diff --git a/arm_compute/core/Helpers.h b/arm_compute/core/Helpers.h
index 90dd608..5a8d6ef 100644
--- a/arm_compute/core/Helpers.h
+++ b/arm_compute/core/Helpers.h
@@ -24,23 +24,17 @@
 #ifndef ARM_COMPUTE_HELPERS_H
 #define ARM_COMPUTE_HELPERS_H
 
-#include "arm_compute/core/Coordinates.h"
 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/IAccessWindow.h"
-#include "arm_compute/core/Steps.h"
-#include "arm_compute/core/Strides.h"
-#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/ITensor.h"
 #include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
 #include "arm_compute/core/Window.h"
-#include "support/MemorySupport.h"
 
 #include <array>
 #include <cstddef>
 #include <cstdint>
-#include <memory>
 #include <tuple>
-#include <type_traits>
-#include <utility>
 
 namespace arm_compute
 {
@@ -48,307 +42,6 @@
 class ITensor;
 class ITensorInfo;
 
-/** Disable bitwise operations by default */
-template <typename T>
-struct enable_bitwise_ops
-{
-    static constexpr bool value = false; /**< Disabled */
-};
-
-#ifndef DOXYGEN_SKIP_THIS
-template <typename T>
-typename std::enable_if<enable_bitwise_ops<T>::value, T>::type operator&(T lhs, T rhs)
-{
-    using underlying_type = typename std::underlying_type<T>::type;
-    return static_cast<T>(static_cast<underlying_type>(lhs) & static_cast<underlying_type>(rhs));
-}
-#endif /* DOXYGEN_SKIP_THIS */
-
-/** Helper function to create and return a unique_ptr pointed to a CL/GLES kernel object
- *  It also calls the kernel's configuration.
- *
- * @param[in] args All the arguments that need pass to kernel's configuration.
- *
- * @return A unique pointer pointed to a CL/GLES kernel object
- */
-template <typename Kernel, typename... T>
-std::unique_ptr<Kernel> create_configure_kernel(T &&... args)
-{
-    std::unique_ptr<Kernel> k = arm_compute::support::cpp14::make_unique<Kernel>();
-    k->configure(std::forward<T>(args)...);
-    return k;
-}
-
-/** Helper function to create and return a unique_ptr pointed to a CL/GLES kernel object
- *
- * @return A unique pointer pointed to a Kernel kernel object
- */
-template <typename Kernel>
-std::unique_ptr<Kernel> create_kernel()
-{
-    std::unique_ptr<Kernel> k = arm_compute::support::cpp14::make_unique<Kernel>();
-    return k;
-}
-
-namespace traits
-{
-/** Check if a type T is contained in a tuple Tuple of types */
-template <typename T, typename Tuple>
-struct is_contained;
-
-template <typename T>
-struct is_contained<T, std::tuple<>> : std::false_type
-{
-};
-
-template <typename T, typename... Ts>
-struct is_contained<T, std::tuple<T, Ts...>> : std::true_type
-{
-};
-
-template <typename T, typename U, typename... Ts>
-struct is_contained<T, std::tuple<U, Ts...>> : is_contained<T, std::tuple<Ts...>>
-{
-};
-}
-
-/** Computes bilinear interpolation using the pointer to the top-left pixel and the pixel's distance between
- * the real coordinates and the smallest following integer coordinates. Input must be in single channel format.
- *
- * @param[in] pixel_ptr Pointer to the top-left pixel value of a single channel input.
