COMPMID-1975: Update depthwise convolution.

Change-Id: Iad58672be35710a7ec2e918653d6d529709387e8
Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-on: https://review.mlplatform.org/c/898
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Giuseppe Rossini <giuseppe.rossini@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>

2 files changed, 555 insertions, 115 deletions

diff --git a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp
index f0fd4cf256..5db94a67c0 100644
--- a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp
@@ -31,112 +31,78 @@
 #include "arm_compute/runtime/NEON/NEScheduler.h"
 #include "support/ToolchainSupport.h"
 
-using namespace arm_compute;
+#include "arm_compute/core/utils/misc/InfoHelpers.h"
+
 using namespace arm_compute::misc;
 using namespace arm_compute::misc::shape_calculator;
 
-NEDepthwiseConvolutionLayer3x3::NEDepthwiseConvolutionLayer3x3()
-    : _dwc_kernel(), _output_stage_kernel(), _border_handler(), _permute_input(), _permute_weights(), _permute_output(), _activationlayer_function(), _accumulator(), _permuted_input(),
-      _permuted_weights(), _permuted_output(), _has_bias(false), _is_quantized(false), _is_optimized(false), _are_weights_reshaped(false), _is_nchw(true), _is_first_run(true), _permute(false),
-      _is_activationlayer_enabled(false)
+namespace arm_compute
+{
+NEDepthwiseConvolutionLayer3x3::NEDepthwiseConvolutionLayer3x3(std::shared_ptr<IMemoryManager> memory_manager)
+    : _memory_group(memory_manager), _dwc_kernel(), _dwc_optimized_func(memory_manager), _output_stage_kernel(), _border_handler(), _permute_input(), _permute_weights(), _permute_output(),
+      _activationlayer_function(), _accumulator(), _permuted_input(), _permuted_weights(), _permuted_output(), _original_weights(nullptr), _has_bias(false), _is_quantized(false), _is_optimized(false),
+      _is_nchw(true), _permute(false), _is_activationlayer_enabled(false), _is_prepared(false)
 {
 }
 
-void NEDepthwiseConvolutionLayer3x3::configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
-                                               unsigned int depth_multiplier, const ActivationLayerInfo &act_info)
+void NEDepthwiseConvolutionLayer3x3::configure_generic(ITensor                   *input,
+                                                       const ITensor             *weights,
+                                                       const ITensor             *biases,
+                                                       ITensor                   *output,
+                                                       const PadStrideInfo       &conv_info,
+                                                       unsigned int               depth_multiplier,
+                                                       const ActivationLayerInfo &act_info)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+    ARM_COMPUTE_UNUSED(act_info);
 
     PixelValue zero_value(0.f);
 
-    _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type());
-    _has_bias     = biases != nullptr;
-    _is_optimized = NEDepthwiseConvolutionLayer3x3Kernel::is_optimized_execution_possible(input->info()->tensor_shape(),
-                                                                                          conv_info,
-                                                                                          input->info()->data_type(),
-                                                                                          depth_multiplier,
-                                                                                          input->info()->data_layout());
-    _are_weights_reshaped = false;
-    _is_nchw              = input->info()->data_layout() == DataLayout::NCHW;
-    _permute              = _is_optimized == _is_nchw;
-
     // Initialize the intermediate accumulator tensor in case of quantized input
     if(_is_quantized)
     {
         TensorShape accum_shape  = output->info()->tensor_shape();
         DataLayout  accum_layout = output->info()->data_layout();
-        if(!_is_optimized && !_is_nchw)
+        if(!_is_nchw)
         {
             permute(accum_shape, PermutationVector(1U, 2U, 0U));
             accum_layout = DataLayout::NCHW;
         }
 
+        _memory_group.manage(&_accumulator);
         _accumulator.allocator()->init(TensorInfo(accum_shape, 1, DataType::S32, output->info()->quantization_info()));
         _accumulator.info()->set_data_layout(accum_layout);
         zero_value = PixelValue(static_cast<uint32_t>(input->info()->quantization_info().offset));
     }
 
-    if(_is_optimized)
+    if(!_is_nchw)
     {
-        ITensor *optimized_output = (_is_quantized) ? &_accumulator : output;
-        if(_is_nchw)
-        {
-            // Configure the function to transform the input tensor from NCHW -> NHWC
-            _permute_input.configure(input, &_permuted_input, PermutationVector(2U, 0U, 1U));
-            _permuted_input.info()->set_data_layout(DataLayout::NHWC);
+        _memory_group.manage(&_permuted_input);
+        _memory_group.manage(&_permuted_output);
 
