COMPMID-1717: CL: Implement Maximum, Minimum, SquaredDifference

Change-Id: Ice653e48211053bd3cd20a693bd76de6b4efc370
Reviewed-on: https://review.mlplatform.org/270
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

9 files changed, 562 insertions, 1021 deletions

diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index f2b5d45e2c..ac1d4b349e 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -149,11 +149,6 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map =
     { "accumulate_weighted", "accumulate.cl" },
     { "activation_layer", "activation_layer.cl" },
     { "activation_layer_qa8", "activation_layer_qa8.cl" },
-    { "arithmetic_add_quantized", "arithmetic_op_quantized.cl" },
-    { "arithmetic_add", "arithmetic_op.cl" },
-    { "arithmetic_sub", "arithmetic_op.cl" },
-    { "arithmetic_sub_quantized", "arithmetic_op_quantized.cl" },
-    { "arithmetic_div", "arithmetic_op.cl" },
     { "batch_to_space_nchw", "batch_to_space.cl" },
     { "batch_to_space_static_nchw", "batch_to_space.cl" },
     { "batch_to_space_nhwc", "batch_to_space.cl" },
@@ -246,6 +241,18 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map =
     { "direct_convolution5x5_nhwc", "direct_convolution5x5.cl" },
     { "direct_convolution5x5_f32_bifrost", "direct_convolution5x5.cl" },
     { "direct_convolution_1x1_3x3_5x5_quantized", "direct_convolution_1x1_3x3_5x5_quantized.cl" },
+    { "elementwise_operation_ADD", "elementwise_operation.cl" },
+    { "elementwise_operation_SUB", "elementwise_operation.cl" },
+    { "elementwise_operation_MAX", "elementwise_operation.cl" },
+    { "elementwise_operation_MIN", "elementwise_operation.cl" },
+    { "elementwise_operation_DIV", "elementwise_operation.cl" },
+    { "elementwise_operation_SQUARED_DIFF", "elementwise_operation.cl" },
+    { "elementwise_operation_ADD_quantized", "elementwise_operation_quantized.cl" },
+    { "elementwise_operation_SUB_quantized", "elementwise_operation_quantized.cl" },
+    { "elementwise_operation_MAX_quantized", "elementwise_operation_quantized.cl" },
+    { "elementwise_operation_MIN_quantized", "elementwise_operation_quantized.cl" },
+    { "elementwise_operation_DIV_quantized", "elementwise_operation_quantized.cl" },
+    { "elementwise_operation_SQUARED_DIFF_quantized", "elementwise_operation_quantized.cl" },
     { "erode", "erode.cl" },
     { "fast_corners", "fast_corners.cl" },
     { "flatten", "flatten.cl" },
@@ -510,14 +517,6 @@ const std::map<std::string, std::string> CLKernelLibrary::_program_source_map =
 #include "./cl_kernels/activation_layer_qa8.clembed"
     },
     {
-        "arithmetic_op.cl",
-#include "./cl_kernels/arithmetic_op.clembed"
-    },
-    {
-        "arithmetic_op_quantized.cl",
-#include "./cl_kernels/arithmetic_op_quantized.clembed"
-    },
-    {
         "batch_to_space.cl",
 #include "./cl_kernels/batch_to_space.clembed"
     },
@@ -642,6 +641,14 @@ const std::map<std::string, std::string> CLKernelLibrary::_program_source_map =
 #include "./cl_kernels/direct_convolution_1x1_3x3_5x5_quantized.clembed"
     },
     {
+        "elementwise_operation.cl",
+#include "./cl_kernels/elementwise_operation.clembed"
+    },
+    {
+        "elementwise_operation_quantized.cl",
+#include "./cl_kernels/elementwise_operation_quantized.clembed"
+    },
+    {
         "erode.cl",
 #include "./cl_kernels/erode.clembed"
     },
diff --git a/src/core/CL/cl_kernels/arithmetic_op.cl b/src/core/CL/cl_kernels/arithmetic_op.cl
deleted file mode 100644
index 557615e7f2..0000000000
--- a/src/core/CL/cl_kernels/arithmetic_op.cl
+++ /dev/null
@@ -1,190 +0,0 @@
-/*
- * Copyright (c) 2016-2018 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 "helpers.h"
-
-#ifdef SATURATE
-#define ADD(x, y) add_sat((x), (y))
-#define SUB(x, y) sub_sat((x), (y))
-#else /* SATURATE */
-#define ADD(x, y) (x) + (y)
-#define SUB(x, y) (x) - (y)
-#endif /* SATURATE */
-
-#define DIV(x, y) (x) / (y)
-
-#if defined(DATA_TYPE_IN1) && defined(DATA_TYPE_IN2) && defined(DATA_TYPE_OUT) && defined(VEC_SIZE)
-/** This function adds two tensors.
- *
- * @attention The input and output data_types need to be passed at compile time using -DDATA_TYPE_IN1, -DDATA_TYPE_IN2 and -DDATA_TYPE_OUT:
- * e.g. -DDATA_TYPE_IN1=uchar -DDATA_TYPE_IN2=uchar -DDATA_TYPE_OUT=short
- * @attention To perform saturating operation -DSATURATE has to be passed to the compiler otherwise wrapping policy will be used.
- * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16
- *
- * @param[in]  in1_ptr                           Pointer to the source tensor. Supported data types: U8/S16/F16/F32
- * @param[in]  in1_stride_x                      Stride of the source tensor in X dimension (in bytes)
- * @param[in]  in1_step_x                        in1_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  in1_stride_y                      Stride of the source tensor in Y dimension (in bytes)
- * @param[in]  in1_step_y                        in1_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  in1_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  in1_step_z                        in1_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  in1_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[in]  in2_ptr                           Pointer to the source tensor. Supported data types: U8/S16/F16/F32
- * @param[in]  in2_stride_x                      Stride of the source tensor in X dimension (in bytes)
- * @param[in]  in2_step_x                        in2_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  in2_stride_y                      Stride of the source tensor in Y dimension (in bytes)
- * @param[in]  in2_step_y                        in2_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  in2_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  in2_step_z                        in2_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  in2_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[out] out_ptr                           Pointer to the destination tensor. Supported data types: U8 (only if both inputs are U8), S16/F16/F32
- * @param[in]  out_stride_x                      Stride of the destination tensor in X dimension (in bytes)
- * @param[in]  out_step_x                        out_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  out_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
- * @param[in]  out_step_y                        out_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  out_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  out_step_z                        out_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  out_offset_first_element_in_bytes The offset of the first element in the destination tensor
- */
-__kernel void arithmetic_add(
-    TENSOR3D_DECLARATION(in1),
-    TENSOR3D_DECLARATION(in2),
-    TENSOR3D_DECLARATION(out))
-{
-    // Get pixels pointer
-    Tensor3D in1 = CONVERT_TO_TENSOR3D_STRUCT(in1);
-    Tensor3D in2 = CONVERT_TO_TENSOR3D_STRUCT(in2);
-    Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(out);
-
-    // Load values
-    VEC_DATA_TYPE(DATA_TYPE_OUT, VEC_SIZE)
-    in_a = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE_IN1 *)in1.ptr), VEC_DATA_TYPE(DATA_TYPE_OUT, VEC_SIZE));
-    VEC_DATA_TYPE(DATA_TYPE_OUT, VEC_SIZE)
-    in_b = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE_IN2 *)in2.ptr), VEC_DATA_TYPE(DATA_TYPE_OUT, VEC_SIZE));
-
-    // Calculate and store result
-    VSTORE(VEC_SIZE)
-    (ADD(in_a, in_b), 0, (__global DATA_TYPE_OUT *)out.ptr);
-}
-#endif /* defined(DATA_TYPE_IN1) && defined(DATA_TYPE_IN2) && defined(DATA_TYPE_OUT) && defined(VEC_SIZE) */
-
-/** This function subtracts one tensor from another.
- *
- * @attention The input and output data_types need to be passed at compile time using -DDATA_TYPE_IN1, -DDATA_TYPE_IN2 and -DDATA_TYPE_OUT:
- * e.g. -DDATA_TYPE_IN1=uchar -DDATA_TYPE_IN2=uchar -DDATA_TYPE_OUT=short
- * @attention To perform saturating operation -DSATURATE has to be passed to the compiler otherwise wrapping policy will be used.
- *
- * @param[in]  in1_ptr                           Pointer to the source tensor. Supported data types: U8, S16
- * @param[in]  in1_stride_x                      Stride of the source tensor in X dimension (in bytes)
- * @param[in]  in1_step_x                        in1_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  in1_stride_y                      Stride of the source tensor in Y dimension (in bytes)
- * @param[in]  in1_step_y                        in1_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  in1_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  in1_step_z                        in1_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  in1_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[in]  in2_ptr                           Pointer to the source tensor. Supported data types: U8, S16
- * @param[in]  in2_stride_x                      Stride of the source tensor in X dimension (in bytes)
- * @param[in]  in2_step_x                        in2_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  in2_stride_y                      Stride of the source tensor in Y dimension (in bytes)
- * @param[in]  in2_step_y                        in2_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  in2_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  in2_step_z                        in2_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  in2_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[out] out_ptr                           Pointer to the destination tensor. Supported data types: U8, S16
- * @param[in]  out_stride_x                      Stride of the destination tensor in X dimension (in bytes)
- * @param[in]  out_step_x                        out_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  out_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
- * @param[in]  out_step_y                        out_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  out_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  out_step_z                        out_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  out_offset_first_element_in_bytes The offset of the first element in the destination tensor
- */
-__kernel void arithmetic_sub(
-    TENSOR3D_DECLARATION(in1),
-    TENSOR3D_DECLARATION(in2),
-    TENSOR3D_DECLARATION(out))
-{
-    // Get pixels pointer
-    Tensor3D in1 = CONVERT_TO_TENSOR3D_STRUCT(in1);
-    Tensor3D in2 = CONVERT_TO_TENSOR3D_STRUCT(in2);
-    Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(out);
-
-    // Load values
-    VEC_DATA_TYPE(DATA_TYPE_OUT, 16)
-    in_a = CONVERT(vload16(0, (__global DATA_TYPE_IN1 *)in1.ptr), VEC_DATA_TYPE(DATA_TYPE_OUT, 16));
-    VEC_DATA_TYPE(DATA_TYPE_OUT, 16)
-    in_b = CONVERT(vload16(0, (__global DATA_TYPE_IN2 *)in2.ptr), VEC_DATA_TYPE(DATA_TYPE_OUT, 16));
-
-    // Calculate and store result
-    vstore16(SUB(in_a, in_b), 0, (__global DATA_TYPE_OUT *)out.ptr);
-}
-
-/** This function divides one tensor from another.