- * @param[in] stride    Stride to access the bottom-left and bottom-right pixel values
- * @param[in] dx        Pixel's distance between the X real coordinate and the smallest X following integer
- * @param[in] dy        Pixel's distance between the Y real coordinate and the smallest Y following integer
- *
- * @note dx and dy must be in the range [0, 1.0]
- *
- * @return The bilinear interpolated pixel value
- */
-template <typename T>
-inline T delta_bilinear_c1(const T *pixel_ptr, size_t stride, float dx, float dy)
-{
-    ARM_COMPUTE_ERROR_ON(pixel_ptr == nullptr);
-
-    const float dx1 = 1.0f - dx;
-    const float dy1 = 1.0f - dy;
-
-    const T a00 = *pixel_ptr;
-    const T a01 = *(pixel_ptr + 1);
-    const T a10 = *(pixel_ptr + stride);
-    const T a11 = *(pixel_ptr + stride + 1);
-
-    const float w1 = dx1 * dy1;
-    const float w2 = dx * dy1;
-    const float w3 = dx1 * dy;
-    const float w4 = dx * dy;
-
-    return static_cast<T>(a00 * w1 + a01 * w2 + a10 * w3 + a11 * w4);
-}
-
-/** Computes bilinear interpolation for quantized input and output, using the pointer to the top-left pixel and the pixel's distance between
- * the real coordinates and the smallest following integer coordinates. Input must be QASYMM8 and in single channel format.
- *
- * @param[in] pixel_ptr Pointer to the top-left pixel value of a single channel input.
- * @param[in] stride    Stride to access the bottom-left and bottom-right pixel values
- * @param[in] dx        Pixel's distance between the X real coordinate and the smallest X following integer
- * @param[in] dy        Pixel's distance between the Y real coordinate and the smallest Y following integer
- * @param[in] iq_info   Input QuantizationInfo
- * @param[in] oq_info   Output QuantizationInfo
- *
- * @note dx and dy must be in the range [0, 1.0]
- *
- * @return The bilinear interpolated pixel value
- */
-inline uint8_t delta_bilinear_c1_quantized(const uint8_t *pixel_ptr, size_t stride, float dx, float dy, UniformQuantizationInfo iq_info, UniformQuantizationInfo oq_info)
-{
-    ARM_COMPUTE_ERROR_ON(pixel_ptr == nullptr);
-
-    const float dx1 = 1.0f - dx;
-    const float dy1 = 1.0f - dy;
-
-    const float a00 = dequantize_qasymm8(*pixel_ptr, iq_info);
-    const float a01 = dequantize_qasymm8(*(pixel_ptr + 1), iq_info);
-    const float a10 = dequantize_qasymm8(*(pixel_ptr + stride), iq_info);
-    const float a11 = dequantize_qasymm8(*(pixel_ptr + stride + 1), iq_info);
-
-    const float w1  = dx1 * dy1;
-    const float w2  = dx * dy1;
-    const float w3  = dx1 * dy;
-    const float w4  = dx * dy;
-    float       res = a00 * w1 + a01 * w2 + a10 * w3 + a11 * w4;
-    return static_cast<uint8_t>(quantize_qasymm8(res, oq_info));
-}
-
-/** Computes bilinear interpolation for quantized input and output, using the pointer to the top-left pixel and the pixel's distance between
- * the real coordinates and the smallest following integer coordinates. Input must be QASYMM8_SIGNED and in single channel format.
- *
- * @param[in] pixel_ptr Pointer to the top-left pixel value of a single channel input.