-            // Configure the function to transform the weights tensor from IHW -> HWI
-            _permute_weights.configure(weights, &_permuted_weights, PermutationVector(2U, 0U, 1U));
-            _permuted_weights.info()->set_data_layout(DataLayout::NHWC);
+        // Configure the function to transform the input tensor from NHWC -> NCHW
+        _permute_input.configure(input, &_permuted_input, PermutationVector(1U, 2U, 0U));
+        _permuted_input.info()->set_data_layout(DataLayout::NCHW);
 
-            // Configure optimized depthwise
-            _dwc_kernel.configure(&_permuted_input, &_permuted_weights, &_permuted_output, conv_info, depth_multiplier, DataLayout::NHWC);
+        // Configure the function to transform the weights tensor from HWI -> IHW
+        _permute_weights.configure(weights, &_permuted_weights, PermutationVector(1U, 2U, 0U));
+        _permuted_weights.info()->set_data_layout(DataLayout::NCHW);
 
-            // Configure the function to transform the convoluted output to ACL's native ordering format NCHW
-            _permuted_output.info()->set_data_layout(DataLayout::NHWC);
-            _permute_output.configure(&_permuted_output, optimized_output, PermutationVector(1U, 2U, 0U));
+        // Configure optimized depthwise
+        _dwc_kernel.configure(&_permuted_input, &_permuted_weights, (_is_quantized) ? &_accumulator : &_permuted_output, conv_info, depth_multiplier);
 
-            // Allocate tensors
-            _permuted_input.allocator()->allocate();
-            _permuted_weights.allocator()->allocate();
-            _permuted_output.allocator()->allocate();
-        }
-        else
-        {
-            _dwc_kernel.configure(input, weights, optimized_output, conv_info, depth_multiplier, DataLayout::NHWC);
-        }
+        // Configure border handler
+        _border_handler.configure(&_permuted_input, _dwc_kernel.border_size(), BorderMode::CONSTANT, zero_value);
+
+        // Allocate tensors
+        _permuted_input.allocator()->allocate();
     }
     else
     {
-        if(!_is_nchw)
-        {
-            // Configure the function to transform the input tensor from NHWC -> NCHW
-            _permute_input.configure(input, &_permuted_input, PermutationVector(1U, 2U, 0U));
-            _permuted_input.info()->set_data_layout(DataLayout::NCHW);
-
-            // Configure the function to transform the weights tensor from HWI -> IHW
-            _permute_weights.configure(weights, &_permuted_weights, PermutationVector(1U, 2U, 0U));
-            _permuted_weights.info()->set_data_layout(DataLayout::NCHW);
-
-            // Configure optimized depthwise
-            _dwc_kernel.configure(&_permuted_input, &_permuted_weights, (_is_quantized) ? &_accumulator : &_permuted_output, conv_info, depth_multiplier);
-
-            // Configure border handler
-            _border_handler.configure(&_permuted_input, _dwc_kernel.border_size(), BorderMode::CONSTANT, zero_value);
+        // Configure depthwise convolution kernel
+        _dwc_kernel.configure(input, weights, (_is_quantized) ? &_accumulator : output, conv_info, depth_multiplier);
 
-            // Allocate tensors
-            _permuted_input.allocator()->allocate();
-            _permuted_weights.allocator()->allocate();
-        }
-        else
-        {
-            // Configure depthwise convolution kernel
-            _dwc_kernel.configure(input, weights, (_is_quantized) ? &_accumulator : output, conv_info, depth_multiplier);
-
-            // Configure border handler
-            _border_handler.configure(input, _dwc_kernel.border_size(), BorderMode::CONSTANT, zero_value);
-        }
+        // Configure border handler
+        _border_handler.configure(input, _dwc_kernel.border_size(), BorderMode::CONSTANT, zero_value);
     }
 
