COMPMID-1751: Remove output_3d_depth from NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint

Change-Id: I1d5bc4d24059917f9ddef0873dd3043b1f2320a8

8 files changed, 303 insertions, 255 deletions

diff --git a/arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h b/arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h
index 8412fa229f..15d09e27ff 100644
--- a/arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h
+++ b/arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h
@@ -72,24 +72,21 @@ public:
      * @param[in]  min                          (Optional) Min value used to saturate down the output result before converting back to QASYMM8
      * @param[in]  max                          (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
      *                                          Along with @p min, this value can be used to implement "rectified linear unit" activation functions
-     * @param[in]  output_3d_depth              (Optional) Depth of output in 3D (Defaults to 1)
      */
-    void configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift,
-                   int min = 0, int max = 0, unsigned int output_3d_depth = 1);
+    void configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift, int min = 0, int max = 0);
     /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel
      *
-     * @param[in] input           Input tensor. Data type supported: S32
-     * @param[in] bias            Biases tensor. Only shared biases supported and it can be a nullptr if the biases addition is not required.
-     *                            Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
-     * @param[in] output          Output tensor. Data type supported: Data type supported: QASYMM8
-     * @param[in] min             (Optional) Min value used to saturate down the output result before converting back to QASYMM8
-     * @param[in] max             (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
+     * @param[in] input  Input tensor. Data type supported: S32
+     * @param[in] bias   Biases tensor. Only shared biases supported and it can be a nullptr if the biases addition is not required.
+     *                   Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
+     * @param[in] output Output tensor. Data type supported: Data type supported: QASYMM8
+     * @param[in] min    (Optional) Min value used to saturate down the output result before converting back to QASYMM8
+     * @param[in] max    (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
      *                            Along with @p min, this value can be used to implement "rectified linear unit" activation functions
-     * @param[in] output_3d_depth (Optional)  Depth of output in 3D (Defaults to 1)
      *
      * @return a status
      */
-    static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = 0, int max = 0, unsigned int output_3d_depth = 1);
+    static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = 0, int max = 0);
 
     // Inherited methods overridden:
     void run(const Window &window, const ThreadInfo &info) override;
@@ -117,7 +114,6 @@ private:
     int                     _result_offset_after_shift;
     int                     _min;
     int                     _max;
-    unsigned int            _output_3d_depth;
 };
 } // namespace arm_compute
 #endif /* __ARM_COMPUTE_NEGEMMLOWPQUANTIZEDOWNINT32TOUINT8SCALEBYFIXEDPOINTKERNEL_H__ */
diff --git a/arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h b/arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h
index f38ecdbbd2..53b91b35b6 100644
--- a/arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h
+++ b/arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h
@@ -131,24 +131,21 @@ public:
      * @param[in]  min                          (Optional) Min value used to saturate down the output result before converting back to QASYMM8
      * @param[in]  max                          (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
      *                                          Along with @p min, this value can be used to implement "rectified linear unit" activation functions
-     * @param[in]  output_3d_depth              (Optional) Depth of output in 3D (Defaults to 1)
      */
-    void configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift,
-                   int min = 0, int max = 0, unsigned int output_3d_depth = 1);
+    void configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift, int min = 0, int max = 0);
     /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint
      *
-     * @param[in] input           Input tensor. It is the output of @ref NEGEMMLowpMatrixMultiplyCore function. Data type supported: S32
-     * @param[in] bias            Biases tensor. Only shared biases supported and it can be a nullptr if the addition of biases is not required.
-     *                            Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
-     * @param[in] output          Output tensor. Data type supported: Data type supported: QASYMM8
-     * @param[in] min             (Optional) Min value used to saturate down the output result before converting back to QASYMM8
-     * @param[in] max             (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
+     * @param[in] input  Input tensor. It is the output of @ref NEGEMMLowpMatrixMultiplyCore function. Data type supported: S32
+     * @param[in] bias   Biases tensor. Only shared biases supported and it can be a nullptr if the addition of biases is not required.
+     *                   Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
+     * @param[in] output Output tensor. Data type supported: Data type supported: QASYMM8
+     * @param[in] min    (Optional) Min value used to saturate down the output result before converting back to QASYMM8
+     * @param[in] max    (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
      *                            Along with @p min, this value can be used to implement "rectified linear unit" activation functions
-     * @param[in] output_3d_depth (Optional) Depth of output in 3D (Defaults to 1)
      *
      * @return a status
      */
-    static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = 0, int max = 0, unsigned int output_3d_depth = 1);
+    static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = 0, int max = 0);
 };
 } // namespace arm_compute
 #endif /*__ARM_COMPUTE_NEGEMMLOWPOUTPUTSTAGE_H__ */
\ No newline at end of file
diff --git a/src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.cpp
index af84d024d5..33a5b4ace3 100644
--- a/src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.cpp
+++ b/src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.cpp
@@ -62,16 +62,24 @@ Status validate_arguments(const ITensorInfo *mm_result, const ITensorInfo *vecto
     if(b_offset != 0)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_row, 1, DataType::S32);
-        ARM_COMPUTE_RETURN_ERROR_ON(vector_sum_row->dimension(0) != mm_result->dimension(1));
+
+        // Check if input is a 3D reinterpretation
+        const bool reinterpret_as_3d = mm_result->num_dimensions() > 1 && mm_result->tensor_shape().y() != vector_sum_row->tensor_shape().x();
+
+        // Validate input
+        ARM_COMPUTE_RETURN_ERROR_ON(reinterpret_as_3d && vector_sum_row->dimension(0) != (mm_result->dimension(1) * mm_result->dimension(2)));
+        ARM_COMPUTE_RETURN_ERROR_ON(!reinterpret_as_3d && vector_sum_row->dimension(0) != mm_result->dimension(1));
 
