COMPMID-1047 Extract Flatten function from Im2Col for NEON

Change-Id: I80f3aaadc8cae8c9ca1a5a239e79bda302b89bd8
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/144813
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>

12 files changed, 357 insertions, 208 deletions

diff --git a/arm_compute/core/NEON/NEKernels.h b/arm_compute/core/NEON/NEKernels.h
index d99e5b4d3c..8664c7732c 100644
--- a/arm_compute/core/NEON/NEKernels.h
+++ b/arm_compute/core/NEON/NEKernels.h
@@ -61,6 +61,7 @@
 #include "arm_compute/core/NEON/kernels/NEFillArrayKernel.h"
 #include "arm_compute/core/NEON/kernels/NEFillBorderKernel.h"
 #include "arm_compute/core/NEON/kernels/NEFillInnerBorderKernel.h"
+#include "arm_compute/core/NEON/kernels/NEFlattenLayerKernel.h"
 #include "arm_compute/core/NEON/kernels/NEFloorKernel.h"
 #include "arm_compute/core/NEON/kernels/NEGEMMAssemblyBaseKernel.h"
 #include "arm_compute/core/NEON/kernels/NEGEMMInterleave4x4Kernel.h"
diff --git a/arm_compute/core/NEON/kernels/NEFlattenLayerKernel.h b/arm_compute/core/NEON/kernels/NEFlattenLayerKernel.h
new file mode 100644
index 0000000000..e5c388a95a
--- /dev/null
+++ b/arm_compute/core/NEON/kernels/NEFlattenLayerKernel.h
@@ -0,0 +1,81 @@
+/*
+ * Copyright (c) 2018 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.
+ */
+#ifndef __ARM_COMPUTE_NEFLATTENLAYERKERNEL_H__
+#define __ARM_COMPUTE_NEFLATTENLAYERKERNEL_H__
+
+#include "arm_compute/core/NEON/INEKernel.h"
+
+namespace arm_compute
+{
+class ITensor;
+
+/** Interface for the flatten layer kernel. */
+class NEFlattenLayerKernel : public INEKernel
+{
+public:
+    const char *name() const override
+    {
+        return "NEFlattenLayerKernel";
+    }
+    /** Default constructor */
+    NEFlattenLayerKernel();
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    NEFlattenLayerKernel(const NEFlattenLayerKernel &) = delete;
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    NEFlattenLayerKernel &operator=(const NEFlattenLayerKernel &) = delete;
+    /** Allow instances of this class to be moved */
+    NEFlattenLayerKernel(NEFlattenLayerKernel &&) = default;
+    /** Allow instances of this class to be moved */
+    NEFlattenLayerKernel &operator=(NEFlattenLayerKernel &&) = default;
+    /** Default destructor */
+    ~NEFlattenLayerKernel() = default;
+
+    /** Set the input and output of the kernel.
+     *
+     * @param[in]  input  First input tensor to flatten with at least 3 dimensions.
+     *                    The dimensions above the third will be interpreted as batches. Data types supported: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32
+     * @param[out] output Output tensor with shape [w*h*d, input_batches] where:
+     *                    w = width input tensor, h = height input tensor and d = depth input tensor. Data type supported: same as @p input
+     */
+    void configure(const ITensor *input, ITensor *output);
+    /** Static function to check if given info will lead to a valid configuration of @ref NEFlattenLayerKernel
+     *
+     * @param[in]  input  First input tensor to flatten with at least 3 dimensions.
+     *                    The dimensions above the third will be interpreted as batches. Data types supported: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32
+     * @param[out] output Output tensor with shape [w*h*d, input_batches] where:
+     *                    w = width input tensor, h = height input tensor and d = depth input tensor. Data type supported: same as @p input
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input, const ITensorInfo *output);
+
+    // Inherited methods overridden:
+    void run(const Window &window, const ThreadInfo &info) override;
+
+private:
+    const ITensor *_input;
+    ITensor       *_output;
+};
+} // namespace arm_compute
+#endif /*__ARM_COMPUTE_NEFLATTENLAYERKERNEL_H__ */
diff --git a/arm_compute/core/NEON/kernels/NEIm2ColKernel.h b/arm_compute/core/NEON/kernels/NEIm2ColKernel.h
index 38bdff0e98..ec89f0f713 100644
--- a/arm_compute/core/NEON/kernels/NEIm2ColKernel.h
+++ b/arm_compute/core/NEON/kernels/NEIm2ColKernel.h
@@ -76,55 +76,46 @@ public:
 