- * @param[in] stride    Stride to access the bottom-left and bottom-right pixel values
- * @param[in] dx        Pixel's distance between the X real coordinate and the smallest X following integer
- * @param[in] dy        Pixel's distance between the Y real coordinate and the smallest Y following integer
- * @param[in] iq_info   Input QuantizationInfo
- * @param[in] oq_info   Output QuantizationInfo
- *
- * @note dx and dy must be in the range [0, 1.0]
- *
- * @return The bilinear interpolated pixel value
- */
-inline int8_t delta_bilinear_c1_quantized(const int8_t *pixel_ptr, size_t stride, float dx, float dy, UniformQuantizationInfo iq_info, UniformQuantizationInfo oq_info)
-{
-    ARM_COMPUTE_ERROR_ON(pixel_ptr == nullptr);
-
-    const float dx1 = 1.0f - dx;
-    const float dy1 = 1.0f - dy;
-
-    const float a00 = dequantize_qasymm8_signed(*pixel_ptr, iq_info);
-    const float a01 = dequantize_qasymm8_signed(*(pixel_ptr + 1), iq_info);
-    const float a10 = dequantize_qasymm8_signed(*(pixel_ptr + stride), iq_info);
-    const float a11 = dequantize_qasymm8_signed(*(pixel_ptr + stride + 1), iq_info);
-
-    const float w1  = dx1 * dy1;
-    const float w2  = dx * dy1;
-    const float w3  = dx1 * dy;
-    const float w4  = dx * dy;
-    float       res = a00 * w1 + a01 * w2 + a10 * w3 + a11 * w4;
-    return static_cast<int8_t>(quantize_qasymm8_signed(res, oq_info));
-}
-
-/** Computes linear interpolation using the pointer to the top pixel and the pixel's distance between
- * the real coordinates and the smallest following integer coordinates. Input must be in single channel format.
- *
- * @param[in] pixel_ptr Pointer to the top pixel value of a single channel input.
- * @param[in] stride    Stride to access the bottom pixel value
- * @param[in] dy        Pixel's distance between the Y real coordinate and the smallest Y following integer
- *
- * @note dy must be in the range [0, 1.0]
- *
- * @return The linear interpolated pixel value
- */
-template <typename T>
-inline T delta_linear_c1_y(const T *pixel_ptr, size_t stride, float dy)
-{
-    ARM_COMPUTE_ERROR_ON(pixel_ptr == nullptr);
-
-    const float dy1 = 1.0f - dy;
-
-    const T a00 = *pixel_ptr;
-    const T a10 = *(pixel_ptr + stride);
-
-    const float w1 = dy1;
-    const float w3 = dy;
-
-    return static_cast<T>(a00 * w1 + a10 * w3);
-}
-/** Computes linear interpolation using the pointer to the left pixel and the pixel's distance between
- * the real coordinates and the smallest following integer coordinates. Input must be in single channel format.
- *
- * @param[in] pixel_ptr Pointer to the left pixel value of a single channel input.
- * @param[in] dx        Pixel's distance between the X real coordinate and the smallest X following integer
- *
- * @note dx must be in the range [0, 1.0]
- *
- * @return The linear interpolated pixel value
- */
-template <typename T>
-inline T delta_linear_c1_x(const T *pixel_ptr, float dx)
-{
-    ARM_COMPUTE_ERROR_ON(pixel_ptr == nullptr);
-
-    const T a00 = *pixel_ptr;
-    const T a01 = *(pixel_ptr + 1);
-
-    const float dx1 = 1.0f - dx;
-
-    const float w1 = dx1;
-    const float w2 = dx;
-
-    return static_cast<T>(a00 * w1 + a01 * w2);
-}
-/** Return the pixel at (x,y) using bilinear interpolation.
- *
- * @warning Only works if the iterator was created with an IImage
- *
- * @param[in] first_pixel_ptr Pointer to the first pixel of a single channel input.
- * @param[in] stride          Stride in bytes of the image;
- * @param[in] x               X position of the wanted pixel
- * @param[in] y               Y position of the wanted pixel
- *
- * @return The pixel at (x, y) using bilinear interpolation.
- */
-template <typename T>
-inline T pixel_bilinear_c1(const T *first_pixel_ptr, size_t stride, float x, float y)
-{
-    ARM_COMPUTE_ERROR_ON(first_pixel_ptr == nullptr);
-
-    const int32_t xi = std::floor(x);
-    const int32_t yi = std::floor(y);
-
-    const float dx = x - xi;
-    const float dy = y - yi;
-
-    return delta_bilinear_c1(first_pixel_ptr + xi + yi * stride, stride, dx, dy);
-}
-
-/** Return the pixel at (x,y) using bilinear interpolation by clamping when out of borders. The image must be single channel input
- *
- * @warning Only works if the iterator was created with an IImage
- *
- * @param[in] first_pixel_ptr Pointer to the first pixel of a single channel image.