         TensorShape output_shape = mm_result->tensor_shape();
         if(output_shape.num_dimensions() > 1)
         {
+            const unsigned int output_batch_idx = reinterpret_as_3d ? 3 : 2;
+
             TensorShape vector_sum_row_shape = vector_sum_row->tensor_shape();
             vector_sum_row_shape.collapse_from(1);
-            output_shape.collapse_from(2);
+            output_shape.collapse_from(output_batch_idx);
 
-            ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_row_shape[1] != output_shape[2],
+            ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_row_shape[1] != output_shape[output_batch_idx],
                                             "mm_result tensor must have the same number of batches of output tensor");
 
             if(a_offset != 0)
@@ -117,77 +125,23 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *mm_result,
     Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
     return std::make_pair(err, win);
 }
-} // namespace
-
-NEGEMMLowpOffsetContributionKernel::NEGEMMLowpOffsetContributionKernel()
-    : _vector_sum_col(nullptr), _vector_sum_row(nullptr), _mm_result(nullptr), _a_offset(0), _b_offset(0), _k_offset(0), _slide_vector_sum_col(true)
-{
-}
-
-void NEGEMMLowpOffsetContributionKernel::configure(ITensor *mm_result, const ITensor *vector_sum_col, const ITensor *vector_sum_row, int32_t k, int32_t a_offset, int32_t b_offset)
-{
-    // Perform validate step
-    ARM_COMPUTE_ERROR_ON_NULLPTR(mm_result);
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(mm_result->info(),
-                                                  vector_sum_col != nullptr ? vector_sum_col->info() : nullptr, // NOLINT
-                                                  vector_sum_row != nullptr ? vector_sum_row->info() : nullptr, // NOLINT
-                                                  a_offset, b_offset));                                         // NOLINT
-
-    _vector_sum_col = vector_sum_col;
-    _vector_sum_row = vector_sum_row;
-    _mm_result      = mm_result;
-    _a_offset       = a_offset;
-    _b_offset       = b_offset;
-    _k_offset       = a_offset * b_offset * k;
-
-    // If a_offset == 0, vector_sum_col can be a nullptr
-    if(a_offset != 0)
-    {
-        // Check if vector_sum_col_shape should be slidden or not
-        // Don't slide vector_sum_col_shape along the y dimension if vector_sum_col_shape has just 1 dimension and vector_sum_row_shape more than 1
-        // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
-        _slide_vector_sum_col = vector_sum_col->info()->tensor_shape().num_dimensions() > 1;
-    }
-
-    // Configure kernel window
-    auto win_config = validate_and_configure_window(mm_result->info(),
-                                                    vector_sum_col != nullptr ? vector_sum_col->info() : nullptr, // NOLINT
-                                                    vector_sum_row != nullptr ? vector_sum_row->info() : nullptr, // NOLINT
-                                                    a_offset, b_offset);
-    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
-    INEKernel::configure(win_config.second);
-}
-
-Status NEGEMMLowpOffsetContributionKernel::validate(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row,
-                                                    int32_t a_offset, int32_t b_offset)
-{
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(mm_result, vector_sum_col, vector_sum_row, a_offset, b_offset));
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(mm_result->clone().get(),
-                                                              vector_sum_col != nullptr ? vector_sum_col->clone().get() : nullptr,
-                                                              vector_sum_row != nullptr ? vector_sum_row->clone().get() : nullptr,
-                                                              a_offset, b_offset)
-                                .first); // NOLINT
-
-    return Status{};
-}
 
-void NEGEMMLowpOffsetContributionKernel::run(const Window &window, const ThreadInfo &info)
+template <bool is_gemm3d>
+void run_offset_contribution(const Window &window,
+                             ITensor *mm_result, const ITensor *vector_sum_col, const ITensor *vector_sum_row,
+                             int32_t a_offset, int32_t b_offset, int32_t k_offset, bool slide_vector_sum_col)
 {
-    ARM_COMPUTE_UNUSED(info);
-    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
-    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    Window collapsed_window = window.collapse_if_possible(window, Window::DimZ);
 
-    Window collapsed_window = window.collapse_if_possible(IKernel::window(), Window::DimZ);
+    const int height_input = is_gemm3d ? mm_result->info()->dimension(1) : 0;
+    const int depth_input  = is_gemm3d ? mm_result->info()->dimension(2) : 1;
 
-    if(_a_offset != 0 && _b_offset != 0) // true, true
+    if((a_offset != 0) && (b_offset != 0) && (vector_sum_col != nullptr) && (vector_sum_row != nullptr)) // true, true
     {
         // Set window for vector_sum_col
         Window win_vector_sum_col(collapsed_window);
         win_vector_sum_col.set(Window::DimY, Window::Dimension(0, 0, 0));
-        if(!_slide_vector_sum_col)
-        {
-            win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0));
-        }
+        win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0));
 
         // Set window for vector_sum_row
         Window win_vector_sum_row(collapsed_window);
@@ -195,42 +149,49 @@ void NEGEMMLowpOffsetContributionKernel::run(const Window &window, const ThreadI
         win_vector_sum_row.set(Window::DimY, Window::Dimension(0, 0, 0));
         win_vector_sum_row.set(Window::DimZ, Window::Dimension(0, 0, 0));
 
-        Iterator vector_sum_col(_vector_sum_col, win_vector_sum_col);
-        Iterator vector_sum_row(_vector_sum_row, win_vector_sum_row);
-        Iterator mm_result(_mm_result, window);
+        Iterator vector_sum_col_it(vector_sum_col, win_vector_sum_col);
+        Iterator vector_sum_row_it(vector_sum_row, win_vector_sum_row);
+        Iterator mm_result_it(mm_result, window);
 