     /** Set the input and output of the kernel.
      *
-     * @param[in]  input              The input tensor to convert. 3 lower dimensions represent a single input [width, height, IFM],
-     *                                while every optional dimension from 4 and above represent a batch of inputs. Data types supported: QASYMM8/F16/F32
-     *                                Note: QASYMM8 works only for has_bias = false
-     * @param[out] output             The output tensor. Data types supported: Same as @p input
-     * @param[in]  kernel_dims        The kernel dimensions (width and height).
-     * @param[in]  conv_info          Contains padding and stride information described in @ref PadStrideInfo.
-     * @param[in]  has_bias           In case biases are provided expands the matrix with 1.
-     * @param[in]  dilation           (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
-     * @param[in]  num_groups         (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is not supported
-     * @param[in]  is_fully_connected (Optional) Determines whether this kernel will be called by @ref NEFullyConnectedLayer in order to validate the arguments
-     * @param[in]  is_flatten         (Optional) Determines whether this kernel will be called by @ref NEFlattenLayer in order to validate the arguments
+     * @param[in]  input       The input tensor to convert. 3 lower dimensions represent a single input [width, height, IFM],
+     *                         while every optional dimension from 4 and above represent a batch of inputs. Data types supported: QASYMM8/F16/F32
+     *                         Note: QASYMM8 works only for has_bias = false
+     * @param[out] output      The output tensor. Data types supported: Same as @p input
+     * @param[in]  kernel_dims The kernel dimensions (width and height).
+     * @param[in]  conv_info   Contains padding and stride information described in @ref PadStrideInfo.
+     * @param[in]  has_bias    In case biases are provided expands the matrix with 1.
+     * @param[in]  dilation    (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
+     * @param[in]  num_groups  (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is not supported
      */
     void configure(const ITensor *input, ITensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info,
-                   bool has_bias, const Size2D &dilation = Size2D(1U, 1U), unsigned int num_groups = 1, bool is_fully_connected = false, bool is_flatten = false);
+                   bool has_bias, const Size2D &dilation = Size2D(1U, 1U), unsigned int num_groups = 1);
     /** Static function to check if given info will lead to a valid configuration of @ref NEIm2ColKernel
      *
-     * @param[in] input              The input tensor to convert. 3 lower dimensions represent a single input [width, height, IFM],
-     *                               while every optional dimension from 4 and above represent a batch of inputs. Data types supported: QASYMM8/F16/F32
-     *                               Note: QASYMM8 works only for has_bias = false
-     * @param[in] output             The output tensor. Data types supported: Same as @p input
-     * @param[in] kernel_dims        The kernel dimensions (width and height).
-     * @param[in] conv_info          Contains padding and stride information described in @ref PadStrideInfo.
-     * @param[in] has_bias           In case biases are provided expands the matrix with 1.
-     * @param[in] dilation           (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
-     * @param[in] num_groups         (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is not supported
-     * @param[in] is_fully_connected (Optional)Determines whether this kernel will be called by @ref NEFullyConnectedLayer in order to validate the arguments
-     * @param[in] is_flatten         (Optional) Determines whether this kernel will be called by @ref NEFlattenLayer in order to validate the arguments
+     * @param[in] input       The input tensor to convert. 3 lower dimensions represent a single input [width, height, IFM],
+     *                        while every optional dimension from 4 and above represent a batch of inputs. Data types supported: QASYMM8/F16/F32
+     *                        Note: QASYMM8 works only for has_bias = false
+     * @param[in] output      The output tensor. Data types supported: Same as @p input
+     * @param[in] kernel_dims The kernel dimensions (width and height).
+     * @param[in] conv_info   Contains padding and stride information described in @ref PadStrideInfo.
+     * @param[in] has_bias    In case biases are provided expands the matrix with 1.
+     * @param[in] dilation    (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
+     * @param[in] num_groups  (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is not supported
      *
      * @return a status
      */
     static Status validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info,
-                           bool has_bias, const Size2D &dilation = Size2D(1U, 1U), unsigned int num_groups = 1, bool is_fully_connected = false, bool is_flatten = false);
+                           bool has_bias, const Size2D &dilation = Size2D(1U, 1U), unsigned int num_groups = 1);
 
     // Inherited methods overridden:
     void run(const Window &window, const ThreadInfo &info) override;
 
