diff --git a/src/runtime/NEON/functions/NEConvolutionLayer.cpp b/src/runtime/NEON/functions/NEConvolutionLayer.cpp
index 8f7d940..bb685c6 100644
--- a/src/runtime/NEON/functions/NEConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEConvolutionLayer.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -29,6 +29,7 @@
 #include "arm_compute/core/Size2D.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
 #include "arm_compute/runtime/NEON/NEScheduler.h"
 #include "support/ToolchainSupport.h"
 
@@ -46,10 +47,10 @@
 {
 namespace
 {
-TensorShape get_reshaped_weights_shape(const ITensorInfo *weights, bool has_bias)
+TensorShape get_reshaped_weights_shape(const 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) + (has_bias ? 1 : 0);
+    const unsigned int mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (append_bias ? 1 : 0);
     return TensorShape(mat_weights_cols, mat_weights_rows);
 }
 } // namespace
@@ -69,14 +70,16 @@
                                                                           transpose1xW));
 
     // Check if bias are present, if yes they will be embedded to the weights matrix
-    const bool _has_bias = (biases != nullptr);
+    const bool append_biases = (biases != nullptr) && !is_data_type_quantized_asymmetric(weights->info()->data_type());
+    //const unsigned bias_element  = (append_biases) ? 1 : 0;
+    const ITensor *biases_to_use = (append_biases) ? biases : nullptr;
 
     _transpose1xW = transpose1xW;
 
     if(transpose1xW)
     {
         // Create tensor to store the reshaped weights
-        TensorInfo info_wr = weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(get_reshaped_weights_shape(weights->info(), _has_bias));
+        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);
@@ -88,30 +91,35 @@
     }
     else
     {
-        _weights_reshape_kernel.configure(weights, biases, 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)
 {
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
-
-    if(biases != nullptr)
+    if(!is_data_type_quantized_asymmetric(weights->data_type()))
     {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output);
+    }
+    // Check if bias are present, if yes they will be embedded to the weights matrix
+    const bool append_bias = (biases != nullptr);
+
+    if(append_bias)
+    {
+        ARM_COMPUTE_ERROR_ON(is_data_type_quantized_asymmetric(weights->data_type()));
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(weights, biases);
         ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
         ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
     }
 
-    // Check if bias are present, if yes they will be embedded to the weights matrix
-    const bool has_bias = (biases != nullptr);
-
     // Checks performed when biases are present
-    if(has_bias)
+    if(append_bias)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
         ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
@@ -120,7 +128,7 @@
 
     if(transpose1xW)
     {
-        TensorInfo weights_reshaped = weights->clone()->set_tensor_shape(get_reshaped_weights_shape(weights, has_bias));
+        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));
     }
@@ -148,10 +156,10 @@
 
 namespace
 {
-TensorShape get_reshaped_weights_shape_conv(const ITensorInfo *weights, bool has_bias, bool is_fully_connected_convolution)
+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) + (has_bias ? 1 : 0);
+    unsigned int mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (append_bias ? 1 : 0);
 
     if(is_fully_connected_convolution)
     {
@@ -167,47 +175,86 @@
 }
 
 Status validate_and_initialize_values(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, DataType &dt,
-                                      bool &has_bias,
-                                      bool &are_weights_reshaped, unsigned int &kernel_width, unsigned int &kernel_height, bool &is_fully_connected_convolution, unsigned int &mat_weights_cols, unsigned int &mat_weights_rows,
+                                      bool &append_bias,
+                                      bool &are_weights_reshaped, unsigned int &kernel_width, unsigned int &kernel_height,
+                                      bool &is_fully_connected_convolution, bool &is_interleaved_transposed, bool &is_quantized,
+                                      unsigned int &mat_weights_cols, unsigned int &mat_weights_rows,
                                       unsigned int &conv_w, unsigned int &conv_h)
 {
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights);
     ARM_COMPUTE_RETURN_ERROR_ON(!weights_info.are_reshaped() && weights->dimension(2) != input->dimension(2));
     ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
+    ARM_COMPUTE_ERROR_ON(weights_info.are_reshaped() && is_data_type_quantized_asymmetric(input->data_type()));
+
+    dt           = input->data_type();
+    is_quantized = is_data_type_quantized_asymmetric(dt);
 
     if(biases != nullptr)
     {
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
+        if(is_quantized)
+        {
+            ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
+        }
+        else
+        {
+            ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
+        }
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases);
         ARM_COMPUTE_RETURN_ERROR_ON(!weights_info.are_reshaped() && biases->dimension(0) != weights->dimension(3));
         ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
     }
 