-        const size_t sum_row_stride_y = _vector_sum_row->info()->strides_in_bytes().y();
+        const size_t sum_row_stride_y = vector_sum_row->info()->strides_in_bytes().y();
+
+        // Offset in case vector_sum_col is batched
+        const int vector_sum_col_batch_offset = slide_vector_sum_col ? vector_sum_col->info()->strides_in_bytes().z() : 0;
 
         execute_window_loop(collapsed_window, [&](const Coordinates & id)
         {
+            const int  batch_id           = id.z() / depth_input;
+            const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_batch_offset);
+
             // Compute the leftover term due to a_offset.
             int32x4x4_t a_offset_term_s32 =
             {
                 {
-                    vld1q_s32(reinterpret_cast<const int32_t *>(vector_sum_col.ptr()) + 0),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(vector_sum_col.ptr()) + 4),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(vector_sum_col.ptr()) + 8),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(vector_sum_col.ptr()) + 12)
+                    vld1q_s32(vector_sum_col_ptr + 0),
+                    vld1q_s32(vector_sum_col_ptr + 4),
+                    vld1q_s32(vector_sum_col_ptr + 8),
+                    vld1q_s32(vector_sum_col_ptr + 12)
                 }
             };
 
-            a_offset_term_s32.val[0] = vmulq_n_s32(a_offset_term_s32.val[0], _a_offset);
-            a_offset_term_s32.val[1] = vmulq_n_s32(a_offset_term_s32.val[1], _a_offset);
-            a_offset_term_s32.val[2] = vmulq_n_s32(a_offset_term_s32.val[2], _a_offset);
-            a_offset_term_s32.val[3] = vmulq_n_s32(a_offset_term_s32.val[3], _a_offset);
+            a_offset_term_s32.val[0] = vmulq_n_s32(a_offset_term_s32.val[0], a_offset);
+            a_offset_term_s32.val[1] = vmulq_n_s32(a_offset_term_s32.val[1], a_offset);
+            a_offset_term_s32.val[2] = vmulq_n_s32(a_offset_term_s32.val[2], a_offset);
+            a_offset_term_s32.val[3] = vmulq_n_s32(a_offset_term_s32.val[3], a_offset);
 
             // Compute the leftover term due to b_offset.
-            int32x4_t b_offset_term_s32 = vld1q_dup_s32(reinterpret_cast<const int32_t *>(vector_sum_row.ptr() + id.z() * sum_row_stride_y) + id.y());
-            b_offset_term_s32           = vmulq_n_s32(b_offset_term_s32, _b_offset);
+            int32x4_t b_offset_term_s32 = vld1q_dup_s32(reinterpret_cast<const int32_t *>(vector_sum_row_it.ptr() + batch_id * sum_row_stride_y) + id.y()
+                                                        + (id.z() % depth_input) * height_input);
+            b_offset_term_s32 = vmulq_n_s32(b_offset_term_s32, b_offset);
 
             // Add a_offset_term_s32 and b_offset_term_s32
             int32x4x4_t offset_term_s32 =
             {
                 {
-                    vdupq_n_s32(_k_offset),
-                    vdupq_n_s32(_k_offset),
-                    vdupq_n_s32(_k_offset),
-                    vdupq_n_s32(_k_offset)
+                    vdupq_n_s32(k_offset),
+                    vdupq_n_s32(k_offset),
+                    vdupq_n_s32(k_offset),
+                    vdupq_n_s32(k_offset)
                 }
             };
 
@@ -242,10 +203,10 @@ void NEGEMMLowpOffsetContributionKernel::run(const Window &window, const ThreadI
             int32x4x4_t in_s32 =
             {
                 {
-                    vld1q_s32(reinterpret_cast<const int32_t *>(mm_result.ptr()) + 0),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(mm_result.ptr()) + 4),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(mm_result.ptr()) + 8),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(mm_result.ptr()) + 12)
+                    vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + 0),
+                    vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + 4),
+                    vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + 8),
+                    vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + 12)
                 }
             };
 
@@ -256,39 +217,44 @@ void NEGEMMLowpOffsetContributionKernel::run(const Window &window, const ThreadI
             in_s32.val[3] = vaddq_s32(in_s32.val[3], offset_term_s32.val[3]);
 
             // Store the result with the offset contribution
-            vst1q_s32(reinterpret_cast<int32_t *>(mm_result.ptr()) + 0, in_s32.val[0]);
-            vst1q_s32(reinterpret_cast<int32_t *>(mm_result.ptr()) + 4, in_s32.val[1]);
-            vst1q_s32(reinterpret_cast<int32_t *>(mm_result.ptr()) + 8, in_s32.val[2]);
-            vst1q_s32(reinterpret_cast<int32_t *>(mm_result.ptr()) + 12, in_s32.val[3]);
+            vst1q_s32(reinterpret_cast<int32_t *>(mm_result_it.ptr()) + 0, in_s32.val[0]);
+            vst1q_s32(reinterpret_cast<int32_t *>(mm_result_it.ptr()) + 4, in_s32.val[1]);
+            vst1q_s32(reinterpret_cast<int32_t *>(mm_result_it.ptr()) + 8, in_s32.val[2]);
+            vst1q_s32(reinterpret_cast<int32_t *>(mm_result_it.ptr()) + 12, in_s32.val[3]);
         },
-        vector_sum_col, vector_sum_row, mm_result);
+        vector_sum_col_it, vector_sum_row_it, mm_result_it);
     }
-    else if((_a_offset == 0) && (_b_offset != 0)) // false, true
+    else if((a_offset == 0) && (b_offset != 0) && (vector_sum_row != nullptr)) // false, true
     {
+        ARM_COMPUTE_ERROR_ON_NULLPTR(vector_sum_row);
+
         // Set window for vector_sum_row
         Window win_vector_sum_row(collapsed_window);
         win_vector_sum_row.set(Window::DimX, Window::Dimension(0, 0, 0));
         win_vector_sum_row.set(Window::DimY, Window::Dimension(0, 0, 0));
         win_vector_sum_row.set(Window::DimZ, Window::Dimension(0, 0, 0));
 