- * @param[in] stride          Stride in bytes of the image
- * @param[in] width           Width of the image
- * @param[in] height          Height of the image
- * @param[in] x               X position of the wanted pixel
- * @param[in] y               Y position of the wanted pixel
- *
- * @return The pixel at (x, y) using bilinear interpolation.
- */
-template <typename T>
-inline uint8_t pixel_bilinear_c1_clamp(const T *first_pixel_ptr, size_t stride, size_t width, size_t height, float x, float y)
-{
-    ARM_COMPUTE_ERROR_ON(first_pixel_ptr == nullptr);
-
-    x = std::max(-1.f, std::min(x, static_cast<float>(width)));
-    y = std::max(-1.f, std::min(y, static_cast<float>(height)));
-
-    const float xi = std::floor(x);
-    const float yi = std::floor(y);
-
-    const float dx = x - xi;
-    const float dy = y - yi;
-
-    if(dx == 0.0f)
-    {
-        if(dy == 0.0f)
-        {
-            return static_cast<T>(first_pixel_ptr[static_cast<int32_t>(xi) + static_cast<int32_t>(yi) * stride]);
-        }
-        return delta_linear_c1_y(first_pixel_ptr + static_cast<int32_t>(xi) + static_cast<int32_t>(yi) * stride, stride, dy);
-    }
-    if(dy == 0.0f)
-    {
-        return delta_linear_c1_x(first_pixel_ptr + static_cast<int32_t>(xi) + static_cast<int32_t>(yi) * stride, dx);
-    }
-    return delta_bilinear_c1(first_pixel_ptr + static_cast<int32_t>(xi) + static_cast<int32_t>(yi) * stride, stride, dx, dy);
-}
-
-/** Return the pixel at (x,y) using area interpolation by clamping when out of borders. The image must be single channel U8
- *
- * @note The interpolation area depends on the width and height ration of the input and output images
- * @note Currently average of the contributing pixels is calculated
- *
- * @param[in] first_pixel_ptr Pointer to the first pixel of a single channel U8 image.
- * @param[in] stride          Stride in bytes of the image
- * @param[in] width           Width of the image
- * @param[in] height          Height of the image
- * @param[in] wr              Width ratio among the input image width and output image width.
- * @param[in] hr              Height ratio among the input image height and output image height.
- * @param[in] x               X position of the wanted pixel
- * @param[in] y               Y position of the wanted pixel
- *
- * @return The pixel at (x, y) using area interpolation.
- */
-inline uint8_t pixel_area_c1u8_clamp(const uint8_t *first_pixel_ptr, size_t stride, size_t width, size_t height, float wr, float hr, int x, int y);
-
 /** Iterator updated by @ref execute_window_loop for each window element */
 class Iterator
 {
@@ -421,179 +114,6 @@
 template <typename L, typename... Ts>
 inline void execute_window_loop(const Window &w, L &&lambda_function, Ts &&... iterators);
 
-/** Update window and padding size for each of the access patterns.
- *
- * First the window size is reduced based on all access patterns that are not
- * allowed to modify the padding of the underlying tensor. Then the padding of
- * the remaining tensors is increased to match the window.
- *
- * @param[in] win      Window that is used by the kernel.
- * @param[in] patterns Access patterns used to calculate the final window and padding.
- *
- * @return True if the window has been changed. Changes to the padding do not
- *         influence the returned value.