- *
- * @attention The input and output data_types need to be passed at compile time using -DDATA_TYPE_IN1, -DDATA_TYPE_IN2 and -DDATA_TYPE_OUT:
- * e.g. -DDATA_TYPE_IN1=float -DDATA_TYPE_IN2=float -DDATA_TYPE_OUT=float
- *
- * @param[in]  in1_ptr                           Pointer to the source tensor. Supported data types: F16/F32
- * @param[in]  in1_stride_x                      Stride of the source tensor in X dimension (in bytes)
- * @param[in]  in1_step_x                        in1_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  in1_stride_y                      Stride of the source tensor in Y dimension (in bytes)
- * @param[in]  in1_step_y                        in1_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  in1_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  in1_step_z                        in1_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  in1_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[in]  in2_ptr                           Pointer to the source tensor. Supported data types: Same as @p in1_ptr
- * @param[in]  in2_stride_x                      Stride of the source tensor in X dimension (in bytes)
- * @param[in]  in2_step_x                        in2_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  in2_stride_y                      Stride of the source tensor in Y dimension (in bytes)
- * @param[in]  in2_step_y                        in2_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  in2_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  in2_step_z                        in2_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  in2_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[out] out_ptr                           Pointer to the destination tensor. Supported data types: Same as @p in1_ptr
- * @param[in]  out_stride_x                      Stride of the destination tensor in X dimension (in bytes)
- * @param[in]  out_step_x                        out_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  out_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
- * @param[in]  out_step_y                        out_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  out_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  out_step_z                        out_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  out_offset_first_element_in_bytes The offset of the first element in the destination tensor
- */
-__kernel void arithmetic_div(
-    TENSOR3D_DECLARATION(in1),
-    TENSOR3D_DECLARATION(in2),
-    TENSOR3D_DECLARATION(out))
-{
-    // Get pixels pointer
-    Tensor3D in1 = CONVERT_TO_TENSOR3D_STRUCT(in1);
-    Tensor3D in2 = CONVERT_TO_TENSOR3D_STRUCT(in2);
-    Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(out);
-
-    // Load values
-    VEC_DATA_TYPE(DATA_TYPE_OUT, 16)
-    in_a = CONVERT(vload16(0, (__global DATA_TYPE_IN1 *)in1.ptr), VEC_DATA_TYPE(DATA_TYPE_OUT, 16));
-    VEC_DATA_TYPE(DATA_TYPE_OUT, 16)
-    in_b = CONVERT(vload16(0, (__global DATA_TYPE_IN2 *)in2.ptr), VEC_DATA_TYPE(DATA_TYPE_OUT, 16));
-
-    // Calculate and store result
-    vstore16(DIV(in_a, in_b), 0, (__global DATA_TYPE_OUT *)out.ptr);
-}
diff --git a/src/core/CL/cl_kernels/arithmetic_op_quantized.cl b/src/core/CL/cl_kernels/arithmetic_op_quantized.cl
deleted file mode 100644
index fc7fa771f3..0000000000
--- a/src/core/CL/cl_kernels/arithmetic_op_quantized.cl
+++ /dev/null
@@ -1,168 +0,0 @@
-/*
- * Copyright (c) 2016-2018 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 "helpers.h"
-
-#ifdef SATURATE
-#define ADD(x, y) add_sat((x), (y))
-#define SUB(x, y) sub_sat((x), (y))
-#else /* SATURATE */
-#define ADD(x, y) (x) + (y)
-#define SUB(x, y) (x) - (y)
-#endif /* SATURATE */
-
-#define CONVERT_RTE(x, type) (convert_##type##_rte((x)))
-#define CONVERT_DOWN(x, type) CONVERT_RTE(x, type)
-
-#if defined(OFFSET_IN1) && defined(OFFSET_IN2) && defined(OFFSET_OUT) && defined(SCALE_IN1) && defined(SCALE_IN2) && defined(SCALE_OUT)
-
-#if defined(VEC_SIZE)
-
-#define VEC_FLOAT VEC_DATA_TYPE(float, VEC_SIZE)
-#define VEC_INT VEC_DATA_TYPE(int, VEC_SIZE)
-#define VEC_UCHAR VEC_DATA_TYPE(uchar, VEC_SIZE)
-
-/** This function adds two tensors.
- *
- * @note The quantization offset of the first operand must be passed at compile time using -DOFFSET_IN1, i.e. -DOFFSET_IN1=10
- * @note The quantization offset of the second operand must be passed at compile time using -DOFFSET_IN2, i.e. -DOFFSET_IN2=10
- * @note The quantization offset of the output must be passed at compile time using -DOFFSET_OUT, i.e. -DOFFSET_OUT=10
- * @note The quantization scale of the first operand must be passed at compile time using -DSCALE_IN1, i.e. -DSCALE_IN1=10
- * @note The quantization scale of the second operand must be passed at compile time using -DSCALE_IN2, i.e. -DSCALE_IN2=10
- * @note The quantization scale of the output must be passed at compile time using -DSCALE_OUT, i.e. -DSCALE_OUT=10
- * @note To perform saturating operation -DSATURATE has to be passed to the compiler otherwise wrapping policy will be used.
- * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16
- *
- * @param[in]  in1_ptr                           Pointer to the source tensor. Supported data types: QASYMM8
- * @param[in]  in1_stride_x                      Stride of the source tensor in X dimension (in bytes)
- * @param[in]  in1_step_x                        in1_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  in1_stride_y                      Stride of the source tensor in Y dimension (in bytes)
- * @param[in]  in1_step_y                        in1_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  in1_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  in1_step_z                        in1_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  in1_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[in]  in2_ptr                           Pointer to the source tensor. Supported data types: same as @p in1_ptr
- * @param[in]  in2_stride_x                      Stride of the source tensor in X dimension (in bytes)
- * @param[in]  in2_step_x                        in2_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  in2_stride_y                      Stride of the source tensor in Y dimension (in bytes)
- * @param[in]  in2_step_y                        in2_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  in2_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  in2_step_z                        in2_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  in2_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[out] out_ptr                           Pointer to the destination tensor. Supported data types: same as @p in1_ptr
- * @param[in]  out_stride_x                      Stride of the destination tensor in X dimension (in bytes)
- * @param[in]  out_step_x                        out_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  out_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
- * @param[in]  out_step_y                        out_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  out_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  out_step_z                        out_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  out_offset_first_element_in_bytes The offset of the first element in the destination tensor
- */
-__kernel void arithmetic_add_quantized(
-    TENSOR3D_DECLARATION(in1),
-    TENSOR3D_DECLARATION(in2),
-    TENSOR3D_DECLARATION(out))
-{
-    // Get pixels pointer
-    Tensor3D in1 = CONVERT_TO_TENSOR3D_STRUCT(in1);
-    Tensor3D in2 = CONVERT_TO_TENSOR3D_STRUCT(in2);
-    Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(out);
-
-    VEC_INT in_a = CONVERT(VLOAD(VEC_SIZE)(0, (__global uchar *)in1.ptr), VEC_INT);
-    VEC_INT in_b = CONVERT(VLOAD(VEC_SIZE)(0, (__global uchar *)in2.ptr), VEC_INT);
-
-    in_a = SUB(in_a, (VEC_INT)((int)OFFSET_IN1));
-    in_b = SUB(in_b, (VEC_INT)((int)OFFSET_IN2));
-
-    const VEC_FLOAT in1f32 = CONVERT(in_a, VEC_FLOAT) * (VEC_FLOAT)((float)SCALE_IN1);
-    const VEC_FLOAT in2f32 = CONVERT(in_b, VEC_FLOAT) * (VEC_FLOAT)((float)SCALE_IN2);
-
-    const VEC_FLOAT qresf32 = (in1f32 + in2f32) / ((VEC_FLOAT)(float)SCALE_OUT) + ((VEC_FLOAT)((float)OFFSET_OUT));
-    const VEC_UCHAR res     = CONVERT_SAT(CONVERT_DOWN(qresf32, VEC_INT), VEC_UCHAR);
-
-    // Store result
-    VSTORE(VEC_SIZE)
-    (res, 0, (__global uchar *)out.ptr);
-}
-#endif /* defined(VEC_SIZE) */
-
-/** This function subtracts two tensors.