-        Iterator vector_sum_row(_vector_sum_row, win_vector_sum_row);
-        Iterator mm_result(_mm_result, window);
+        Iterator vector_sum_row_it(vector_sum_row, win_vector_sum_row);
+        Iterator mm_result_it(mm_result, window);
 
-        const size_t sum_row_stride_y = _vector_sum_row->info()->strides_in_bytes().y();
+        const size_t sum_row_stride_y = vector_sum_row->info()->strides_in_bytes().y();
 
         execute_window_loop(window, [&](const Coordinates & id)
         {
+            const int batch_id = id.z() / depth_input;
+
             // Compute the leftover term due to b_offset.
-            int32x4_t b_offset_term_s32 = vld1q_dup_s32(reinterpret_cast<const int32_t *>(vector_sum_row.ptr() + id.z() * sum_row_stride_y) + id.y());
-            b_offset_term_s32           = vmulq_n_s32(b_offset_term_s32, _b_offset);
+            int32x4_t b_offset_term_s32 = vld1q_dup_s32(reinterpret_cast<const int32_t *>(vector_sum_row_it.ptr() + batch_id * sum_row_stride_y) + id.y()
+                                                        + (id.z() % depth_input) * height_input);
+            b_offset_term_s32 = vmulq_n_s32(b_offset_term_s32, b_offset);
 
             int32x4x4_t in_s32 =
             {
                 {
-                    vld1q_s32(reinterpret_cast<const int32_t *>(mm_result.ptr()) + 0),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(mm_result.ptr()) + 4),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(mm_result.ptr()) + 8),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(mm_result.ptr()) + 12)
+                    vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + 0),
+                    vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + 4),
+                    vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + 8),
+                    vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + 12)
                 }
             };
 
@@ -299,51 +265,54 @@ void NEGEMMLowpOffsetContributionKernel::run(const Window &window, const ThreadI
             in_s32.val[3] = vaddq_s32(in_s32.val[3], b_offset_term_s32);
 
             // Store the result with the offset contribution
-            vst1q_s32(reinterpret_cast<int32_t *>(mm_result.ptr()) + 0, in_s32.val[0]);
-            vst1q_s32(reinterpret_cast<int32_t *>(mm_result.ptr()) + 4, in_s32.val[1]);
-            vst1q_s32(reinterpret_cast<int32_t *>(mm_result.ptr()) + 8, in_s32.val[2]);
-            vst1q_s32(reinterpret_cast<int32_t *>(mm_result.ptr()) + 12, in_s32.val[3]);
+            vst1q_s32(reinterpret_cast<int32_t *>(mm_result_it.ptr()) + 0, in_s32.val[0]);
+            vst1q_s32(reinterpret_cast<int32_t *>(mm_result_it.ptr()) + 4, in_s32.val[1]);
+            vst1q_s32(reinterpret_cast<int32_t *>(mm_result_it.ptr()) + 8, in_s32.val[2]);
+            vst1q_s32(reinterpret_cast<int32_t *>(mm_result_it.ptr()) + 12, in_s32.val[3]);
         },
-        vector_sum_row, mm_result);
+        vector_sum_row_it, mm_result_it);
     }
-    else if((_a_offset != 0) && (_b_offset == 0)) // true, false
+    else if((a_offset != 0) && (b_offset == 0) && (vector_sum_col != nullptr)) // true, false
     {
         // Set window for vector_sum_col
         Window win_vector_sum_col(collapsed_window);
         win_vector_sum_col.set(Window::DimY, Window::Dimension(0, 0, 0));
-        if(!_slide_vector_sum_col)
-        {
-            win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0));
-        }
+        win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0));
 
-        Iterator vector_sum_col(_vector_sum_col, win_vector_sum_col);
-        Iterator mm_result(_mm_result, window);
+        Iterator vector_sum_col_it(vector_sum_col, win_vector_sum_col);
+        Iterator mm_result_it(mm_result, window);
+
+        // Offset in case vector_sum_col is batched
+        const int vector_sum_col_batch_offset = slide_vector_sum_col ? vector_sum_col->info()->strides_in_bytes().z() : 0;
 
         execute_window_loop(window, [&](const Coordinates & id)
         {
+            const int  batch_id           = id.z() / depth_input;
+            const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_batch_offset);
+
             // Compute the leftover term due to a_offset.
             int32x4x4_t a_offset_term_s32 =
             {
                 {
-                    vld1q_s32(reinterpret_cast<const int32_t *>(vector_sum_col.ptr()) + 0),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(vector_sum_col.ptr()) + 4),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(vector_sum_col.ptr()) + 8),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(vector_sum_col.ptr()) + 12)
+                    vld1q_s32(vector_sum_col_ptr + 0),
+                    vld1q_s32(vector_sum_col_ptr + 4),
+                    vld1q_s32(vector_sum_col_ptr + 8),
+                    vld1q_s32(vector_sum_col_ptr + 12)
                 }
             };
 
-            a_offset_term_s32.val[0] = vmulq_n_s32(a_offset_term_s32.val[0], _a_offset);
-            a_offset_term_s32.val[1] = vmulq_n_s32(a_offset_term_s32.val[1], _a_offset);
-            a_offset_term_s32.val[2] = vmulq_n_s32(a_offset_term_s32.val[2], _a_offset);
-            a_offset_term_s32.val[3] = vmulq_n_s32(a_offset_term_s32.val[3], _a_offset);
+            a_offset_term_s32.val[0] = vmulq_n_s32(a_offset_term_s32.val[0], a_offset);
+            a_offset_term_s32.val[1] = vmulq_n_s32(a_offset_term_s32.val[1], a_offset);
+            a_offset_term_s32.val[2] = vmulq_n_s32(a_offset_term_s32.val[2], a_offset);
+            a_offset_term_s32.val[3] = vmulq_n_s32(a_offset_term_s32.val[3], a_offset);
 
             int32x4x4_t in_s32 =
             {
                 {
-                    vld1q_s32(reinterpret_cast<const int32_t *>(mm_result.ptr()) + 0),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(mm_result.ptr()) + 4),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(mm_result.ptr()) + 8),
-                    vld1q_s32(reinterpret_cast<const int32_t *>(mm_result.ptr()) + 12)
+                    vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + 0),
+                    vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + 4),
+                    vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + 8),
+                    vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + 12)
                 }
             };
 