 private:
-    /** Template function to run the im2col optimised for the fully connected and flatten layers case
-     *
-     * @param[in] window Region on which to execute the kernel. (Must be a valid region of the window returned by window()).
-     */
-    template <typename T>
-    void run_reduced(const Window &window);
-    /** Template function to run the im2col used for the convolution layer case
+    /** Template function to run the im2col
      *
      * @param[in] window Region on which to execute the kernel. (Must be a valid region of the window returned by window()).
      */
     template <typename T, bool has_pads>
-    void run_generic(const Window &window);
+    void run_im2col(const Window &window);
+
     /** Common signature for all the specialised im2col functions
      *
      * @param[in] window Region on which to execute the kernel.
diff --git a/arm_compute/runtime/CL/functions/CLFlattenLayer.h b/arm_compute/runtime/CL/functions/CLFlattenLayer.h
index ebc0e5e53f..24ed56f345 100644
--- a/arm_compute/runtime/CL/functions/CLFlattenLayer.h
+++ b/arm_compute/runtime/CL/functions/CLFlattenLayer.h
@@ -47,7 +47,7 @@ public:
      *                    w = width input tensor, h = height input tensor and d = depth input tensor. Data type supported: same as @p input
      */
     void configure(const ICLTensor *input, ICLTensor *output);
-    /** Static function to check if given info will lead to a valid configuration of @ref CLTranspose
+    /** Static function to check if given info will lead to a valid configuration of @ref CLFlattenLayer
      *
      * @param[in]  input  First input tensor to flatten with at least 3 dimensions.
      *                    The dimensions above the third will be interpreted as batches. Data types supported: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32
diff --git a/arm_compute/runtime/NEON/functions/NEFlattenLayer.h b/arm_compute/runtime/NEON/functions/NEFlattenLayer.h
index 2c259fa178..26d7c7f636 100644
--- a/arm_compute/runtime/NEON/functions/NEFlattenLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEFlattenLayer.h
@@ -31,11 +31,7 @@ namespace arm_compute
 {
 class ITensor;
 
-/** Basic function to execute flatten. This function calls the following NEON kernel:
-*
-* -# @ref NEIm2ColKernel
-*
-*/
+/** Basic function to execute flatten layer kernel. */
 class NEFlattenLayer : public INESimpleFunction
 {
 public:
@@ -46,6 +42,17 @@ public:
      *             w = width input tensor, h = height input tensor and d = depth input tensor. Data type supported: same as @p input
      */
     void configure(const ITensor *input, ITensor *output);
+
+    /** Static function to check if given info will lead to a valid configuration of @ref NEFlattenLayer
+     *
+     * @param[in]  input  First input tensor to flatten with at least 3 dimensions.
+     *                    The dimensions above the third will be interpreted as batches. Data types supported: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32
+     * @param[out] output Output tensor with shape [w*h*d, input_batches] where:
+     *                    w = width input tensor, h = height input tensor and d = depth input tensor. Data type supported: same as @p input
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input, const ITensorInfo *output);
 };
 } // namespace arm_compute
 
diff --git a/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h b/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h
index fe0f2f03f7..9c9074ceec 100644
--- a/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h
@@ -26,8 +26,8 @@
 
 #include "arm_compute/runtime/IFunction.h"
 
+#include "arm_compute/core/NEON/kernels/NEFlattenLayerKernel.h"
 #include "arm_compute/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.h"
-#include "arm_compute/core/NEON/kernels/NEIm2ColKernel.h"
 #include "arm_compute/core/NEON/kernels/NETransposeKernel.h"
 #include "arm_compute/runtime/MemoryGroup.h"
 #include "arm_compute/runtime/NEON/functions/NEConvertFullyConnectedWeights.h"
@@ -129,14 +129,14 @@ private:
     void configure_mm(const ITensor *input, const ITensor *weights, ITensor *output);
 