- *
- * @note The quantization offset of the first operand must be passed at compile time using -DOFFSET_IN1, i.e. -DOFFSET_IN1=10
- * @note The quantization offset of the second operand must be passed at compile time using -DOFFSET_IN2, i.e. -DOFFSET_IN2=10
- * @note The quantization offset of the output must be passed at compile time using -DOFFSET_OUT, i.e. -DOFFSET_OUT=10
- * @note The quantization scale of the first operand must be passed at compile time using -DSCALE_IN1, i.e. -DSCALE_IN1=10
- * @note The quantization scale of the second operand must be passed at compile time using -DSCALE_IN2, i.e. -DSCALE_IN2=10
- * @note The quantization scale of the output must be passed at compile time using -DSCALE_OUT, i.e. -DSCALE_OUT=10
- * @note To perform saturating operation -DSATURATE has to be passed to the compiler otherwise wrapping policy will be used.
- *
- * @param[in]  in1_ptr                           Pointer to the source tensor. Supported data types: QASYMM8
- * @param[in]  in1_stride_x                      Stride of the source tensor in X dimension (in bytes)
- * @param[in]  in1_step_x                        in1_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  in1_stride_y                      Stride of the source tensor in Y dimension (in bytes)
- * @param[in]  in1_step_y                        in1_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  in1_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  in1_step_z                        in1_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  in1_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[in]  in2_ptr                           Pointer to the source tensor. Supported data types: same as @p in1_ptr
- * @param[in]  in2_stride_x                      Stride of the source tensor in X dimension (in bytes)
- * @param[in]  in2_step_x                        in2_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  in2_stride_y                      Stride of the source tensor in Y dimension (in bytes)
- * @param[in]  in2_step_y                        in2_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  in2_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  in2_step_z                        in2_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  in2_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[out] out_ptr                           Pointer to the destination tensor. Supported data types: same as @p in1_ptr
- * @param[in]  out_stride_x                      Stride of the destination tensor in X dimension (in bytes)
- * @param[in]  out_step_x                        out_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  out_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
- * @param[in]  out_step_y                        out_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  out_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  out_step_z                        out_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  out_offset_first_element_in_bytes The offset of the first element in the destination tensor
- */
-__kernel void arithmetic_sub_quantized(
-    TENSOR3D_DECLARATION(in1),
-    TENSOR3D_DECLARATION(in2),
-    TENSOR3D_DECLARATION(out))
-{
-    // Get pixels pointer
-    Tensor3D in1 = CONVERT_TO_TENSOR3D_STRUCT(in1);
-    Tensor3D in2 = CONVERT_TO_TENSOR3D_STRUCT(in2);
-    Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(out);
-
-    int16 in_a = CONVERT(vload16(0, (__global uchar *)in1.ptr), int16);
-    int16 in_b = CONVERT(vload16(0, (__global uchar *)in2.ptr), int16);
-
-    in_a = SUB(in_a, (int16)((int)OFFSET_IN1));
-    in_b = SUB(in_b, (int16)((int)OFFSET_IN2));
-
-    const float16 in1f32  = convert_float16(in_a) * (float16)((float)SCALE_IN1);
-    const float16 in2f32  = convert_float16(in_b) * (float16)((float)SCALE_IN2);
-    const float16 qresf32 = (in1f32 - in2f32) / ((float16)(float)SCALE_OUT) + ((float16)((float16)OFFSET_OUT));
-    const uchar16 res     = convert_uchar16_sat(convert_int16_rte(qresf32));
-
-    // Store result
-    vstore16(res, 0, (__global uchar *)out.ptr);
-}
-#endif /* defined(OFFSET_IN1) && defined(OFFSET_IN2) && defined(OFFSET_OUT) && defined(SCALE_IN1) && defined(SCALE_IN2) && defined(SCALE_OUT) */
diff --git a/src/core/CL/cl_kernels/elementwise_operation.cl b/src/core/CL/cl_kernels/elementwise_operation.cl
new file mode 100644
index 0000000000..00d7ed3ba1
--- /dev/null
+++ b/src/core/CL/cl_kernels/elementwise_operation.cl
@@ -0,0 +1,98 @@
+/*
+ * 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 "helpers.h"
+
+/** List of all the operations supported by this kernel.