@@ -354,12 +323,12 @@ void NEGEMMLowpOffsetContributionKernel::run(const Window &window, const ThreadI
             in_s32.val[3] = vaddq_s32(in_s32.val[3], a_offset_term_s32.val[3]);
 
             // Store the result with the offset contribution
-            vst1q_s32(reinterpret_cast<int32_t *>(mm_result.ptr()) + 0, in_s32.val[0]);
-            vst1q_s32(reinterpret_cast<int32_t *>(mm_result.ptr()) + 4, in_s32.val[1]);
-            vst1q_s32(reinterpret_cast<int32_t *>(mm_result.ptr()) + 8, in_s32.val[2]);
-            vst1q_s32(reinterpret_cast<int32_t *>(mm_result.ptr()) + 12, in_s32.val[3]);
+            vst1q_s32(reinterpret_cast<int32_t *>(mm_result_it.ptr()) + 0, in_s32.val[0]);
+            vst1q_s32(reinterpret_cast<int32_t *>(mm_result_it.ptr()) + 4, in_s32.val[1]);
+            vst1q_s32(reinterpret_cast<int32_t *>(mm_result_it.ptr()) + 8, in_s32.val[2]);
+            vst1q_s32(reinterpret_cast<int32_t *>(mm_result_it.ptr()) + 12, in_s32.val[3]);
         },
-        vector_sum_col, mm_result);
+        vector_sum_col_it, mm_result_it);
     }
     else // false, false
     {
@@ -367,3 +336,77 @@ void NEGEMMLowpOffsetContributionKernel::run(const Window &window, const ThreadI
         return;
     }
 }
+} // namespace
+
+NEGEMMLowpOffsetContributionKernel::NEGEMMLowpOffsetContributionKernel()
+    : _vector_sum_col(nullptr), _vector_sum_row(nullptr), _mm_result(nullptr), _a_offset(0), _b_offset(0), _k_offset(0), _slide_vector_sum_col(true)
+{
+}
+
+void NEGEMMLowpOffsetContributionKernel::configure(ITensor *mm_result, const ITensor *vector_sum_col, const ITensor *vector_sum_row, int32_t k, int32_t a_offset, int32_t b_offset)
+{
+    // Perform validate step
+    ARM_COMPUTE_ERROR_ON_NULLPTR(mm_result);
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(mm_result->info(),
+                                                  vector_sum_col != nullptr ? vector_sum_col->info() : nullptr, // NOLINT
+                                                  vector_sum_row != nullptr ? vector_sum_row->info() : nullptr, // NOLINT
+                                                  a_offset, b_offset));                                         // NOLINT
+
+    _vector_sum_col = vector_sum_col;
+    _vector_sum_row = vector_sum_row;
+    _mm_result      = mm_result;
+    _a_offset       = a_offset;
+    _b_offset       = b_offset;
+    _k_offset       = a_offset * b_offset * k;
+
+    // If a_offset == 0, vector_sum_col can be a nullptr
+    if(a_offset != 0)
+    {
+        // Check if vector_sum_col_shape should be slidden or not
+        // Don't slide vector_sum_col_shape along the y dimension if vector_sum_col_shape has just 1 dimension and vector_sum_row_shape more than 1
+        // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
+        _slide_vector_sum_col = vector_sum_col->info()->tensor_shape().num_dimensions() > 1;
+    }
+
+    // Configure kernel window
+    auto win_config = validate_and_configure_window(mm_result->info(),
+                                                    vector_sum_col != nullptr ? vector_sum_col->info() : nullptr, // NOLINT
+                                                    vector_sum_row != nullptr ? vector_sum_row->info() : nullptr, // NOLINT
+                                                    a_offset, b_offset);
+    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+    INEKernel::configure(win_config.second);
+}
+
+Status NEGEMMLowpOffsetContributionKernel::validate(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row,
+                                                    int32_t a_offset, int32_t b_offset)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(mm_result, vector_sum_col, vector_sum_row, a_offset, b_offset));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(mm_result->clone().get(),
+                                                              vector_sum_col != nullptr ? vector_sum_col->clone().get() : nullptr,
+                                                              vector_sum_row != nullptr ? vector_sum_row->clone().get() : nullptr,
+                                                              a_offset, b_offset)
+                                .first); // NOLINT
+
+    return Status{};
+}
+
+void NEGEMMLowpOffsetContributionKernel::run(const Window &window, const ThreadInfo &info)
+{
+    ARM_COMPUTE_UNUSED(info);
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    // Check if input is a 3D reinterpretation
+    const bool reinterpret_as_3d = _vector_sum_row != nullptr
+                                   && _mm_result->info()->num_dimensions() > 1
+                                   && _mm_result->info()->tensor_shape().y() != _vector_sum_row->info()->tensor_shape().x();
+
+    if(reinterpret_as_3d)
+    {
+        run_offset_contribution<true>(window, _mm_result, _vector_sum_col, _vector_sum_row, _a_offset, _b_offset, _k_offset, _slide_vector_sum_col);
+    }
+    else
+    {
+        run_offset_contribution<false>(window, _mm_result, _vector_sum_col, _vector_sum_row, _a_offset, _b_offset, _k_offset, _slide_vector_sum_col);
+    }
+}
\ No newline at end of file
diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp
index 7cd50cc5a0..024c4f8863 100644
--- a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp
+++ b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp
@@ -43,8 +43,7 @@ using namespace arm_compute;
 