     // Configure biases accumulation
@@ -147,32 +113,116 @@ void NEDepthwiseConvolutionLayer3x3::configure(ITensor *input, const ITensor *we
         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);
-        _output_stage_kernel.configure(&_accumulator, biases, (_is_nchw || _is_optimized) ? output : &_permuted_output, output_multiplier, output_shift, output_quant_info.offset);
+        _output_stage_kernel.configure(&_accumulator, biases, _is_nchw ? output : &_permuted_output, output_multiplier, output_shift, output_quant_info.offset);
         _accumulator.allocator()->allocate();
     }
     else if(_has_bias)
     {
-        _output_stage_kernel.configure((_is_nchw || _is_optimized) ? output : &_permuted_output, biases);
+        _output_stage_kernel.configure(_is_nchw ? output : &_permuted_output, biases);
     }
 
-    if(!_is_optimized && !_is_nchw)
+    // Permute output
+    if(!_is_nchw)
     {
         // Configure the function to transform the convoluted output to NHWC
         _permute_output.configure(&_permuted_output, output, PermutationVector(2U, 0U, 1U));
         _permuted_output.allocator()->allocate();
     }
+}
+
+void NEDepthwiseConvolutionLayer3x3::configure_optimized(const ITensor             *input,
+                                                         const ITensor             *weights,
+                                                         const ITensor             *biases,
+                                                         ITensor                   *output,
+                                                         const PadStrideInfo       &conv_info,
+                                                         unsigned int               depth_multiplier,
+                                                         const ActivationLayerInfo &act_info)
+{
+    ActivationLayerInfo act_info_to_use = ActivationLayerInfo();
+    const bool          is_relu         = arm_compute::utils::info_helpers::is_relu(act_info);
+    const bool          is_relu6        = arm_compute::utils::info_helpers::is_relu6(act_info);
+    _is_activationlayer_enabled         = act_info.enabled() && !(is_relu || is_relu6);
+    if(!_is_activationlayer_enabled)
+    {
+        act_info_to_use = act_info;
+    }
+
+    if(_is_nchw)
+    {
+        _memory_group.manage(&_permuted_input);
+        _memory_group.manage(&_permuted_output);
+
+        // Configure the function to transform the input tensor from NCHW -> NHWC
+        _permute_input.configure(input, &_permuted_input, PermutationVector(2U, 0U, 1U));
+        _permuted_input.info()->set_data_layout(DataLayout::NHWC);
+
+        // Configure the function to transform the weights tensor from IHW -> HWI
+        _permute_weights.configure(weights, &_permuted_weights, PermutationVector(2U, 0U, 1U));
+        _permuted_weights.info()->set_data_layout(DataLayout::NHWC);
+
+        // Configure optimized depthwise
+        _dwc_optimized_func.configure(&_permuted_input, &_permuted_weights, biases, &_permuted_output, conv_info, depth_multiplier, act_info_to_use);
+
+        // Configure the function to transform the convoluted output to ACL's native ordering format NCHW
+        _permuted_output.info()->set_data_layout(DataLayout::NHWC);
+        _permute_output.configure(&_permuted_output, output, PermutationVector(1U, 2U, 0U));
+
+        // Allocate tensors
+        _permuted_input.allocator()->allocate();
+        _permuted_output.allocator()->allocate();
+    }
+    else
+    {
+        _dwc_optimized_func.configure(input, weights, biases, output, conv_info, depth_multiplier, act_info_to_use);
+    }
+}
 
-    //Configure Activation Layer
+void NEDepthwiseConvolutionLayer3x3::configure(ITensor       *input,
+                                               const ITensor *weights,
+                                               const ITensor *biases,
+                                               ITensor *output, const PadStrideInfo &conv_info,
+                                               unsigned int               depth_multiplier,
+                                               const ActivationLayerInfo &act_info)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+
+    _original_weights = weights;
+    _is_quantized     = is_data_type_quantized_asymmetric(input->info()->data_type());
+    _has_bias         = biases != nullptr;
+    _is_optimized     = NEDepthwiseConvolutionAssemblyDispatch::is_optimized_supported(input->info(),
+                                                                                       weights->info(),
+                                                                                       conv_info,
+                                                                                       depth_multiplier);
+    _is_nchw                    = input->info()->data_layout() == DataLayout::NCHW;
+    _permute                    = _is_optimized == _is_nchw;
+    _is_prepared                = false;
     _is_activationlayer_enabled = act_info.enabled();
 
+    // Configure appropriate pipeline
+    if(_is_optimized)
+    {
+        configure_optimized(input, weights, biases, output, conv_info, depth_multiplier, act_info);
+    }
+    else
+    {
+        configure_generic(input, weights, biases, output, conv_info, depth_multiplier, act_info);
+    }
+
+    // Configure activation
     if(_is_activationlayer_enabled)
     {
         _activationlayer_function.configure(output, nullptr, act_info);
     }
 }
 