- */
-template <typename... Ts>
-bool update_window_and_padding(Window &win, Ts &&... patterns)
-{
-    bool window_changed = false;
-
-    utility::for_each([&](const IAccessWindow & w)
-    {
-        window_changed |= w.update_window_if_needed(win);
-    },
-    patterns...);
-
-    bool padding_changed = false;
-
-    utility::for_each([&](IAccessWindow & w)
-    {
-        padding_changed |= w.update_padding_if_needed(win);
-    },
-    patterns...);
-
-    return window_changed;
-}
-
-/** Calculate the maximum window for a given tensor shape and border setting
- *
- * @param[in] valid_region Valid region object defining the shape of the tensor space for which the window is created.
- * @param[in] steps        (Optional) Number of elements processed for each step.
- * @param[in] skip_border  (Optional) If true exclude the border region from the window.
- * @param[in] border_size  (Optional) Border size.
- *
- * @return The maximum window the kernel can be executed on.
- */
-Window calculate_max_window(const ValidRegion &valid_region, const Steps &steps = Steps(), bool skip_border = false, BorderSize border_size = BorderSize());
-
-/** Calculate the maximum window for a given tensor shape and border setting
- *
- * @param[in] info        Tensor info object defining the shape of the object for which the window is created.
- * @param[in] steps       (Optional) Number of elements processed for each step.
- * @param[in] skip_border (Optional) If true exclude the border region from the window.
- * @param[in] border_size (Optional) Border size.
- *
- * @return The maximum window the kernel can be executed on.
- */
-inline Window calculate_max_window(const ITensorInfo &info, const Steps &steps = Steps(), bool skip_border = false, BorderSize border_size = BorderSize())
-{
-    return calculate_max_window(info.valid_region(), steps, skip_border, border_size);
-}
-
-/** Calculate the maximum window used by a horizontal kernel for a given tensor shape and border setting
- *
- * @param[in] valid_region Valid region object defining the shape of the tensor space for which the window is created.
- * @param[in] steps        (Optional) Number of elements processed for each step.
- * @param[in] skip_border  (Optional) If true exclude the border region from the window.
- * @param[in] border_size  (Optional) Border size. The border region will be excluded from the window.
- *
- * @return The maximum window the kernel can be executed on.
- */
-Window calculate_max_window_horizontal(const ValidRegion &valid_region, const Steps &steps = Steps(), bool skip_border = false, BorderSize border_size = BorderSize());
-
-/** Calculate the maximum window used by a horizontal kernel for a given tensor shape and border setting
- *
- * @param[in] info        Tensor info object defining the shape of the object for which the window is created.
- * @param[in] steps       (Optional) Number of elements processed for each step.
- * @param[in] skip_border (Optional) If true exclude the border region from the window.
- * @param[in] border_size (Optional) Border size.
- *
- * @return The maximum window the kernel can be executed on.
- */
-inline Window calculate_max_window_horizontal(const ITensorInfo &info, const Steps &steps = Steps(), bool skip_border = false, BorderSize border_size = BorderSize())
-{
-    return calculate_max_window_horizontal(info.valid_region(), steps, skip_border, border_size);
-}
-
-/** Calculate the maximum window for a given tensor shape and border setting. The window will also includes the border.
- *
- * @param[in] valid_region Valid region object defining the shape of the tensor space for which the window is created.
- * @param[in] steps        (Optional) Number of elements processed for each step.
- * @param[in] border_size  (Optional) Border size. The border region will be included in the window.
- *
- * @return The maximum window the kernel can be executed on.
- */
-Window calculate_max_enlarged_window(const ValidRegion &valid_region, const Steps &steps = Steps(), BorderSize border_size = BorderSize());
-
-/** Calculate the maximum window for a given tensor shape and border setting. The window will also includes the border.
- *
- * @param[in] info        Tensor info object defining the shape of the object for which the window is created.
- * @param[in] steps       (Optional) Number of elements processed for each step.
- * @param[in] border_size (Optional) Border size. The border region will be included in the window.
- *
- * @return The maximum window the kernel can be executed on.