     MemoryGroup                                         _memory_group;
-    NEIm2ColKernel                                      _im2col_kernel;
+    NEFlattenLayerKernel                                _flatten_kernel;
     NEConvertFullyConnectedWeights                      _convert_weights;
     NEFullyConnectedLayerReshapeWeights                 _reshape_weights_function;
     NEGEMM                                              _mm_gemm;
     NEGEMMLowpMatrixMultiplyCore                        _mm_gemmlowp;
     NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint _gemmlowp_output_stage;
     NEGEMMMatrixAccumulateBiasesKernel                  _accumulate_biases_kernel;
-    Tensor                                              _im2col_output;
+    Tensor                                              _flatten_output;
     Tensor                                              _gemmlowp_output;
     Tensor                                              _converted_weights_output;
     Tensor                                              _reshape_weights_output;
diff --git a/arm_compute/runtime/NEON/functions/NEIm2Col.h b/arm_compute/runtime/NEON/functions/NEIm2Col.h
index 9df4f070d8..de4780f8f0 100644
--- a/arm_compute/runtime/NEON/functions/NEIm2Col.h
+++ b/arm_compute/runtime/NEON/functions/NEIm2Col.h
@@ -42,38 +42,34 @@ public:
     NEIm2Col();
     /** Configure the im2col NEON kernel
      *
-     * @param[in]  input              The input tensor to convert. 3 lower dimensions represent a single input [width, height, IFM],
-     *                                while every optional dimension from 4 and above represent a batch of inputs. Data types supported: QASYMM8/F16/F32
-     *                                Note: QASYMM8 works only for has_bias = false
-     * @param[out] output             The output tensor. Data types supported: Same as @p input
-     * @param[in]  kernel_dims        The kernel dimensions (width and height).
-     * @param[in]  conv_info          Contains padding and stride information described in @ref PadStrideInfo.
-     * @param[in]  has_bias           In case biases are provided expands the matrix with 1.
-     * @param[in]  dilation           (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
-     * @param[in]  num_groups         (Optional) Number of groups when performing a grouped convolution
-     * @param[in]  is_fully_connected (Optional) Determines whether this function will be called by @ref NEFullyConnectedLayer in order to validate the arguments
-     * @param[in]  is_flatten         (Optional) Determines whether this function will be called by @ref NEFlattenLayer in order to validate the arguments
+     * @param[in]  input       The input tensor to convert. 3 lower dimensions represent a single input [width, height, IFM],
+     *                         while every optional dimension from 4 and above represent a batch of inputs. Data types supported: QASYMM8/F16/F32
+     *                         Note: QASYMM8 works only for has_bias = false
+     * @param[out] output      The output tensor. Data types supported: Same as @p input
+     * @param[in]  kernel_dims The kernel dimensions (width and height).
+     * @param[in]  conv_info   Contains padding and stride information described in @ref PadStrideInfo.
+     * @param[in]  has_bias    In case biases are provided expands the matrix with 1.
+     * @param[in]  dilation    (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
+     * @param[in]  num_groups  (Optional) Number of groups when performing a grouped convolution
      */
     void configure(const ITensor *input, ITensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation = Size2D(1U, 1U),
-                   unsigned int num_groups = 1, bool is_fully_connected = false, bool is_flatten = false);
+                   unsigned int num_groups = 1);
     /** Static function to check if given info will lead to a valid configuration of @ref NEIm2Col
      *
-     * @param[in] input              The input tensor to convert. 3 lower dimensions represent a single input [width, height, IFM],
-     *                               while every optional dimension from 4 and above represent a batch of inputs. Data types supported: QASYMM8/F16/F32
-     *                               Note: QASYMM8 works only for has_bias = false
-     * @param[in] output             The output tensor. Data types supported: Same as @p input
-     * @param[in] kernel_dims        The kernel dimensions (width and height).
-     * @param[in] conv_info          Contains padding and stride information described in @ref PadStrideInfo.
-     * @param[in] has_bias           In case biases are provided expands the matrix with 1.
-     * @param[in] dilation           (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
-     * @param[in] num_groups         (Optional) Number of groups when performing a grouped convolution
-     * @param[in] is_fully_connected (Optional) Determines whether this function will be called by @ref NEFullyConnectedLayer in order to validate the arguments
-     * @param[in] is_flatten         (Optional) Determines whether this function will be called by @ref NEFlattenLayer in order to validate the arguments
+     * @param[in] input       The input tensor to convert. 3 lower dimensions represent a single input [width, height, IFM],
+     *                        while every optional dimension from 4 and above represent a batch of inputs. Data types supported: QASYMM8/F16/F32
+     *                        Note: QASYMM8 works only for has_bias = false
+     * @param[in] output      The output tensor. Data types supported: Same as @p input
+     * @param[in] kernel_dims The kernel dimensions (width and height).
+     * @param[in] conv_info   Contains padding and stride information described in @ref PadStrideInfo.
+     * @param[in] has_bias    In case biases are provided expands the matrix with 1.
+     * @param[in] dilation    (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
+     * @param[in] num_groups  (Optional) Number of groups when performing a grouped convolution
      *
      * @return a status
      */
     static Status validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation = Size2D(1U, 1U),
-                           unsigned int num_groups = 1, bool is_fully_connected = false, bool is_flatten = false);
+                           unsigned int num_groups = 1);
 