-Status NEDepthwiseConvolutionLayer3x3::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
-                                                unsigned int depth_multiplier, const ActivationLayerInfo &act_info)
+Status NEDepthwiseConvolutionLayer3x3::validate(const ITensorInfo         *input,
+                                                const ITensorInfo         *weights,
+                                                const ITensorInfo         *biases,
+                                                const ITensorInfo         *output,
+                                                const PadStrideInfo       &conv_info,
+                                                unsigned int               depth_multiplier,
+                                                const ActivationLayerInfo &act_info)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
     ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN);
@@ -184,14 +234,20 @@ Status NEDepthwiseConvolutionLayer3x3::validate(const ITensorInfo *input, const
         ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(channel_idx));
     }
 
-    const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
-    TensorInfo accumulator  = TensorInfo(output->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32));
-
-    ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseConvolutionLayer3x3Kernel::validate(input, weights, is_quantized ? &accumulator : output, conv_info, depth_multiplier));
+    if(!NEDepthwiseConvolutionAssemblyDispatch::is_optimized_supported(input, weights, conv_info, depth_multiplier))
+    {
+        const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
+        TensorInfo accumulator  = TensorInfo(output->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32));
+        ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseConvolutionLayer3x3Kernel::validate(input, weights, is_quantized ? &accumulator : output, conv_info, depth_multiplier));
 
-    if(is_quantized)
+        if(is_quantized)
+        {
+            ARM_COMPUTE_RETURN_ON_ERROR(NEDirectConvolutionLayerOutputStageKernel::validate(&accumulator, biases, output));
+        }
+    }
+    else
     {
-        ARM_COMPUTE_RETURN_ON_ERROR(NEDirectConvolutionLayerOutputStageKernel::validate(&accumulator, biases, output));
+        ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseConvolutionAssemblyDispatch::validate(input, weights, biases, output, conv_info, depth_multiplier));
     }
 
     //Validate Activation Layer
@@ -203,59 +259,86 @@ Status NEDepthwiseConvolutionLayer3x3::validate(const ITensorInfo *input, const
     return Status{};
 }
 
-void NEDepthwiseConvolutionLayer3x3::run()
+void NEDepthwiseConvolutionLayer3x3::run_generic()
 {
-    if(_is_first_run && _is_optimized)
-    {
-        _is_first_run = false;
-        // Create convolver (deferred)
-        _dwc_kernel.generate_convolver();
-    }
+    // Fill border
+    NEScheduler::get().schedule(&_border_handler, Window::DimX);
 
-    // Permute weights
-    if(_permute)
-    {
-        if(!_are_weights_reshaped)
-        {
-            _are_weights_reshaped = true;
-            _permute_weights.run();
-        }
+    // Execute depthwise convolution
+    NEScheduler::get().schedule(&_dwc_kernel, Window::DimX);
 
-        _permute_input.run();
+    // Add biases
+    if(_has_bias || _is_quantized)
+    {
+        NEScheduler::get().schedule(&_output_stage_kernel, Window::DimX);
     }
 
-    // Handle input
-    if(!_is_optimized)
+    // Permute output
+    if(!_is_nchw)
     {
-        // Fill border
-        NEScheduler::get().schedule(&_border_handler, Window::DimX);
+        _permute_output.run();
     }
+}
 
-    // Execute depthwise convolution
-    NEScheduler::get().schedule(&_dwc_kernel, Window::DimX);
+void NEDepthwiseConvolutionLayer3x3::run_optimized()
+{
+    // Run assembly function
+    _dwc_optimized_func.run();
 
     // Permute output
-    if(_is_optimized && _is_nchw)
+    if(_is_nchw)
     {
         _permute_output.run();
     }
+}
 
-    // Add biases
-    if(_has_bias || _is_quantized)
-    {
-        NEScheduler::get().schedule(&_output_stage_kernel, Window::DimX);
-    }
+void NEDepthwiseConvolutionLayer3x3::run()
+{
+    prepare();
 
-    // Permute output
-    if(!_is_optimized && !_is_nchw)
+    _memory_group.acquire();
+
+    // Permute input
+    if(_permute)
     {
-        _permute_output.run();
+        _permute_input.run();
     }
 
