);
+        _gemmlowp_final.allocator()->allocate();
+    }
+
+    // If the input was padded, the output needs to be sliced.
+    if(_padded_input)
+    {
+        const auto start_end = compute_start_end_slice_coordinates(*deconv_reshape_output->info(), deconv_info, _is_nchw);
+        _slice_gemm.configure(&_slice_gemm_input, slice_output, start_end.first, start_end.second);
+        _slice_gemm_input.allocator()->allocate();
+    }
+}
+
+void CLGEMMDeconvolutionLayer::run()
+{
+    prepare();
+
+    MemoryGroupResourceScope scope_mg(_memory_group);
+
+    if(_is_nchw)
+    {
+        _permute_input_to_nhwc.run();
+    }
+
+    if(_is_quantized)
+    {
+        _mm_gemmlowp.run();
+    }
+    else
+    {
+        _mm_gemm.run();
+    }
+
+    CLScheduler::get().enqueue(_deconv_reshape, false);
+
+    if(_is_quantized)
+    {
+        _gemmlowp_output_stage.run();
+    }
+
+    if(_padded_input)
+    {
+        _slice_gemm.run();
+    }
+}
+
+void CLGEMMDeconvolutionLayer::prepare()
+{
+    if(!_is_prepared)
+    {
+        ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
+
+        if(_is_nchw)
+        {
+            _permuted_weights.allocator()->allocate();
+            _permute_weights_to_nhwc.run();
+        }
+
+        _reshaped_weights.allocator()->allocate();
+        _reshape_weights.run();
+
+        if(_is_nchw)
+        {
+            _permuted_weights.allocator()->free();
+        }
+
+        _reshaped_weights_t.allocator()->allocate();
+        _transpose_weights.run();
+
+        // Prepare gemm
+        if(!_is_quantized)
+        {
+            _mm_gemm.prepare();
+        }
+        else
+        {
+            _mm_gemmlowp.prepare();
+        }
+
+        // Free resources
+        if(!_reshaped_weights_t.is_used())
+        {
+            _reshaped_weights_t.allocator()->free();
+        }
+
+        _original_weights->mark_as_unused();
+        _is_prepared = true;
+    }
+}
+} // namespace arm_compute