     // Inherited methods overridden:
     void run() override;
diff --git a/src/core/NEON/NEFlattenLayerKernel.cpp b/src/core/NEON/NEFlattenLayerKernel.cpp
new file mode 100644
index 0000000000..b8452fb5fc
--- /dev/null
+++ b/src/core/NEON/NEFlattenLayerKernel.cpp
@@ -0,0 +1,137 @@
+/*
+ * Copyright (c) 2018 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/core/NEON/kernels/NEFlattenLayerKernel.h"
+
+#include "arm_compute/core/CPP/Validate.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+
+#include <arm_neon.h>
+
+using namespace arm_compute;
+using namespace misc::shape_calculator;
+
+namespace
+{
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QASYMM8,
+                                                         DataType::U16, DataType::S16,
+                                                         DataType::U32, DataType::S32,
+                                                         DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
+
+    // Checks performed when output is configured
+    if(output->total_size() != 0)
+    {
+        const TensorInfo tensor_info_output = input->clone()->set_tensor_shape(compute_flatten_shape(input));
+
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+    }
+
+    return Status{};
+}
+
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output)
+{
+    // Output tensor auto initialization if not yet initialized
+    auto_init_if_empty(*output, input->clone()->set_tensor_shape(compute_flatten_shape(input)));
+
+    Window win = calculate_max_window(*input, Steps()); // Flatten does not need paddings
+
+    output->set_valid_region(ValidRegion(Coordinates(), output->tensor_shape()));
+
+    return std::make_pair(Status{}, win);
+}
+} // namespace
+
+NEFlattenLayerKernel::NEFlattenLayerKernel()
+    : _input(nullptr), _output(nullptr)
+{
+}
+
+void NEFlattenLayerKernel::configure(const ITensor *input, ITensor *output)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info()));
+
+    _input  = input;
+    _output = output;
+
+    // Configure kernel window
+    auto win_config = validate_and_configure_window(input->info(), output->info());
+    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+    INEKernel::configure(win_config.second);
+}
+
+Status NEFlattenLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get()).first);
+    return Status{};
+}
+
+void NEFlattenLayerKernel::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);
+
+    const size_t in_width   = _input->info()->dimension(0);
+    const size_t in_height  = _input->info()->dimension(1);
+    const size_t out_step_x = in_width * _input->info()->element_size();
+    const size_t out_step_y = out_step_x * in_height;
+
+    Window in_window(window);
+    in_window.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+    Window out_window;
+    out_window.use_tensor_dimensions(_output->info()->tensor_shape());
+    out_window.set(Window::DimX, Window::Dimension(out_window.x().start(), out_window.x().end(), in_width));
+
+    Window in_slice  = in_window.first_slice_window_3D();
+    Window out_slice = out_window.first_slice_window_1D();
+
+    do
+    {
+        Iterator in(_input, in_slice);
+        Iterator out(_output, out_slice);
+
+        uint8_t *out_ptr = out.ptr();
+
+        execute_window_loop(in_slice, [&](const Coordinates & id)
+        {
+            memcpy(out_ptr + id.y() * out_step_x + id.z() * out_step_y, in.ptr(), out_step_x);
+        },
+        in);
+    }
+    while(in_window.slide_window_slice_3D(in_slice) && out_window.slide_window_slice_1D(out_slice));
+}
diff --git a/src/core/NEON/kernels/NEIm2ColKernel.cpp b/src/core/NEON/kernels/NEIm2ColKernel.cpp
index 98b1488a9d..e5d31289a4 100644
--- a/src/core/NEON/kernels/NEIm2ColKernel.cpp
+++ b/src/core/NEON/kernels/NEIm2ColKernel.cpp
@@ -41,11 +41,12 @@
 #include <tuple>
 
 using namespace arm_compute;
+using namespace misc::shape_calculator;
 
 namespace
 {
 Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info,
-                          bool has_bias, const Size2D &dilation, unsigned int num_groups, bool is_fully_connected, bool is_flatten)
+                          bool has_bias, const Size2D &dilation, unsigned int num_groups)
 {
     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);
@@ -55,18 +56,7 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, c
 
     if(output->total_size() > 0)
     {
-        TensorShape expected_output_shape;
-
-        if(is_flatten || is_fully_connected)
-        {
-            expected_output_shape = misc::shape_calculator::compute_flatten_shape(input);
-        }
-        else
-        {
-            expected_output_shape = misc::shape_calculator::compute_im2col_conv_shape(input, kernel_dims, conv_info, has_bias, dilation, false);
-        }
-
-        TensorInfo expected_output = output->clone()->set_tensor_shape(expected_output_shape);
+        TensorInfo expected_output = output->clone()->set_tensor_shape(compute_im2col_conv_shape(input, kernel_dims, conv_info, has_bias, dilation, false));
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&expected_output, output);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
     }
@@ -74,6 +64,31 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, c
     return Status{};
 }
 
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info,
+                                                        bool has_bias, const Size2D &dilation)
+{
+    const unsigned int width_idx   = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
+    const unsigned int height_idx  = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
+    const unsigned int channel_idx = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL);
+
+    std::pair<unsigned int, unsigned int> convolved_dims = scaled_dimensions(input->dimension(width_idx), input->dimension(height_idx),
+                                                                             kernel_dims.width, kernel_dims.height,
+                                                                             conv_info, dilation);
+
+    // Output tensor auto initialization if not yet initialized
+    auto_init_if_empty(*output, input->clone()->set_tensor_shape(compute_im2col_conv_shape(input, kernel_dims, conv_info, has_bias, dilation, false)));
+
+    Window win = calculate_max_window(*input, Steps());
+    win.set(width_idx, Window::Dimension(0, convolved_dims.first, 1));
+    win.set(height_idx, Window::Dimension(0, convolved_dims.second, 1));
+    win.set(channel_idx, Window::Dimension(0, 1, 1));
+
+    // The NEIm2ColKernel doesn't need padding so update_window_and_padding() can be skipped
+    output->set_valid_region(ValidRegion(Coordinates(), output->tensor_shape()));
+
+    return std::make_pair(Status{}, win);
+}
+
 template <typename T, bool has_pads>
 inline void linearize_volume(const uint8_t *const in_ptr,
                              T                   *out_ptr,
@@ -174,7 +189,7 @@ inline void linearize_volume(const uint8_t *const in_ptr,
 } // namespace
 
 template <typename T, bool has_pads>
-void NEIm2ColKernel::run_generic(const Window &window)
+void NEIm2ColKernel::run_im2col(const Window &window)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
@@ -244,66 +259,21 @@ void NEIm2ColKernel::run_generic(const Window &window)
     in, out);
 }
 