+    _is_optimized ? run_optimized() : run_generic();
+
+    // Run activation
     if(_is_activationlayer_enabled)
     {
         _activationlayer_function.run();
     }
+
+    _memory_group.release();
+}
+
+void NEDepthwiseConvolutionLayer3x3::prepare()
+{
+    if(!_is_prepared)
+    {
+        // Permute weights
+        if(_permute)
+        {
+            _permuted_weights.allocator()->allocate();
+            _permute_weights.run();
+            _original_weights->mark_as_unused();
+        }
+
+        // Prepare optimized function
+        if(_is_optimized)
+        {
+            _dwc_optimized_func.prepare();
+            if(!_permuted_weights.is_used())
+            {
+                _permuted_weights.allocator()->free();
+            }
+        }
+
+        _is_prepared = true;
+    }
 }
 
 NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayer()
@@ -542,3 +625,4 @@ void NEDepthwiseConvolutionLayer::prepare()
         _is_prepared = true;
     }
 }
+} // namespace arm_compute
diff --git a/src/runtime/NEON/functions/assembly/NEDepthwiseConvolutionAssemblyDispatch.cpp b/src/runtime/NEON/functions/assembly/NEDepthwiseConvolutionAssemblyDispatch.cpp
new file mode 100644
index 0000000000..a64426500f
--- /dev/null
+++ b/src/runtime/NEON/functions/assembly/NEDepthwiseConvolutionAssemblyDispatch.cpp
@@ -0,0 +1,356 @@
+/*
+ * 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/NEON/functions/assembly/NEDepthwiseConvolutionAssemblyDispatch.h"
+
+#include "arm_compute/core/CPP/Validate.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/kernels/convolution/depthwise/depthwise_quantized.hpp"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/utils/misc/InfoHelpers.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
+
+#include "arm_compute/runtime/NEON/NEScheduler.h"
+
+namespace arm_compute
+{
+namespace
+{
+std::unique_ptr<depthwise::IDepthwiseConvolution> create_convolver(const ITensor      *input,
+                                                                   const ITensor      *weights,
+                                                                   ITensor            *output,
+                                                                   PadStrideInfo       conv_info,
+                                                                   ActivationLayerInfo act_info)
+{
+    const DataType    data_type = input->info()->data_type();
+    const TensorShape shape     = input->info()->tensor_shape();
+
+    const int n_batches      = shape[3];
+    const int in_rows        = shape.z();
+    const int in_cols        = shape.y();
+    const int n_channels     = shape.x();
+    const int padding_top    = conv_info.pad_top();
+    const int padding_left   = conv_info.pad_left();
+    const int padding_bottom = conv_info.pad_bottom();
+    const int padding_right  = conv_info.pad_right();
+
+    const unsigned int stride_x = conv_info.stride().first;
+
+    // Map activation function
+    neon_convolution_kernels::ActivationFunction activation = neon_convolution_kernels::ActivationFunction::None;
+    if(arm_compute::utils::info_helpers::is_relu(act_info))
+    {
+        activation = neon_convolution_kernels::ActivationFunction::ReLU;
+    }
+    else if(arm_compute::utils::info_helpers::is_relu6(act_info))
+    {
+        activation = neon_convolution_kernels::ActivationFunction::ReLU6;
+    }
+
+    // Create quantized convolver
+    if(data_type == DataType::QASYMM8)
+    {
+        const QuantizationInfo &input_qinfo   = input->info()->quantization_info();
+        const QuantizationInfo &weights_qinfo = weights->info()->quantization_info();
+        const QuantizationInfo &output_qinfo  = output->info()->quantization_info();
+
+        // Check that quantization info are in the range [0, 255]
+        ARM_COMPUTE_ERROR_ON(input_qinfo.offset < 0 || input_qinfo.offset > 255);
+        ARM_COMPUTE_ERROR_ON(weights_qinfo.offset < 0 || weights_qinfo.offset > 255);
+        ARM_COMPUTE_ERROR_ON(output_qinfo.offset < 0 || output_qinfo.offset > 255);
+        const qasymm8::QAsymm8Params iqinfo{ static_cast<uint8_t>(input_qinfo.offset), input_qinfo.scale };
+        const qasymm8::QAsymm8Params wqinfo{ static_cast<uint8_t>(weights_qinfo.offset), weights_qinfo.scale };
+        const qasymm8::QAsymm8Params oqinfo{ static_cast<uint8_t>(output_qinfo.offset), output_qinfo.scale };
+
+        // Calculate rescale parameters
+        const float fmultipler  = iqinfo.scale * wqinfo.scale / oqinfo.scale;
+        int         qmultiplier = 0;
+        int         qshift      = 0;
+        quantization::calculate_quantized_multiplier_less_than_one(fmultipler, &qmultiplier, &qshift);