- */
-inline Window calculate_max_enlarged_window(const ITensorInfo &info, const Steps &steps = Steps(), BorderSize border_size = BorderSize())
-{
-    return calculate_max_enlarged_window(info.valid_region(), steps, border_size);
-}
-
-/** Intersect multiple valid regions.
- *
- * @param[in] regions Valid regions.
- *
- * @return Intersection of all regions.
- */
-template <typename... Ts>
-ValidRegion intersect_valid_regions(const Ts &... regions)
-{
-    auto intersect = [](const ValidRegion & r1, const ValidRegion & r2) -> ValidRegion
-    {
-        ValidRegion region;
-
-        for(size_t d = 0; d < std::min(r1.anchor.num_dimensions(), r2.anchor.num_dimensions()); ++d)
-        {
-            region.anchor.set(d, std::max(r1.anchor[d], r2.anchor[d]));
-        }
-
-        for(size_t d = 0; d < std::min(r1.shape.num_dimensions(), r2.shape.num_dimensions()); ++d)
-        {
-            region.shape.set(d, std::min(r1.shape[d], r2.shape[d]));
-        }
-
-        return region;
-    };
-
-    return utility::foldl(intersect, regions...);
-}
-
-/** Create a strides object based on the provided strides and the tensor dimensions.
- *
- * @param[in] info          Tensor info object providing the shape of the tensor for unspecified strides.
- * @param[in] stride_x      Stride to be used in X dimension (in bytes).
- * @param[in] fixed_strides Strides to be used in higher dimensions starting at Y (in bytes).
- *
- * @return Strides object based on the specified strides. Missing strides are
- *         calculated based on the tensor shape and the strides of lower dimensions.
- */
-template <typename T, typename... Ts>
-inline Strides compute_strides(const ITensorInfo &info, T stride_x, Ts &&... fixed_strides)
-{
-    const TensorShape &shape = info.tensor_shape();
-
-    // Create strides object
-    Strides strides(stride_x, fixed_strides...);
-
-    for(size_t i = 1 + sizeof...(Ts); i < info.num_dimensions(); ++i)
-    {
-        strides.set(i, shape[i - 1] * strides[i - 1]);
-    }
-
-    return strides;
-}
-
-/** Create a strides object based on the tensor dimensions.
- *
- * @param[in] info Tensor info object used to compute the strides.
- *
- * @return Strides object based on element size and tensor shape.
- */
-template <typename... Ts>
-inline Strides compute_strides(const ITensorInfo &info)
-{
-    return compute_strides(info, info.element_size());
-}
-
 /** Permutes given Dimensions according to a permutation vector
  *
  * @warning Validity of permutation is not checked
@@ -629,79 +149,6 @@
     }
 }
 
-/** Auto initialize the tensor info (shape, number of channels and data type) if the current assignment is empty.
- *
- * @param[in,out] info              Tensor info used to check and assign.
- * @param[in]     shape             New shape.
- * @param[in]     num_channels      New number of channels.
- * @param[in]     data_type         New data type
- * @param[in]     quantization_info (Optional) New quantization info
- *
- * @return True if the tensor info has been initialized
- */
-bool auto_init_if_empty(ITensorInfo       &info,
-                        const TensorShape &shape,
-                        int num_channels, DataType data_type,
-                        QuantizationInfo quantization_info = QuantizationInfo());
-
-/** Auto initialize the tensor info using another tensor info.
- *
- * @param info_sink   Tensor info used to check and assign
- * @param info_source Tensor info used to assign
- *
- * @return True if the tensor info has been initialized
- */
-bool auto_init_if_empty(ITensorInfo &info_sink, const ITensorInfo &info_source);
-
-/** Set the shape to the specified value if the current assignment is empty.
- *
- * @param[in,out] info  Tensor info used to check and assign.
- * @param[in]     shape New shape.
- *
- * @return True if the shape has been changed.
- */
-bool set_shape_if_empty(ITensorInfo &info, const TensorShape &shape);
-
-/** Set the format, data type and number of channels to the specified value if
- * the current data type is unknown.