-    dt                   = input->data_type();
-    has_bias             = (biases != nullptr);
+    append_bias          = (biases != nullptr) && (!is_quantized);
     are_weights_reshaped = weights_info.are_reshaped();
     kernel_width         = (are_weights_reshaped) ? weights_info.kernel_size().first : weights->dimension(0);
     kernel_height        = (are_weights_reshaped) ? weights_info.kernel_size().second : weights->dimension(1);
     mat_weights_cols     = weights->dimension(3);
-    mat_weights_rows     = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (has_bias ? 1 : 0);
+    mat_weights_rows     = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (append_bias ? 1 : 0);
 
     std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height,
                                                  conv_info);
 
+    // Check if its a "fully connected" convolution
     is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
+    is_interleaved_transposed      = (!is_fully_connected_convolution && !is_quantized);
 
     return Status{};
 }
 } // namespace
 
 NEConvolutionLayer::NEConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
-    : _memory_group(std::move(memory_manager)), _input_im2col_kernel(), _input_interleave_kernel(), _reshape_weights(), _mm_kernel(), _mm_optimised_kernel(nullptr), _output_col2im_kernel(),
-      _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _gemm_output(), _workspace(), _has_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false)
+    : _memory_group(std::move(memory_manager)), _input_im2col_kernel(), _input_interleave_kernel(), _reshape_weights(), _mm_kernel(), _mm_optimised_kernel(nullptr), _mm_gemmlowp(memory_manager),
+      _gemmlowp_output_stage(), _output_col2im_kernel(), _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _gemm_output(), _tmp_output(), _workspace(), _append_bias(false),
+      _is_fully_connected_convolution(false), _are_weights_reshaped(false), _is_quantized(false), _is_interleaved_transposed(false)
 {
 }
 
+void NEConvolutionLayer::configure_mm(const ITensor *input, const ITensor *weights, ITensor *output)
+{
+    if(_is_quantized)
+    {
+        // Since we need negative offsets for computing convolution, we need to change QuantizationInfo()
+        // Extract and negate input and weights offset
+        const QuantizationInfo input_quantization_info   = input->info()->quantization_info();
+        const QuantizationInfo weights_quantization_info = weights->info()->quantization_info();
+
+        input->info()->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset));
+        weights->info()->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset));
+
+        _mm_gemmlowp.configure(input, weights, output, GEMMInfo(false, false, true /* Reshape weights only for the first run*/));
+
+        // Revert back QuantizatioInfo as input and weights could be used in other convolution layers
+        input->info()->set_quantization_info(input_quantization_info);
+        weights->info()->set_quantization_info(weights_quantization_info);
+    }
+    else
+    {
+        _mm_kernel.configure(input, weights, output, 1.f);
+    }
+}
+
 void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
 {
     // Perform validate step
@@ -221,14 +268,15 @@
     unsigned int conv_w           = 0;
     unsigned int conv_h           = 0;
 
-    Status status = validate_and_initialize_values(input->info(), weights->info(), (biases == nullptr) ? nullptr : biases->info(), conv_info, weights_info, dt, _has_bias, _are_weights_reshaped,
+    Status status = validate_and_initialize_values(input->info(), weights->info(), (biases == nullptr) ? nullptr : biases->info(), conv_info, weights_info, dt, _append_bias, _are_weights_reshaped,
                                                    kernel_width, kernel_height,
-                                                   _is_fully_connected_convolution,
+                                                   _is_fully_connected_convolution, _is_interleaved_transposed, _is_quantized,
                                                    mat_weights_cols, mat_weights_rows, conv_w, conv_h);
 
     ARM_COMPUTE_ERROR_THROW_ON(status);
 
     const unsigned int fixed_point_position = input->info()->fixed_point_position();
+    const ITensor     *biases_to_use        = (_append_bias) ? biases : nullptr;
 