+        qasymm8::QAsymm8RescaleParams rescale_params(qshift, qmultiplier, fmultipler);
+
+        // Create convolver
+        switch(stride_x)
+        {
+            case 1:
+                return arm_compute::support::cpp14::make_unique<depthwise::QAsymm8DepthwiseConvolution<2, 2, 3, 3, 1, 1>>(
+                           n_batches, in_rows, in_cols, n_channels, activation, wqinfo, iqinfo, oqinfo, rescale_params, padding_top, padding_left, padding_bottom, padding_right);
+            case 2:
+                return arm_compute::support::cpp14::make_unique<depthwise::QAsymm8DepthwiseConvolution<2, 2, 3, 3, 2, 2>>(
+                           n_batches, in_rows, in_cols, n_channels, activation, wqinfo, iqinfo, oqinfo, rescale_params, padding_top, padding_left, padding_bottom, padding_right);
+            default:
+                return nullptr;
+        }
+    }
+    else
+    {
+        // Create float convolver
+        switch(data_type)
+        {
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+            case DataType::F16:
+            {
+                switch(stride_x)
+                {
+                    case 1:
+                        return arm_compute::support::cpp14::make_unique<depthwise::DepthwiseConvolution<3, 3, 3, 3, 1, 1, float16_t, float16_t, float16_t>>(
+                                   n_batches, in_rows, in_cols, n_channels, activation, padding_top, padding_left, padding_bottom, padding_right);
+                    case 2:
+                        return arm_compute::support::cpp14::make_unique<depthwise::DepthwiseConvolution<3, 3, 3, 3, 2, 2, float16_t, float16_t, float16_t>>(
+                                   n_batches, in_rows, in_cols, n_channels, activation, padding_top, padding_left, padding_bottom, padding_right);
+                    default:
+                        return nullptr;
+                }
+                break;
+            }
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+            case DataType::F32:
+            {
+                switch(stride_x)
+                {
+                    case 1:
+                        return arm_compute::support::cpp14::make_unique<depthwise::DepthwiseConvolution<4, 4, 3, 3, 1, 1, float, float, float>>(
+                                   n_batches, in_rows, in_cols, n_channels, activation, padding_top, padding_left, padding_bottom, padding_right);
+                    case 2:
+                        return arm_compute::support::cpp14::make_unique<depthwise::DepthwiseConvolution<3, 3, 3, 3, 2, 2, float, float, float>>(
+                                   n_batches, in_rows, in_cols, n_channels, activation, padding_top, padding_left, padding_bottom, padding_right);
+                    default:
+                        return nullptr;
+                }
+                break;
+            }
+            default:
+                return nullptr;
+        }
+    }
+}
+} // namespace
+
+#ifndef DOXYGEN_SKIP_THIS
+NEDepthwiseConvolutionAssemblyDispatch::NEDepthwiseConvolutionAssemblyDispatch(std::shared_ptr<arm_compute::IMemoryManager> memory_manager)
+    : _memory_group(std::move(memory_manager)), _input(nullptr), _weights(nullptr), _bias(nullptr), _output(nullptr), _packed_weights(), _workspace(), _is_prepared(false), _dwc_assembly_kernel(nullptr),
+      _dwc_acl_kernel()
+{
+}
+#endif /* DOXYGEN_SKIP_THIS */
+
+void NEDepthwiseConvolutionAssemblyDispatch::configure(const ITensor             *input,
+                                                       const ITensor             *weights,
+                                                       const ITensor             *bias,
+                                                       ITensor                   *output,
+                                                       const PadStrideInfo       &conv_info,
+                                                       unsigned int               depth_multiplier,
+                                                       const ActivationLayerInfo &act_info)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
+    ARM_COMPUTE_UNUSED(depth_multiplier);
+    ARM_COMPUTE_ERROR_THROW_ON(NEDepthwiseConvolutionAssemblyDispatch::validate(input->info(),
+                                                                                weights->info(),
+                                                                                bias != nullptr ? bias->info() : nullptr,
+                                                                                output->info(),
+                                                                                conv_info,