 namespace
 {
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output,
-                          int min, int max, unsigned int output_3d_depth)
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32);
     ARM_COMPUTE_RETURN_ERROR_ON(max > 255);
@@ -60,10 +59,8 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, con
 
     if(output->total_size() != 0)
     {
-        const TensorShape output_shape       = arm_compute::misc::shape_calculator::compute_output_stage_shape(*input, output_3d_depth);
-        const TensorInfo  tensor_info_output = output->clone()->set_tensor_shape(output_shape);
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8);
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, input);
     }
 
     return Status{};
@@ -76,6 +73,9 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen
     // For this reason num_elems_processed_per_iteration is set to 1
     constexpr unsigned int num_elems_processed_per_iteration = 1;
 
+    // Output auto inizialitation if not yet initialized
+    auto_init_if_empty(*output, input->clone()->set_data_type(DataType::QASYMM8));
+
     // Configure kernel window
     Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
 
@@ -146,15 +146,15 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window
     ARM_COMPUTE_UNUSED(min_u8);
     ARM_COMPUTE_UNUSED(max_u8);
 
-    const int          window_step_x  = 16;
-    const auto         window_start_x = static_cast<int>(window.x().start());
-    const auto         window_end_x   = static_cast<int>(window.x().end());
-    const unsigned int gemm_3d_height = _input->info()->tensor_shape().y() / _output_3d_depth;
+    const int  window_step_x  = 16;
+    const auto window_start_x = static_cast<int>(window.x().start());
+    const auto window_end_x   = static_cast<int>(window.x().end());
 
     Window win_collapsed = window.collapse_if_possible(window, Window::DimZ);
     win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1));
 
     Iterator in(_input, win_collapsed);
+    Iterator out(_output, win_collapsed);
     if(_bias != nullptr)
     {
         Window win_biases;
@@ -164,16 +164,6 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window
         Iterator bias(_bias, win_biases);
         execute_window_loop(win_collapsed, [&](const Coordinates & id)
         {
-            // Calculate output coordinates
-            Coordinates out_coords = id;
-            if(_output_3d_depth != 1)
-            {
-                out_coords.set(Window::DimY, id.y() % gemm_3d_height);
-                out_coords.set(Window::DimZ, id.y() / gemm_3d_height);
-                out_coords.set(3, id.z());
-            }
-            uint8_t *out_ptr = _output->ptr_to_element(out_coords);
-
             // Compute 16 elements per iteration
             int x = window_start_x;
             for(; x <= (window_end_x - window_step_x); x += window_step_x)
@@ -204,7 +194,7 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window
                 in_s32.val[2] = vaddq_s32(in_s32.val[2], bias_s32.val[2]);
                 in_s32.val[3] = vaddq_s32(in_s32.val[3], bias_s32.val[3]);
 
-                vst1q_u8(out_ptr + x, finalize_quantization<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, min_u8, max_u8));
+                vst1q_u8(out.ptr() + x, finalize_quantization<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, min_u8, max_u8));
             }
 
             // Compute left-over elements
@@ -217,26 +207,16 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window
                 in_value += bias_value;
 
                 // Finalize and store the result
-                *(out_ptr + x) = finalize_quantization<is_bounded_relu>(vdupq_n_s32(in_value), _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, static_cast<uint8_t>(_min),
-                                                                        static_cast<uint8_t>(_max));
+                *(out.ptr() + x) = finalize_quantization<is_bounded_relu>(vdupq_n_s32(in_value), _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, static_cast<uint8_t>(_min),
+                                                                          static_cast<uint8_t>(_max));
             }
         },
-        in, bias);
+        in, out, bias);
     }
     else
     {
         execute_window_loop(win_collapsed, [&](const Coordinates & id)
         {
-            // Calculate output coordinates
-            Coordinates out_coords = id;
-            if(_output_3d_depth != 1)
-            {
-                out_coords.set(Window::DimY, id.y() % _output_3d_depth);
-                out_coords.set(Window::DimZ, id.y() / _output_3d_depth);
-                out_coords.set(3, id.z());
-            }
-            uint8_t *out_ptr = _output->ptr_to_element(out_coords);
-
             // Compute 16 elements per iteration
             int x = window_start_x;
             for(; x <= (window_end_x - window_step_x); x += window_step_x)
@@ -251,7 +231,7 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window
                     }
                 };
 
-                vst1q_u8(out_ptr + x, finalize_quantization<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, min_u8, max_u8));
+                vst1q_u8(out.ptr() + x, finalize_quantization<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, min_u8, max_u8));
             }
 
             // Compute left-over elements
@@ -260,30 +240,24 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window
                 const int32x4_t in_s32 = vld1q_dup_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x);
 
                 // Finalize and store the result
-                *(out_ptr + x) = finalize_quantization<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, static_cast<uint8_t>(_min), static_cast<uint8_t>(_max));
+                *(out.ptr() + x) = finalize_quantization<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, static_cast<uint8_t>(_min), static_cast<uint8_t>(_max));
             }
         },
-        in);
+        in, out);
     }
 }
 
 NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel()
-    : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _result_fixedpoint_multiplier(0), _result_shift(0), _result_offset_after_shift(0), _min(0), _max(0), _output_3d_depth(1)
+    : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _result_fixedpoint_multiplier(0), _result_shift(0), _result_offset_after_shift(0), _min(0), _max(0)
 {
 }
 
 void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift,
-                                                                          int result_offset_after_shift, int min, int max, unsigned int output_3d_depth)
+                                                                          int result_offset_after_shift, int min, int max)
 {
     // Perform validate step
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
-
-    // Output auto inizialitation if not yet initialized
-    const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_output_stage_shape(*input->info(), output_3d_depth);
-    auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(DataType::QASYMM8).set_tensor_shape(output_shape));
-
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info(),
-                                                  min, max, output_3d_depth));
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info(), min, max));
 