+ * @note ADD and SUB operations, when executed on integers, support saturation */
+#ifdef SATURATE
+#define ADD(x, y) add_sat((x), (y))
+#define SUB(x, y) sub_sat((x), (y))
+#else /* SATURATE */
+#define ADD(x, y) (x) + (y)
+#define SUB(x, y) (x) - (y)
+#endif /* SATURATE */
+
+#define MAX(x, y) max(x, y)
+#define MIN(x, y) min(x, y)
+#define SQUARED_DIFF(x, y) (x - y) * (x - y)
+#define DIV(x, y) (x / y)
+
+#define OP_FUN_NAME_STR(op) elementwise_operation_##op
+#define OP_FUN_NAME(op) OP_FUN_NAME_STR(op)
+
+#if defined(OP) && defined(DATA_TYPE_IN1) && defined(DATA_TYPE_IN2) && defined(DATA_TYPE_OUT) && defined(VEC_SIZE)
+/** This function executes an element-wise operation among two tensors.
+ *
+ * @attention The input and output data_types need to be passed at compile time using -DDATA_TYPE_IN1, -DDATA_TYPE_IN2 and -DDATA_TYPE_OUT:
+ * e.g. -DDATA_TYPE_IN1=uchar -DDATA_TYPE_IN2=uchar -DDATA_TYPE_OUT=short
+ * @attention To perform saturating operation -DSATURATE has to be passed to the compiler otherwise wrapping policy will be used.
+ * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16
+ * @attention The element-wise operation to be executed has to be passed at compile time using -DOP (e.g., -DOP=ADD)
+ *
+ * @param[in]  in1_ptr                           Pointer to the source tensor. Supported data types: U8/S16/F16/F32
+ * @param[in]  in1_stride_x                      Stride of the source tensor in X dimension (in bytes)
+ * @param[in]  in1_step_x                        in1_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  in1_stride_y                      Stride of the source tensor in Y dimension (in bytes)
+ * @param[in]  in1_step_y                        in1_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  in1_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  in1_step_z                        in1_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  in1_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[in]  in2_ptr                           Pointer to the source tensor. Supported data types: U8/S16/F16/F32
+ * @param[in]  in2_stride_x                      Stride of the source tensor in X dimension (in bytes)
+ * @param[in]  in2_step_x                        in2_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  in2_stride_y                      Stride of the source tensor in Y dimension (in bytes)
+ * @param[in]  in2_step_y                        in2_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  in2_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  in2_step_z                        in2_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  in2_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] out_ptr                           Pointer to the destination tensor. Supported data types: U8 (only if both inputs are U8), S16/F16/F32
+ * @param[in]  out_stride_x                      Stride of the destination tensor in X dimension (in bytes)
+ * @param[in]  out_step_x                        out_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  out_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in]  out_step_y                        out_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  out_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  out_step_z                        out_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  out_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void OP_FUN_NAME(OP)(
+    TENSOR3D_DECLARATION(in1),
+    TENSOR3D_DECLARATION(in2),
+    TENSOR3D_DECLARATION(out))
+{
+    // Get pixels pointer
+    Tensor3D in1 = CONVERT_TO_TENSOR3D_STRUCT(in1);
+    Tensor3D in2 = CONVERT_TO_TENSOR3D_STRUCT(in2);
+    Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(out);
+
+    // Load values
+    VEC_DATA_TYPE(DATA_TYPE_OUT, VEC_SIZE)
+    in_a = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE_IN1 *)in1.ptr), VEC_DATA_TYPE(DATA_TYPE_OUT, VEC_SIZE));
+    VEC_DATA_TYPE(DATA_TYPE_OUT, VEC_SIZE)
+    in_b = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE_IN2 *)in2.ptr), VEC_DATA_TYPE(DATA_TYPE_OUT, VEC_SIZE));
+
+    // Calculate and store result
+    VSTORE(VEC_SIZE)
+    (OP(in_a, in_b), 0, (__global DATA_TYPE_OUT *)out.ptr);
+}
+#endif /* defined(DATA_TYPE_IN1) && defined(DATA_TYPE_IN2) && defined(DATA_TYPE_OUT) && defined(VEC_SIZE) */
diff --git a/src/core/CL/cl_kernels/elementwise_operation_quantized.cl b/src/core/CL/cl_kernels/elementwise_operation_quantized.cl
new file mode 100644
index 0000000000..1f0533be13
--- /dev/null
+++ b/src/core/CL/cl_kernels/elementwise_operation_quantized.cl
@@ -0,0 +1,107 @@
+/*
+ * 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 "helpers.h"
+
+#define SUB(x, y) (x - y)
+#define ADD(x, y) (x + y)
+#define MAX(x, y) max((x), (y))
+#define MIN(x, y) min((x), (y))
+#define SQUARED_DIFF(x, y) (x - y) * (x - y)
+#define DIV(x, y) (x / y)
+
+#define CONVERT_RTE(x, type) (convert_##type##_rte((x)))
+#define CONVERT_DOWN(x, type) CONVERT_RTE(x, type)
+
+#define OP_FUN_NAME_STR(op) elementwise_operation_##op##_quantized
+#define OP_FUN_NAME(op) OP_FUN_NAME_STR(op)
+
+#if defined(OP) && defined(VEC_SIZE) && defined(OFFSET_IN1) && defined(OFFSET_IN2) && defined(OFFSET_OUT) && defined(SCALE_IN1) && defined(SCALE_IN2) && defined(SCALE_OUT)
+
+#define VEC_FLOAT VEC_DATA_TYPE(float, VEC_SIZE)
+#define VEC_INT VEC_DATA_TYPE(int, VEC_SIZE)
+#define VEC_UCHAR VEC_DATA_TYPE(uchar, VEC_SIZE)
+
+/** This function executes an element-wise operation among two tensors.