-template <typename T>
-void NEIm2ColKernel::run_reduced(const Window &window)
-{
-    const size_t in_width   = _input->info()->dimension(0);
-    const size_t in_height  = _input->info()->dimension(1);
-    const size_t out_step_x = in_width * _input->info()->element_size();
-    const size_t out_step_y = out_step_x * in_height;
-    const size_t out_width  = _output->info()->dimension(0);
-
-    Window in_window(window);
-    in_window.set(Window::DimX, Window::Dimension(0, 1, 1));
-
-    Window out_window;
-    out_window.use_tensor_dimensions(_output->info()->tensor_shape());
-    out_window.set(Window::DimX, Window::Dimension(out_window.x().start(), out_window.x().end(), in_width));
-
-    Window in_slice  = in_window.first_slice_window_3D();
-    Window out_slice = out_window.first_slice_window_1D();
-
-    do
-    {
-        Iterator in(_input, in_slice);
-        Iterator out(_output, out_slice);
-
-        uint8_t *out_ptr = out.ptr();
-
-        execute_window_loop(in_slice, [&](const Coordinates & id)
-        {
-            memcpy(out_ptr + id.y() * out_step_x + id.z() * out_step_y, in.ptr(), out_step_x);
-        },
-        in);
-
-        // Add bias
-        if(_has_bias)
-        {
-            *(reinterpret_cast<T *>(out_ptr) + out_width - 1) = static_cast<T>(1);
-        }
-    }
-    while(in_window.slide_window_slice_3D(in_slice) && out_window.slide_window_slice_1D(out_slice));
-}
-
 NEIm2ColKernel::NEIm2ColKernel()
     : _func(), _input(nullptr), _output(nullptr), _convolved_dims(), _conv_info(), _kernel_width(0), _kernel_height(0), _has_bias(false), _dilation(1U, 1U)
 {
 }
 
 void NEIm2ColKernel::configure(const ITensor *input, ITensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info,
-                               bool has_bias, const Size2D &dilation, unsigned int num_groups, bool is_fully_connected, bool is_flatten)
+                               bool has_bias, const Size2D &dilation, unsigned int num_groups)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
-
-    // Perform validation step
-    ARM_COMPUTE_UNUSED(is_fully_connected, is_flatten);
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), kernel_dims, conv_info, has_bias, dilation, num_groups));
     ARM_COMPUTE_UNUSED(num_groups);
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), kernel_dims, conv_info, has_bias, dilation, num_groups, is_fully_connected, is_flatten));
 
     const DataLayout   data_layout = input->info()->data_layout();
     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);
-    const unsigned int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
 
     _input          = input;
     _output         = output;
@@ -316,73 +286,35 @@ void NEIm2ColKernel::configure(const ITensor *input, ITensor *output, const Size
                                         _conv_info, _dilation);
     _has_bias = has_bias;
 
-    unsigned int stride_x = 0;
-    unsigned int stride_y = 0;
-    std::tie(stride_x, stride_y) = conv_info.stride();
-
-    bool run_img2col_reduced = (output->info()->dimension(0) == (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))) && (TensorShape::num_max_dimensions >= 4)
-                               && (std::equal(input->info()->tensor_shape().cbegin() + 3,
-                                              input->info()->tensor_shape().cend(),
-                                              output->info()->tensor_shape().cbegin() + 1))
-                               && ((stride_x == 1) && (stride_y == 1) && !conv_info.has_padding())
-                               && ((dilation.x() == 1) && (dilation.y() == 1));
-
-    Window window = calculate_max_window(*input->info(), Steps());
-
-    if(run_img2col_reduced)
+    switch(_input->info()->data_type())
     {
-        switch(_input->info()->data_type())
-        {
-            case DataType::F32:
-                _func = &NEIm2ColKernel::run_reduced<float>;
-                break;
+        case DataType::F32:
+            _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col<float, false> : &NEIm2ColKernel::run_im2col<float, true>;
+            break;
 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-            case DataType::F16:
-                _func = &NEIm2ColKernel::run_reduced<float16_t>;
-                break;
+        case DataType::F16:
+            _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col<float16_t, false> : &NEIm2ColKernel::run_im2col<float16_t, true>;
+            break;
 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
-            case DataType::QASYMM8:
-                _func = &NEIm2ColKernel::run_reduced<qasymm8_t>;
-                break;
-            default:
-                ARM_COMPUTE_ERROR("Data type not supported");
-                break;
-        }
+        case DataType::QASYMM8:
+            _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col<qasymm8_t, false> : &NEIm2ColKernel::run_im2col<qasymm8_t, true>;
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Data type not supported");
+            break;
     }
-    else
-    {
-        switch(_input->info()->data_type())
-        {
-            case DataType::F32:
-                _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_generic<float, false> : &NEIm2ColKernel::run_generic<float, true>;
-                break;
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-            case DataType::F16:
-                _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_generic<float16_t, false> : &NEIm2ColKernel::run_generic<float16_t, true>;
-                break;
-#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
-            case DataType::QASYMM8:
-                _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_generic<qasymm8_t, false> : &NEIm2ColKernel::run_generic<qasymm8_t, true>;
-                break;
-            default:
-                ARM_COMPUTE_ERROR("Data type not supported");
-                break;
-        }
-        window.set(width_idx, Window::Dimension(0, _convolved_dims.first, 1));
-        window.set(height_idx, Window::Dimension(0, _convolved_dims.second, 1));
-        window.set(channel_idx, Window::Dimension(0, 1, 1));
-    }
-
-    // The NEIm2ColKernel doesn't need padding so update_window_and_padding() can be skipped
-    output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape()));
 