- *
- * @param[in,out] info   Tensor info used to check and assign.
- * @param[in]     format New format.
- *
- * @return True if the format has been changed.
- */
-bool set_format_if_unknown(ITensorInfo &info, Format format);
-
-/** Set the data type and number of channels to the specified value if
- * the current data type is unknown.
- *
- * @param[in,out] info      Tensor info used to check and assign.
- * @param[in]     data_type New data type.
- *
- * @return True if the data type has been changed.
- */
-bool set_data_type_if_unknown(ITensorInfo &info, DataType data_type);
-
-/** Set the data layout to the specified value if
- * the current data layout is unknown.
- *
- * @param[in,out] info        Tensor info used to check and assign.
- * @param[in]     data_layout New data layout.
- *
- * @return True if the data type has been changed.
- */
-bool set_data_layout_if_unknown(ITensorInfo &info, DataLayout data_layout);
-
-/** Set the quantization info to the specified value if
- * the current quantization info is empty and the data type of asymmetric quantized type
- *
- * @param[in,out] info              Tensor info used to check and assign.
- * @param[in]     quantization_info Quantization info
- *
- * @return True if the quantization info has been changed.
- */
-bool set_quantization_info_if_empty(ITensorInfo &info, QuantizationInfo quantization_info);
-
 /** Helper function to calculate the Valid Region for Scale.
  *
  * @param[in] src_info           Input tensor info used to check.
@@ -751,21 +198,6 @@
  */
 inline DataLayoutDimension get_index_data_layout_dimension(const DataLayout data_layout, const size_t index);
 
-/** Calculate the normalization dimension index for a given normalization type
- *
- * @param[in] layout Data layout of the input and output tensor
- * @param[in] info   Normalization info
- *
- * @return Normalization dimension index
- */
-inline unsigned int get_normalization_dimension_index(DataLayout layout, const NormalizationLayerInfo &info)
-{
-    const unsigned int width_idx   = get_data_layout_dimension_index(layout, DataLayoutDimension::WIDTH);
-    const unsigned int channel_idx = get_data_layout_dimension_index(layout, DataLayoutDimension::CHANNEL);
-
-    return info.is_in_map() ? width_idx : channel_idx;
-}
-
 /** Calculate the number of output tiles required by Winograd Convolution layer. This utility function can be used by the Winograd input transform
  *  to know the number of tiles on the x and y direction
  *
@@ -814,49 +246,6 @@
     }
     return coords;
 }
-
-/** Given an integer value, this function returns the next power of two
- *
- * @param[in] x Input value
- *
- * @return the next power of two
- */
-inline unsigned int get_next_power_two(unsigned int x)
-{
-    // Decrement by 1
-    x--;
-
-    // Shift right by 1
-    x |= x >> 1u;
-    // Shift right by 2
-    x |= x >> 2u;
-    // Shift right by 4
-    x |= x >> 4u;
-    // Shift right by 8
-    x |= x >> 8u;
-    // Shift right by 16
-    x |= x >> 16u;
-
-    // Increment by 1
-    x++;
-
-    return x;
-}
-
-/** Given a softmax axis, this function returns the permutation vector required to put the axis to the front
- *
- * @note This function assumes a tensor rank <= 4
- *
- * Axis selects the dimension on which softmax is performed.
- * E.g. For input of shape 4x5x6 and axis=1, softmax will be applied to 4x6=24 vectors of size 5.
- * Interally softmax kernels is always performed on the first dimension (front dimension), therefore permutation is
- * required to put the dimension specified by @p axis to the first dimension.
- *
- * @param[in] axis Axis on which to perform softmax. Supported: 1, 2, 3 (0 implies no permutation needed)
- *
- * @return the permutation vector
- */
-PermutationVector get_permutation_vector_from_softmax_axis(size_t axis);
 } // namespace arm_compute
 
 #include "arm_compute/core/Helpers.inl"