 #if defined(__arm__)
     if(NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && dt == DataType::F32)
@@ -264,7 +312,7 @@
     {
         if(_are_weights_reshaped)
         {
-            if(_is_fully_connected_convolution)
+            if(_is_fully_connected_convolution || _is_quantized)
             {
                 mat_weights_cols = weights_info.num_kernels();
                 mat_weights_rows = weights->info()->dimension(1);
@@ -273,14 +321,14 @@
             {
                 const unsigned int transpose_width = 16 / input->info()->element_size();
                 mat_weights_cols                   = weights_info.num_kernels();
-                mat_weights_rows                   = weights->info()->dimension(0) / transpose_width + (_has_bias ? 1 : 0);
+                mat_weights_rows                   = weights->info()->dimension(0) / transpose_width + (_append_bias ? 1 : 0);
             }
         }
         else
         {
             TensorShape reshaped_weights_shape;
 
-            if(_is_fully_connected_convolution)
+            if(_is_fully_connected_convolution || _is_quantized)
             {
                 reshaped_weights_shape = TensorShape{ mat_weights_cols, mat_weights_rows };
             }
@@ -294,7 +342,7 @@
 
             // Create tensor to store the reshaped weights
             _weights_reshaped.allocator()->init(TensorInfo(reshaped_weights_shape, 1, dt, fixed_point_position));
-            _reshape_weights.configure(weights, biases, &_weights_reshaped, !_is_fully_connected_convolution /* 1xW transpose */);
+            _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, _is_interleaved_transposed /* 1xW transpose */);
             weights = &_weights_reshaped;
         }
     }
@@ -324,12 +372,18 @@
     TensorShape shape_gemm(_input_im2col_reshaped.info()->tensor_shape());
     shape_gemm.set(0, mat_weights_cols);
     shape_gemm.set(1, mat_input_rows);
-    _gemm_output.allocator()->init(_input_im2col_reshaped.info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_gemm));
+    const DataType gemm_data_type = _is_quantized ? DataType::S32 : dt;
+    // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input.
+    TensorInfo info_gemm(shape_gemm, 1, gemm_data_type, input->info()->fixed_point_position());
+    info_gemm.set_quantization_info(output->info()->quantization_info());
+    _gemm_output.allocator()->init(info_gemm);
     _memory_group.manage(&_gemm_output);
 
     // Configure kernels
-    _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _has_bias);
+    // Configure im2col
+    _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias);
 
+    // Configure matrix multiply
 #if defined(__arm__) || defined(__aarch64__)
     if(_mm_optimised_kernel != nullptr)
     {
@@ -357,22 +411,44 @@
     else
 #endif /* defined(__arm__) || defined(__aarch64__) */
     {
-        if(_is_fully_connected_convolution)
+        if(_is_interleaved_transposed)
         {
-            _mm_kernel.configure(&_input_im2col_reshaped, weights, &_gemm_output, 1.0f);
+            // Configure GEMMInterleave4x4. _input_interleaved_reshaped will be auto configured in the kernel
+            _memory_group.manage(&_input_interleaved_reshaped);
+            _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
+
+            // Configure GEMM
+            configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output);
+            _input_interleaved_reshaped.allocator()->allocate();
         }
         else
         {
-            _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
-            _mm_kernel.configure(&_input_interleaved_reshaped, weights, &_gemm_output, 1.0f);
-            _input_interleaved_reshaped.allocator()->allocate();
+            configure_mm(&_input_im2col_reshaped, weights, &_gemm_output);
         }
     }
 
     _input_im2col_reshaped.allocator()->allocate();
-    _output_col2im_kernel.configure(&_gemm_output, output, Size2D(conv_w, conv_h));
+
+    // Configure output stage for quantized case
+    if(_is_quantized)
+    {
+        float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output->info()->quantization_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->info()->quantization_info().offset);
+    }
+
+    // Configure Col2Im
+    _output_col2im_kernel.configure(_is_quantized ? &_tmp_output : &_gemm_output, output, Size2D(conv_w, conv_h));
+    if(_is_quantized)
+    {
+        _tmp_output.allocator()->allocate();
+    }
     _gemm_output.allocator()->allocate();
 