-    IKernel::configure(window);
+    // Configure kernel window
+    auto win_config = validate_and_configure_window(input->info(), output->info(), kernel_dims, conv_info, has_bias, dilation);
+    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+    INEKernel::configure(win_config.second);
 }
 
 Status NEIm2ColKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info,
-                                bool has_bias, const Size2D &dilation, unsigned int num_groups, bool is_fully_connected, bool is_flatten)
+                                bool has_bias, const Size2D &dilation, unsigned int num_groups)
 {
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, kernel_dims, conv_info, has_bias, dilation, num_groups, is_fully_connected, is_flatten));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, kernel_dims, conv_info, has_bias, dilation, num_groups));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get(), kernel_dims, conv_info, has_bias, dilation).first);
     return Status{};
 }
 
diff --git a/src/runtime/NEON/functions/NEFlattenLayer.cpp b/src/runtime/NEON/functions/NEFlattenLayer.cpp
index 1814d61e2f..57bef2b933 100644
--- a/src/runtime/NEON/functions/NEFlattenLayer.cpp
+++ b/src/runtime/NEON/functions/NEFlattenLayer.cpp
@@ -23,7 +23,7 @@
  */
 #include "arm_compute/runtime/NEON/functions/NEFlattenLayer.h"
 
-#include "arm_compute/core/NEON/kernels/NEIm2ColKernel.h"
+#include "arm_compute/core/NEON/kernels/NEFlattenLayerKernel.h"
 #include "arm_compute/core/Size2D.h"
 #include "support/ToolchainSupport.h"
 
@@ -31,7 +31,12 @@ using namespace arm_compute;
 
 void NEFlattenLayer::configure(const ITensor *input, ITensor *output)
 {
-    auto k = arm_compute::support::cpp14::make_unique<NEIm2ColKernel>();
-    k->configure(input, output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false, Size2D(1U, 1U), 1, false, true);
+    auto k = arm_compute::support::cpp14::make_unique<NEFlattenLayerKernel>();
+    k->configure(input, output);
     _kernel = std::move(k);
+}
+
+Status NEFlattenLayer::validate(const ITensorInfo *input, const ITensorInfo *output)
+{
+    return NEFlattenLayerKernel::validate(input, output);
 }
\ No newline at end of file
diff --git a/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp
index f1606aa93e..60f6294394 100644
--- a/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp
+++ b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp
@@ -74,8 +74,8 @@ Status NEFullyConnectedLayerReshapeWeights::validate(const ITensorInfo *input, c
 }
 
 NEFullyConnectedLayer::NEFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager)
-    : _memory_group(std::move(memory_manager)), _im2col_kernel(), _convert_weights(), _reshape_weights_function(), _mm_gemm(), _mm_gemmlowp(), _gemmlowp_output_stage(), _accumulate_biases_kernel(),
-      _im2col_output(), _gemmlowp_output(), _converted_weights_output(), _reshape_weights_output(), _original_weights(nullptr), _are_weights_converted(true), _are_weights_reshaped(false),
+    : _memory_group(std::move(memory_manager)), _flatten_kernel(), _convert_weights(), _reshape_weights_function(), _mm_gemm(), _mm_gemmlowp(), _gemmlowp_output_stage(), _accumulate_biases_kernel(),
+      _flatten_output(), _gemmlowp_output(), _converted_weights_output(), _reshape_weights_output(), _original_weights(nullptr), _are_weights_converted(true), _are_weights_reshaped(false),
       _is_fc_after_conv(false), _accumulate_biases(false), _is_quantized(false), _is_prepared(false)
 {
 }
@@ -112,19 +112,19 @@ void NEFullyConnectedLayer::configure_conv_fc(const ITensor *input, const ITenso
 