     _input                        = input;
     _bias                         = bias;
@@ -293,7 +267,6 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::configure(const
     _result_offset_after_shift    = result_offset_after_shift;
     _min                          = min;
     _max                          = max;
-    _output_3d_depth              = output_3d_depth;
 
     // Configure kernel window
     auto win_config = validate_and_configure_window(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info());
@@ -305,10 +278,10 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::configure(const
     _func                      = is_bounded_relu ? &NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run<true> : &NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run<false>;
 }
 
-Status NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max, unsigned int output_3d_depth)
+Status NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max, output_3d_depth));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max));
     ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(),
                                                               (bias != nullptr) ? bias->clone().get() : nullptr,
                                                               output->clone().get())
diff --git a/src/runtime/NEON/functions/NEGEMM.cpp b/src/runtime/NEON/functions/NEGEMM.cpp
index 82b9cb80ae..72a3e80330 100644
--- a/src/runtime/NEON/functions/NEGEMM.cpp
+++ b/src/runtime/NEON/functions/NEGEMM.cpp
@@ -139,7 +139,7 @@ Status NEGEMM::validate(const ITensorInfo *a, const ITensorInfo *b, const ITenso
     ARM_COMPUTE_UNUSED(alpha);
 
     ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(a);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::F32, DataType::F16);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(a, b, output);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->dimension(0) != b->dimension(1), "The product AB is defined only if the number of columns in A is equal to the number of rows in B");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported");
diff --git a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
index d02c63cfb3..24332014aa 100644
--- a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
@@ -101,6 +101,9 @@ void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *w
     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));
 
+    const GEMMInfo &gemm_info = 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 */);
+
     if(_is_quantized)
     {
         // Since we need negative offsets for computing convolution, we need to change QuantizationInfo()
@@ -111,7 +114,7 @@ void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *w
         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, nullptr, output, GEMMInfo(false, false, true /* Reshape weights only for the first run*/));
+        _mm_gemmlowp.configure(input, weights, nullptr, output, gemm_info);
 
         // Revert back QuantizatioInfo as input and weights could be used in other convolution layers
         input->info()->set_quantization_info(input_quantization_info);
@@ -120,8 +123,7 @@ void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *w
     else
     {
         // 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 */));
+        _mm_gemm.configure(input, weights, nullptr, output, 1.0f, 0.0f, gemm_info);
     }
 }
 
@@ -129,7 +131,8 @@ Status NEGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITens
 {
     const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
 
-    const GEMMInfo gemm_info = GEMMInfo(false, false, true /* Reshape weights only for the first run */, gemm_3d_depth, skip_im2col);
+    const GEMMInfo &gemm_info = 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 */);
     if(is_quantized)
     {
         // Since we need negative offsets for computing convolution, we need to change QuantizationInfo()
@@ -256,15 +259,24 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig
     }
 
     // Create temporary GEMM output tensor in case we cannot skip col2im
-    if(!_skip_col2im)
+    if(!_skip_col2im || _is_quantized)
     {
-        // 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);
-
         // 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;
+        TensorShape    shape_gemm;
+
+        if(_is_quantized && _skip_col2im)
+        {
+            shape_gemm = output->info()->tensor_shape();
+        }
+        else
+        {
+            // Calculate GEMM output shape
+            shape_gemm = _im2col_output.info()->tensor_shape();
+            shape_gemm.set(0, mat_weights_cols);
+            shape_gemm.set(1, conv_w * conv_h);
+        }
+
         // 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()).set_data_layout(input->info()->data_layout());
@@ -321,8 +333,7 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig
             _is_activationlayer_enabled = false;
         }
 
-        _gemmlowp_output_stage.configure(gemm_output_to_use, biases, gemm_output_staged_to_use, output_multiplier, output_shift, output_quant_info.offset, min_activation, max_activation,
-                                         skip_reshape ? conv_h : 1);
+        _gemmlowp_output_stage.configure(gemm_output_to_use, biases, gemm_output_staged_to_use, output_multiplier, output_shift, output_quant_info.offset, min_activation, max_activation);
     }
 
     if(!_skip_col2im && _data_layout == DataLayout::NCHW)
@@ -336,7 +347,7 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig
         _tmp_output.allocator()->allocate();
     }
 
-    if(!_skip_col2im)
+    if(!_skip_col2im || _is_quantized)
     {
         _gemm_output.allocator()->allocate();
     }
@@ -464,18 +475,20 @@ Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI
     }
 
     // Create temporary GEMM output tensor in case we cannot skip col2im
+    const DataType gemm_data_type = is_quantized ? DataType::S32 : data_type;
     if(!skip_col2im)
     {
         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()).set_data_layout(input->data_layout());
-
-        gemm_output_to_use = &info_gemm;
     }
+    else
+    {
+        info_gemm = TensorInfo(output->tensor_shape(), 1, gemm_data_type);
+    }
+    info_gemm.set_quantization_info(output->quantization_info()).set_data_layout(input->data_layout());
+    gemm_output_to_use = &info_gemm;
 
     ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, gemm_output_to_use, skip_col2im ? conv_h : 0, skip_im2col));
 
@@ -516,7 +529,7 @@ Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI
         }
 
         // Validate output stage for quantized case
-        NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(gemm_output_to_use, biases, gemm_output_staged_to_use, min_activation, max_activation, skip_reshape ? conv_h : 0);
+        NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(gemm_output_to_use, biases, gemm_output_staged_to_use, min_activation, max_activation);
     }
 