+ *
+ * @attention The quantization offset of the first operand must be passed at compile time using -DOFFSET_IN1, i.e. -DOFFSET_IN1=10
+ * @attention The quantization offset of the second operand must be passed at compile time using -DOFFSET_IN2, i.e. -DOFFSET_IN2=10
+ * @attention The quantization offset of the output must be passed at compile time using -DOFFSET_OUT, i.e. -DOFFSET_OUT=10
+ * @attention The quantization scale of the first operand must be passed at compile time using -DSCALE_IN1, i.e. -DSCALE_IN1=10
+ * @attention The quantization scale of the second operand must be passed at compile time using -DSCALE_IN2, i.e. -DSCALE_IN2=10
+ * @attention The quantization scale of the output must be passed at compile time using -DSCALE_OUT, i.e. -DSCALE_OUT=10
+ * @attention To perform saturating operation -DSATURATE has to be passed to the compiler otherwise wrapping policy will be used.
+ * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16
+ * @attention The element-wise operation to be executed has to be passed at compile time using -DOP (e.g., -DOP=ADD)
+ *
+ * @param[in]  in1_ptr                           Pointer to the source tensor. Supported data types: QASYMM8
+ * @param[in]  in1_stride_x                      Stride of the source tensor in X dimension (in bytes)
+ * @param[in]  in1_step_x                        in1_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  in1_stride_y                      Stride of the source tensor in Y dimension (in bytes)
+ * @param[in]  in1_step_y                        in1_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  in1_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  in1_step_z                        in1_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  in1_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[in]  in2_ptr                           Pointer to the source tensor. Supported data types: same as @p in1_ptr
+ * @param[in]  in2_stride_x                      Stride of the source tensor in X dimension (in bytes)
+ * @param[in]  in2_step_x                        in2_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  in2_stride_y                      Stride of the source tensor in Y dimension (in bytes)
+ * @param[in]  in2_step_y                        in2_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  in2_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  in2_step_z                        in2_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  in2_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] out_ptr                           Pointer to the destination tensor. Supported data types: same as @p in1_ptr
+ * @param[in]  out_stride_x                      Stride of the destination tensor in X dimension (in bytes)
+ * @param[in]  out_step_x                        out_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  out_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in]  out_step_y                        out_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  out_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  out_step_z                        out_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  out_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void OP_FUN_NAME(OP)(
+    TENSOR3D_DECLARATION(in1),
+    TENSOR3D_DECLARATION(in2),
+    TENSOR3D_DECLARATION(out))
+{
+    // Get pixels pointer
+    Tensor3D in1 = CONVERT_TO_TENSOR3D_STRUCT(in1);
+    Tensor3D in2 = CONVERT_TO_TENSOR3D_STRUCT(in2);
+    Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(out);
+
+    VEC_INT in_a = CONVERT(VLOAD(VEC_SIZE)(0, (__global uchar *)in1.ptr), VEC_INT);
+    VEC_INT in_b = CONVERT(VLOAD(VEC_SIZE)(0, (__global uchar *)in2.ptr), VEC_INT);
+
+    in_a = SUB(in_a, (VEC_INT)((int)OFFSET_IN1));
+    in_b = SUB(in_b, (VEC_INT)((int)OFFSET_IN2));
+
+    const VEC_FLOAT in1f32  = CONVERT(in_a, VEC_FLOAT) * (VEC_FLOAT)((float)SCALE_IN1);
+    const VEC_FLOAT in2f32  = CONVERT(in_b, VEC_FLOAT) * (VEC_FLOAT)((float)SCALE_IN2);
+    const VEC_FLOAT qresf32 = OP(in1f32, in2f32) / ((VEC_FLOAT)(float)SCALE_OUT) + ((VEC_FLOAT)((float)OFFSET_OUT));
+    const VEC_UCHAR res     = CONVERT_SAT(CONVERT_DOWN(qresf32, VEC_INT), VEC_UCHAR);
+
+    // Store result
+    VSTORE(VEC_SIZE)
+    (res, 0, (__global uchar *)out.ptr);
+}
+#endif /* defined(OFFSET_IN1) && defined(OFFSET_IN2) && defined(OFFSET_OUT) && defined(SCALE_IN1) && defined(SCALE_IN2) && defined(SCALE_OUT) */
diff --git a/src/core/CL/kernels/CLArithmeticAdditionKernel.cpp b/src/core/CL/kernels/CLArithmeticAdditionKernel.cpp
deleted file mode 100644
index 10d7fd4f2c..0000000000
--- a/src/core/CL/kernels/CLArithmeticAdditionKernel.cpp
+++ /dev/null
@@ -1,233 +0,0 @@
-/*
- * Copyright (c) 2016-2018 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/CL/kernels/CLArithmeticAdditionKernel.h"
-
-#include "arm_compute/core/CL/CLHelpers.h"
-#include "arm_compute/core/CL/CLValidate.h"
-#include "arm_compute/core/CL/ICLTensor.h"
-
-using namespace arm_compute;
-
-namespace
-{
-constexpr unsigned int num_elems_processed_per_iteration = 8;
-
-Status validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output, ConvertPolicy policy)
-{
-    ARM_COMPUTE_UNUSED(policy);
-    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input1);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input2);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input2, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32);
-
-    const bool is_qasymm = is_data_type_quantized_asymmetric(input1.data_type()) || is_data_type_quantized_asymmetric(input2.data_type());
-    if(is_qasymm)
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &input2);
-    }
-
-    const TensorShape out_shape = TensorShape::broadcast_shape(input1.tensor_shape(), input2.tensor_shape());
-
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");
-
-    // Validate in case of configured output
-    if(output.total_size() > 0)
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&output);
-        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&output, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32);
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG((output.data_type() == DataType::U8) && ((input1.data_type() != DataType::U8) || (input2.data_type() != DataType::U8)),
-                                        "Output can only be U8 if both inputs are U8");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output.tensor_shape(), 0),
-                                        "Wrong shape for output");
-        if(is_qasymm)
-        {
-            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &output);
-        }
-    }
-
-    return Status{};
-}
-
-std::pair<Status, Window> validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output)