+                                                                                depth_multiplier,
+                                                                                act_info));
+
+    // Output auto inizialitation if not yet initialized
+    const TensorShape output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(*input->info(), *weights->info(), conv_info, depth_multiplier);
+    auto_init_if_empty(*output->info(), input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape));
+
+    _input       = input;
+    _weights     = weights;
+    _bias        = bias;
+    _output      = output;
+    _is_prepared = false;
+
+    // Create convolver
+    _dwc_assembly_kernel = create_convolver(input, weights, output, conv_info, act_info);
+    ARM_COMPUTE_ERROR_ON(_dwc_assembly_kernel == nullptr);
+
+    // Create assembly kernel wrapper
+    _dwc_acl_kernel.configure(_dwc_assembly_kernel.get());
+
+    constexpr size_t alignment = 128;
+
+    // Create workspace
+    const unsigned int num_threads    = NEScheduler::get().num_threads();
+    const size_t       workspace_size = _dwc_assembly_kernel->get_working_space_size(num_threads);
+    ARM_COMPUTE_ERROR_ON_MSG(workspace_size == 0, "Workspace size cannot be 0 !");
+    _workspace.allocator()->init(TensorInfo(TensorShape{ workspace_size }, 1, DataType::S8), alignment);
+    _memory_group.manage(&_workspace);
+    _workspace.allocator()->allocate();
+
+    // Create packing tensor
+    const size_t pack_tensor_size = _dwc_assembly_kernel->get_packed_params_size();
+    ARM_COMPUTE_ERROR_ON_MSG(pack_tensor_size == 0, "Pack tensor size cannot be 0 !");
+    _packed_weights.allocator()->init(TensorInfo(TensorShape{ pack_tensor_size }, 1, DataType::S8), alignment);
+}
+
+Status NEDepthwiseConvolutionAssemblyDispatch::validate(const ITensorInfo         *input,
+                                                        const ITensorInfo         *weights,
+                                                        const ITensorInfo         *bias,
+                                                        const ITensorInfo         *output,
+                                                        const PadStrideInfo       &conv_info,
+                                                        unsigned int               depth_multiplier,
+                                                        const ActivationLayerInfo &act_info)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
+    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 auto       strides     = conv_info.stride();
+    const DataLayout data_layout = input->data_layout();
+    unsigned int     width_idx   = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+    unsigned int     height_idx  = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(width_idx) != 3 || weights->dimension(height_idx) != 3);
+    ARM_COMPUTE_RETURN_ERROR_ON(!((strides.first == strides.second) && ((strides.first == 1) || (strides.first == 2))));
+    ARM_COMPUTE_RETURN_ERROR_ON(depth_multiplier != 1);
+
+    const bool is_relu  = arm_compute::utils::info_helpers::is_relu(act_info);
+    const bool is_relu6 = arm_compute::utils::info_helpers::is_relu6(act_info);
+    ARM_COMPUTE_RETURN_ERROR_ON(act_info.enabled() && !(is_relu || is_relu6));
+
+    // Check bias
+    if(bias != nullptr)
+    {
+        unsigned int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
+        ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
+        ARM_COMPUTE_RETURN_ERROR_ON(bias->dimension(0) != weights->dimension(channel_idx));
+    }
+
+    // Check output
+    if(output->total_size() != 0)
+    {
+        const TensorShape output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+    }
+
+    return Status{};
+}
+
+bool NEDepthwiseConvolutionAssemblyDispatch::is_optimized_supported(const ITensorInfo *input,
+                                                                    const ITensorInfo *weights,
+                                                                    PadStrideInfo      conv_info,
+                                                                    unsigned int       depth_multiplier)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights);
+
+    // Reshape input shape if in NHWC format
+    const DataLayout data_layout = input->data_layout();
+    TensorShape      in_shape{ input->tensor_shape() };
+    if(data_layout == DataLayout::NHWC)
+    {
+        in_shape.set(Window::DimX, input->tensor_shape().y());
+        in_shape.set(Window::DimY, input->tensor_shape().z());
+        in_shape.set(Window::DimZ, input->tensor_shape().x());