     // Validate Col2Im/ReshapeLayer
diff --git a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp
index 16ee3d07fd..4b026948b9 100644
--- a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp
+++ b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp
@@ -190,42 +190,68 @@ Status NEGEMMLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITenso
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(c != nullptr, "Bias addition not supported in NEGEMMLowpMatrixMultiplyCore");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG((a)->dimension(0) != (b)->dimension(1),
                                     "The product AB is defined only if the number of columns in A is equal to the number of rows in B");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG((a)->dimension(1) != (output)->dimension(1),
-                                    "The output matrix must have the same number of rows as the matrix A");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG((b)->dimension(0) != (output)->dimension(0),
-                                    "The output matrix must have the same number of columns as the matrix B");
-    ARM_COMPUTE_UNUSED(gemm_info);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.reinterpret_input_as_3d(), "NEGEMMLowpMatrixMultiplyCore cannot reinterpret the input tensor as 3D");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.depth_output_gemm3d() != 0, "NEGEMMLowpMatrixMultiplyCore cannot reinterpret the output tensor as 3D");
 
-    int32_t a_offset                         = a->quantization_info().offset;
-    int32_t b_offset                         = b->quantization_info().offset;
-    bool    run_vector_matrix_multiplication = a->dimension(1) < 2;
+    int32_t    a_offset                    = a->quantization_info().offset;
+    int32_t    b_offset                    = b->quantization_info().offset;
+    const bool reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run();
 
-    if(!run_vector_matrix_multiplication)
+    // Check if we need to run the optimized assembly kernel
+    const bool run_optimised = bool(NEGEMMAssemblyDispatch::validate(a, b, output, 1.f, 0.f, reshape_b_only_on_first_run));
+
+    if(run_optimised)
     {
-        // The interleaved output matrix will have the following shape: [ a_height * 4, ceil(a_width / 4.0f) ]
-        TensorShape shape_tmp_a = a->tensor_shape();
-        shape_tmp_a.set(0, a->dimension(0) * 4);
-        shape_tmp_a.set(1, std::ceil(a->dimension(1) / 4.f));
-
-        // The transpose1xW output matrix will have the following shape: [ b_height * 16, ceil(b_width / 16.0f) ]
-        TensorShape shape_tmp_b = b->tensor_shape();
-        shape_tmp_b.set(0, b->dimension(1) * 16);
-        shape_tmp_b.set(1, std::ceil(b->dimension(0) / 16.f));
-
-        TensorInfo info_a(shape_tmp_a, 1, a->data_type());
-        TensorInfo info_b(shape_tmp_b, 1, b->data_type());
-
-        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMInterleave4x4Kernel::validate(a, &info_a));
-        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMTranspose1xWKernel::validate(b, &info_b));
-        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixMultiplyKernel::validate(&info_a, &info_b, output));
+        if(output->total_size() != 0)
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON(b->dimension(0) != output->dimension(0));
+            if(gemm_info.depth_output_gemm3d() != 0)
+            {
+                if(gemm_info.reinterpret_input_as_3d())
+                {
+                    ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != output->dimension(1));
+                    ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(2) != output->dimension(2));
+                }
+                else
+                {
+                    ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != output->dimension(1) * output->dimension(2));
+                }
+            }
+            else
+            {
+                ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != output->dimension(1));
+            }
+        }
     }
     else
     {
-        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixMultiplyKernel::validate(a, b, output));
+        ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.reinterpret_input_as_3d(), "NEGEMM cannot reinterpret the input tensor as 3D");
+        ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.depth_output_gemm3d() != 0, "NEGEMM cannot reinterpret the output tensor as 3D");
+
+        const bool run_vector_matrix_multiplication = a->dimension(1) < 2;
+        if(!run_vector_matrix_multiplication)
+        {
+            // The interleaved output matrix will have the following shape: [ a_height * 4, ceil(a_width / 4.0f) ]
+            TensorShape shape_tmp_a = a->tensor_shape();
+            shape_tmp_a.set(0, a->dimension(0) * 4);
+            shape_tmp_a.set(1, std::ceil(a->dimension(1) / 4.f));
+
+            // The transpose1xW output matrix will have the following shape: [ b_height * 16, ceil(b_width / 16.0f) ]
+            TensorShape shape_tmp_b = b->tensor_shape();
+            shape_tmp_b.set(0, b->dimension(1) * 16);
+            shape_tmp_b.set(1, std::ceil(b->dimension(0) / 16.f));
+
+            TensorInfo info_a(shape_tmp_a, 1, a->data_type());
+            TensorInfo info_b(shape_tmp_b, 1, b->data_type());
+
+            ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMInterleave4x4Kernel::validate(a, &info_a));
+            ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMTranspose1xWKernel::validate(b, &info_b));
+            ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixMultiplyKernel::validate(&info_a, &info_b, output));
+        }
+        else
+        {
+            ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixMultiplyKernel::validate(a, b, output));
+        }
     }
 
     TensorInfo info_vector_sum_col, info_vector_sum_row;
diff --git a/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp b/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp
index d270a77fc2..ce69fa0bfd 100644
--- a/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp
+++ b/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp
@@ -43,14 +43,14 @@ Status NEGEMMLowpQuantizeDownInt32ToUint8Scale::validate(const ITensorInfo *inpu
 }
 
 void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift,
-                                                                    int result_offset_after_shift, int min, int max, unsigned int output_3d_depth)
+                                                                    int result_offset_after_shift, int min, int max)
 {
     auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel>();
-    k->configure(input, bias, output, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max, output_3d_depth);
+    k->configure(input, bias, output, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max);
     _kernel = std::move(k);
 }
 
-Status NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max, unsigned int output_3d_depth)
+Status NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max)
 {
-    return NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(input, bias, output, min, max, output_3d_depth);
+    return NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(input, bias, output, min, max);
 }
\ No newline at end of file