-{
-    const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(input1, input2);
-    const TensorShape &out_shape    = broadcast_pair.first;
-    const ValidRegion &valid_region = broadcast_pair.second;
-
-    // Auto initialize output if not initialized
-    {
-        set_shape_if_empty(output, out_shape);
-
-        if(input1.data_type() == DataType::S16 || input2.data_type() == DataType::S16)
-        {
-            set_format_if_unknown(output, Format::S16);
-        }
-        else if(input1.data_type() == DataType::F16 && input2.data_type() == DataType::F16)
-        {
-            set_format_if_unknown(output, Format::F16);
-        }
-        else if(input1.data_type() == DataType::F32 || input2.data_type() == DataType::F32)
-        {
-            set_format_if_unknown(output, Format::F32);
-        }
-    }
-
-    Window win        = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration));
-    Window win_input1 = win.broadcast_if_dimension_le_one(input1);
-    Window win_input2 = win.broadcast_if_dimension_le_one(input2);
-
-    AccessWindowHorizontal input1_access(&input1, 0, num_elems_processed_per_iteration);
-    AccessWindowHorizontal input2_access(&input2, 0, num_elems_processed_per_iteration);
-    AccessWindowHorizontal output_access(&output, 0, num_elems_processed_per_iteration);
-
-    bool window_changed = update_window_and_padding(win_input1, input1_access)
-                          || update_window_and_padding(win_input2, input2_access)
-                          || update_window_and_padding(win, output_access);
-
-    output_access.set_valid_region(win, valid_region);
-
-    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
-    return std::make_pair(err, win);
-}
-} // namespace
-
-CLArithmeticAdditionKernel::CLArithmeticAdditionKernel()
-    : _input1(nullptr), _input2(nullptr), _output(nullptr)
-{
-}
-
-void CLArithmeticAdditionKernel::configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, ConvertPolicy policy)
-{
-    ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*input1->info(), *input2->info(), *output->info(), policy));
-
-    // Configure kernel window
-    auto win_config = validate_and_configure_window(*input1->info(), *input2->info(), *output->info());
-    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
-
-    _input1 = input1;
-    _input2 = input2;
-    _output = output;
-
-    const bool has_float_out = is_data_type_float(output->info()->data_type());
-
-    std::string kernel_name = "arithmetic_add";
-
-    // Set kernel build options
-    std::set<std::string> build_opts;
-    build_opts.emplace((policy == ConvertPolicy::WRAP || has_float_out) ? "-DWRAP" : "-DSATURATE");
-    build_opts.emplace("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(input1->info()->data_type()));
-    build_opts.emplace("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(input2->info()->data_type()));
-    build_opts.emplace("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output->info()->data_type()));
-    build_opts.emplace("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration));
-    if(is_data_type_quantized_asymmetric(input1->info()->data_type()))
-    {
-        build_opts.emplace("-DOFFSET_IN1=" + support::cpp11::to_string(input1->info()->quantization_info().offset));
-        build_opts.emplace("-DOFFSET_IN2=" + support::cpp11::to_string(input2->info()->quantization_info().offset));
-        build_opts.emplace("-DOFFSET_OUT=" + support::cpp11::to_string(output->info()->quantization_info().offset));
-        build_opts.emplace("-DSCALE_IN1=" + support::cpp11::to_string(input1->info()->quantization_info().scale));
-        build_opts.emplace("-DSCALE_IN2=" + support::cpp11::to_string(input2->info()->quantization_info().scale));
-        build_opts.emplace("-DSCALE_OUT=" + support::cpp11::to_string(output->info()->quantization_info().scale));
-        kernel_name += "_quantized";
-    }
-
-    // Create kernel
-    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts));
-
-    ICLKernel::configure_internal(win_config.second);
-
-    // Set config_id for enabling LWS tuning
-    _config_id = kernel_name;
-    _config_id += "_";
-    _config_id += lower_string(string_from_data_type(input1->info()->data_type()));
-    _config_id += "_";
-    _config_id += support::cpp11::to_string(output->info()->dimension(0));
-    _config_id += "_";
-    _config_id += support::cpp11::to_string(output->info()->dimension(1));
-    _config_id += (policy == ConvertPolicy::WRAP) ? "_wrap_" : "_saturate_";
-    _config_id += lower_string(string_from_data_layout(input1->info()->data_layout()));
-}
-
-Status CLArithmeticAdditionKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output);
-
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(*input1, *input2, *output, policy));
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(*input1->clone(), *input2->clone(), *output->clone()).first);
-
-    return Status{};
-}
-
-void CLArithmeticAdditionKernel::run(const Window &window, cl::CommandQueue &queue)
-{
-    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
-    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
-
-    const TensorShape &in_shape1 = _input1->info()->tensor_shape();
-    const TensorShape &in_shape2 = _input2->info()->tensor_shape();
-    const TensorShape &out_shape = _output->info()->tensor_shape();
-
-    bool       can_collapse = true;
-    const bool is_vector    = in_shape1.num_dimensions() == 1 || in_shape2.num_dimensions() == 1;
-    if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1 && !is_vector)
-    {
-        can_collapse = (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ);
-        for(size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); d++)
-        {
-            can_collapse = (in_shape1[d] == in_shape2[d]);
-        }
-    }
-
-    bool   has_collapsed = false;
-    Window collapsed     = can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed) : window;
-
-    const TensorShape &in_shape1_collapsed = has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1;
-    const TensorShape &in_shape2_collapsed = has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2;
-
-    Window slice        = collapsed.first_slice_window_3D();
-    Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed);
-    Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed);
-
-    do
-    {
-        unsigned int idx = 0;
-
-        add_3D_tensor_argument(idx, _input1, slice_input1);
-        add_3D_tensor_argument(idx, _input2, slice_input2);
-        add_3D_tensor_argument(idx, _output, slice);
-
-        enqueue(queue, *this, slice, lws_hint());
-
-        collapsed.slide_window_slice_3D(slice_input1);