+    }
+
+    // Check data type
+    const DataType data_type          = weights->data_type();
+    bool           is_data_type_valid = is_data_type_float(data_type) || is_data_type_quantized_asymmetric(data_type);
+
+    // Check weighs size
+    const unsigned int width_idx         = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+    const unsigned int height_idx        = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+    bool               weights_supported = (weights->dimension(width_idx) == 3) && (weights->dimension(height_idx) == 3);
+
+    // Check for supported strides
+    const auto &strides           = conv_info.stride();
+    bool        supported_strides = (strides.first == strides.second) && ((strides.first == 1) || (strides.first == 2));
+
+    // Check for supported padding
+    const auto    pad_top           = conv_info.pad_top();
+    const auto    pad_right         = conv_info.pad_right();
+    const auto    pad_bottom        = conv_info.pad_bottom();
+    const auto    pad_left          = conv_info.pad_left();
+    PadStrideInfo same_pad          = calculate_same_pad(in_shape, TensorShape(3U, 3U), conv_info);
+    bool          is_same_padding   = (pad_top == same_pad.pad_top()) && (pad_right == same_pad.pad_right()) && (pad_bottom == same_pad.pad_bottom()) && (pad_left == same_pad.pad_left());
+    bool          is_valid_padding  = (pad_top == 0) && (pad_right == 0) && (pad_bottom == 0) && (pad_left == 0);
+    bool          supported_padding = is_same_padding || is_valid_padding;
+
+    return is_data_type_valid && weights_supported && supported_strides && supported_padding && (depth_multiplier == 1);
+}
+
+void NEDepthwiseConvolutionAssemblyDispatch::run()
+{
+    // Prepare assembly kernel
+    prepare();
+
+    _memory_group.acquire();
+
+    // Setup inputs/outputs
+    ARM_COMPUTE_ERROR_ON(_workspace.buffer() == nullptr);
+    _dwc_assembly_kernel->set_working_space(static_cast<void *>(_workspace.buffer()));
+
+    ARM_COMPUTE_ERROR_ON(_input->buffer() == nullptr);
+    const int   input_element_size = _input->info()->element_size();
+    const int   input_batch_stride = _input->info()->strides_in_bytes()[3] / input_element_size;
+    const int   input_row_stride   = _input->info()->strides_in_bytes().z() / input_element_size;
+    const int   input_col_stride   = _input->info()->strides_in_bytes().y() / input_element_size;
+    const void *input_ptr          = _input->buffer() + _input->info()->offset_first_element_in_bytes();
+    _dwc_assembly_kernel->set_input(input_ptr, input_batch_stride, input_row_stride, input_col_stride);
+
+    ARM_COMPUTE_ERROR_ON(_output->buffer() == nullptr);
+    const int output_element_size = _output->info()->element_size();
+    const int output_batch_stride = _output->info()->strides_in_bytes()[3] / output_element_size;
+    const int output_row_stride   = _output->info()->strides_in_bytes().z() / output_element_size;
+    const int output_col_stride   = _output->info()->strides_in_bytes().y() / output_element_size;
+    void     *output_ptr          = _output->buffer() + _output->info()->offset_first_element_in_bytes();
+    _dwc_assembly_kernel->set_output(output_ptr, output_batch_stride, output_row_stride, output_col_stride);
+
+    // Schedule assembly kernel
+    NEScheduler::get().schedule(&_dwc_acl_kernel, Window::DimX);
+
+    _memory_group.release();
+}
+
+void NEDepthwiseConvolutionAssemblyDispatch::prepare()
+{
+    if(!_is_prepared)
+    {
+        _packed_weights.allocator()->allocate();
+        ARM_COMPUTE_ERROR_ON(_packed_weights.buffer() == nullptr);
+
+        // Pack weights and bias
+        const int weights_element_size = _weights->info()->element_size();
+        const int weights_row_stride   = _weights->info()->strides_in_bytes().z() / weights_element_size;
+        const int weights_col_stride   = _weights->info()->strides_in_bytes().y() / weights_element_size;
+        _dwc_assembly_kernel->pack_params(_packed_weights.buffer(),
+                                          _weights->buffer() + _weights->info()->offset_first_element_in_bytes(),
+                                          weights_row_stride,
+                                          weights_col_stride,
+                                          (_bias != nullptr) ? _bias->buffer() : nullptr);
+        _dwc_assembly_kernel->set_packed_params_buffer(_packed_weights.buffer());
+
+        _weights->mark_as_unused();
+        if(_bias != nullptr)
+        {
+            _bias->mark_as_unused();
+        }
+        _is_prepared = true;
+    }
+}
+} // namespace arm_compute