+    ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) || (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one");
+
     // Allocate intermediate tensor
     if(!_are_weights_reshaped)
     {
@@ -384,9 +460,11 @@
                                     const WeightsInfo &weights_info)
 {
     DataType     dt{};
-    bool         has_bias{};
+    bool         append_bias{};
     bool         are_weights_reshaped{};
     bool         is_fully_connected_convolution{};
+    bool         is_interleaved_transposed{};
+    bool         is_quantized{};
     unsigned int kernel_width     = 0;
     unsigned int kernel_height    = 0;
     unsigned int mat_weights_cols = 0;
@@ -394,8 +472,8 @@
     unsigned int conv_w           = 0;
     unsigned int conv_h           = 0;
 
-    Status status = validate_and_initialize_values(input, weights, biases, conv_info, weights_info, dt, has_bias, are_weights_reshaped, kernel_width, kernel_height,
-                                                   is_fully_connected_convolution, mat_weights_cols, mat_weights_rows,
+    Status status = validate_and_initialize_values(input, weights, biases, conv_info, weights_info, dt, append_bias, are_weights_reshaped, kernel_width, kernel_height,
+                                                   is_fully_connected_convolution, is_interleaved_transposed, is_quantized, mat_weights_cols, mat_weights_rows,
                                                    conv_w, conv_h);
 
     ARM_COMPUTE_RETURN_ON_ERROR(status);
@@ -428,7 +506,7 @@
             TensorShape reshaped_weights_shape{ mat_weights_cols, mat_weights_rows };
 
             // Create tensor to store the reshaped weights
-            reshaped_weights->set_tensor_shape(get_reshaped_weights_shape_conv(weights, has_bias, is_fully_connected_convolution));
+            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();
         }
@@ -439,13 +517,13 @@
         {
             const unsigned int transpose_width = 16 / input->element_size();
             mat_weights_cols                   = weights_info.num_kernels();
-            mat_weights_rows                   = weights->dimension(0) / transpose_width + (has_bias ? 1 : 0);
+            mat_weights_rows                   = weights->dimension(0) / transpose_width + (append_bias ? 1 : 0);
         }
         else
         {
             TensorShape reshaped_weights_shape;
 
-            if(is_fully_connected_convolution)
+            if(is_fully_connected_convolution || is_quantized)
             {
                 reshaped_weights_shape = TensorShape{ mat_weights_cols, mat_weights_rows };
             }
@@ -458,7 +536,7 @@
             }
 
             // Create tensor to store the reshaped weights
-            reshaped_weights->set_tensor_shape(get_reshaped_weights_shape_conv(weights, has_bias, is_fully_connected_convolution));
+            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();
         }
@@ -472,7 +550,7 @@
     shape_im2col.set(1, mat_input_rows);
     shape_im2col.set(2, 1);
     TensorInfo im2_col_info = input->clone()->set_tensor_shape(shape_im2col);
-    ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, &im2_col_info, Size2D(weights->dimension(0), weights->dimension(1)), conv_info, has_bias));
+    ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, &im2_col_info, Size2D(weights->dimension(0), weights->dimension(1)), conv_info, append_bias));
 
     // Create GEMM output tensor
     TensorShape shape_gemm(im2_col_info.tensor_shape());
@@ -481,7 +559,7 @@
     TensorInfo gemm_output_info = input->clone()->set_tensor_shape(shape_gemm);
 
     // Validate GEMM interleave and multiply
-    if(!is_fully_connected_convolution)
+    if(is_interleaved_transposed)
     {
         TensorShape shape_interleaved = shape_im2col;
         shape_interleaved.set(0, shape_interleaved.x() * 4);
@@ -523,13 +601,27 @@
     }
     else
     {
-        if(!_is_fully_connected_convolution)
+        if(_is_interleaved_transposed)
         {
             // Run interleave
             NEScheduler::get().schedule(&_input_interleave_kernel, Window::DimY);
         }
 
-        NEScheduler::get().schedule(&_mm_kernel, Window::DimY);
+        // Runs matrix multiply on reshaped matrices
+        if(_is_quantized)
+        {
+            _mm_gemmlowp.run();
+        }
+        else
+        {
+            NEScheduler::get().schedule(&_mm_kernel, Window::DimY);
+        }
+    }
+
+    // Run output stage for quantized case
+    if(_is_quantized)
+    {
+        _gemmlowp_output_stage.run();
     }
 
     // Reshape output matrix