     // If the fully connected layer is called after a convolution layer, the input tensor must be linearized
 
-    // Initialize output tensor for im2col
-    TensorShape shape_im2col = compute_flatten_shape(input->info());
-    _im2col_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
+    // Initialize output tensor for flatten
+    TensorShape shape_flatten = compute_flatten_shape(input->info());
+    _flatten_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_flatten));
 
-    // Configure im2col kernel
-    _memory_group.manage(&_im2col_output);
-    _im2col_kernel.configure(input, &_im2col_output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false, Size2D(1U, 1U), 1, true);
+    // Configure flatten kernel
+    _memory_group.manage(&_flatten_output);
+    _flatten_kernel.configure(input, &_flatten_output);
 
     // Configure matrix multiply kernel
-    configure_mm(&_im2col_output, weights, output);
+    configure_mm(&_flatten_output, weights, output);
 
-    // Allocate the output tensor for im2col once all the configure methods have been called
-    _im2col_output.allocator()->allocate();
+    // Allocate the output tensor for flatten once all the configure methods have been called
+    _flatten_output.allocator()->allocate();
 }
 
 void NEFullyConnectedLayer::configure_fc_fc(const ITensor *input, const ITensor *weights, ITensor *output)
@@ -249,7 +249,7 @@ Status NEFullyConnectedLayer::validate(const ITensorInfo *input, const ITensorIn
     bool is_fc_after_conv = true;
     bool is_quantized     = is_data_type_quantized_asymmetric(input->data_type());
 
-    const ITensorInfo &im2col_input      = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_flatten_shape(input)));
+    const ITensorInfo &flatten_input     = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_flatten_shape(input)));
     const ITensorInfo &reshaped_weights  = TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_transposed_shape(*weights)));
     const ITensorInfo &converted_weights = weights_reshaped ? TensorInfo(weights->clone()->set_is_resizable(true).reset_padding()) : TensorInfo(*reshaped_weights.clone());
     const ITensorInfo &gemmlowp_output   = TensorInfo(output->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32));
@@ -307,9 +307,9 @@ Status NEFullyConnectedLayer::validate(const ITensorInfo *input, const ITensorIn
         // Fully Connected layer after a Convolution Layer without batches
         ARM_COMPUTE_RETURN_ERROR_ON((weights_to_use->dimension(1) != (input->dimension(0) * input->dimension(1) * input->dimension(2))));
 
-        // Validate im2col kernel
-        ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, &im2col_input, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false, Size2D(1U, 1U), 1, true));
-        input_to_use = &im2col_input;
+        // Validate flatten kernel
+        ARM_COMPUTE_RETURN_ON_ERROR(NEFlattenLayerKernel::validate(input, &flatten_input));
+        input_to_use = &flatten_input;
     }
     else
     {
@@ -337,7 +337,7 @@ void NEFullyConnectedLayer::run()
     // Linearize input if it comes from a convolutional layer
     if(_is_fc_after_conv)
     {
-        NEScheduler::get().schedule(&_im2col_kernel, Window::DimY);
+        NEScheduler::get().schedule(&_flatten_kernel, Window::DimY);
     }
 
     // Run matrix multiply
diff --git a/src/runtime/NEON/functions/NEIm2Col.cpp b/src/runtime/NEON/functions/NEIm2Col.cpp
index 4245b650e2..9102fca7f6 100644
--- a/src/runtime/NEON/functions/NEIm2Col.cpp
+++ b/src/runtime/NEON/functions/NEIm2Col.cpp
@@ -34,18 +34,17 @@ NEIm2Col::NEIm2Col()
 {
 }
 
-void NEIm2Col::configure(const ITensor *input, ITensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation, unsigned int num_groups,
-                         bool is_fully_connected, bool is_flatten)
+void NEIm2Col::configure(const ITensor *input, ITensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation, unsigned int num_groups)
 {
     _y_dim = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT);
 
-    _kernel.configure(input, output, kernel_dims, conv_info, has_bias, dilation, num_groups, is_fully_connected, is_flatten);
+    _kernel.configure(input, output, kernel_dims, conv_info, has_bias, dilation, num_groups);
 }
 
 Status NEIm2Col::validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation,
-                          unsigned int num_groups, bool is_fully_connected, bool is_flatten)
+                          unsigned int num_groups)
 {
-    return NEIm2ColKernel::validate(input, output, kernel_dims, conv_info, has_bias, dilation, num_groups, is_fully_connected, is_flatten);
+    return NEIm2ColKernel::validate(input, output, kernel_dims, conv_info, has_bias, dilation, num_groups);
 }
 
 void NEIm2Col::run()