-        collapsed.slide_window_slice_3D(slice_input2);
-    }
-    while(collapsed.slide_window_slice_3D(slice));
-}
-
-BorderSize CLArithmeticAdditionKernel::border_size() const
-{
-    const unsigned int replicateSize = _output->info()->dimension(0) - std::min(_input1->info()->dimension(0), _input2->info()->dimension(0));
-    const unsigned int border        = std::min<unsigned int>(num_elems_processed_per_iteration - 1U, replicateSize);
-    return BorderSize(0, border, 0, 0);
-}
diff --git a/src/core/CL/kernels/CLArithmeticDivisionKernel.cpp b/src/core/CL/kernels/CLArithmeticDivisionKernel.cpp
deleted file mode 100644
index e995ba1a41..0000000000
--- a/src/core/CL/kernels/CLArithmeticDivisionKernel.cpp
+++ /dev/null
@@ -1,185 +0,0 @@
-/*
- * 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/CL/kernels/CLArithmeticDivisionKernel.h"
-
-#include "arm_compute/core/CL/CLHelpers.h"
-#include "arm_compute/core/CL/CLValidate.h"
-#include "arm_compute/core/CL/ICLTensor.h"
-
-using namespace arm_compute;
-
-namespace
-{
-constexpr unsigned int num_elems_processed_per_iteration = 16;
-
-Status validate_arguments(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output);
-    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input1);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::F16, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input1, input2);
-
-    const TensorShape out_shape = TensorShape::broadcast_shape(input1->tensor_shape(), input2->tensor_shape());
-
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");
-
-    // Validate in case of configured output
-    if(output->total_size() > 0)
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input1, output);
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output->tensor_shape(), 0),
-                                        "Wrong shape for output");
-    }
-
-    return Status{};
-}
-
-std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output)
-{
-    const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(*input1, *input2);
-    const TensorShape &out_shape    = broadcast_pair.first;
-    const ValidRegion &valid_region = broadcast_pair.second;
-
-    // Auto initialize output if not initialized
-    {
-        set_shape_if_empty(*output, out_shape);
-
-        if(input1->data_type() == DataType::F16 && input2->data_type() == DataType::F16)
-        {
-            set_format_if_unknown(*output, Format::F16);
-        }
-        else if(input1->data_type() == DataType::F32 || input2->data_type() == DataType::F32)
-        {
-            set_format_if_unknown(*output, Format::F32);
-        }
-    }
-
-    Window win        = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration));
-    Window win_input1 = win.broadcast_if_dimension_le_one(*input1);
-    Window win_input2 = win.broadcast_if_dimension_le_one(*input2);
-
-    AccessWindowHorizontal input1_access(input1, 0, num_elems_processed_per_iteration);
-    AccessWindowHorizontal input2_access(input2, 0, num_elems_processed_per_iteration);
-    AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);
-
-    bool window_changed = update_window_and_padding(win_input1, input1_access)
-                          || update_window_and_padding(win_input2, input2_access)
-                          || update_window_and_padding(win, output_access);
-
-    output_access.set_valid_region(win, valid_region);
-
-    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
-    return std::make_pair(err, win);
-}
-} // namespace
-
-CLArithmeticDivisionKernel::CLArithmeticDivisionKernel()
-    : _input1(nullptr), _input2(nullptr), _output(nullptr)
-{
-}
-
-void CLArithmeticDivisionKernel::configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output)
-{
-    ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input1->info(), input2->info(), output->info()));
-
-    // Configure kernel window
-    auto win_config = validate_and_configure_window(input1->info(), input2->info(), output->info());
-    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
-
-    _input1 = input1;
-    _input2 = input2;
-    _output = output;
-
-    // Set kernel build options
-    std::set<std::string> build_opts;
-    build_opts.emplace("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(input1->info()->data_type()));
-    build_opts.emplace("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(input2->info()->data_type()));
-    build_opts.emplace("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output->info()->data_type()));
-
-    // Create kernel
-    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("arithmetic_div", build_opts));
-
-    ICLKernel::configure_internal(win_config.second);
-}
-
-Status CLArithmeticDivisionKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output)
-{
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input1, input2, output));
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input1->clone().get(), input2->clone().get(), output->clone().get()).first);
-
-    return Status{};
-}
-
-void CLArithmeticDivisionKernel::run(const Window &window, cl::CommandQueue &queue)
-{
-    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
-    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
-
-    const TensorShape &in_shape1 = _input1->info()->tensor_shape();
-    const TensorShape &in_shape2 = _input2->info()->tensor_shape();
-    const TensorShape &out_shape = _output->info()->tensor_shape();
-
-    bool can_collapse = true;
-    if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1)
-    {
-        can_collapse = (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ);
-        for(size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); d++)
-        {
-            can_collapse = (in_shape1[d] == in_shape2[d]);
-        }
-    }
-
-    bool   has_collapsed = false;
-    Window collapsed     = can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed) : window;
-
-    const TensorShape &in_shape1_collapsed = has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1;
-    const TensorShape &in_shape2_collapsed = has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2;
-
-    Window slice        = collapsed.first_slice_window_3D();
-    Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed);
-    Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed);
-
-    do
-    {
-        unsigned int idx = 0;
-
-        add_3D_tensor_argument(idx, _input1, slice_input1);
-        add_3D_tensor_argument(idx, _input2, slice_input2);
-        add_3D_tensor_argument(idx, _output, slice);
-
-        enqueue(queue, *this, slice);
-
-        collapsed.slide_window_slice_3D(slice_input1);
-        collapsed.slide_window_slice_3D(slice_input2);
-    }
-    while(collapsed.slide_window_slice_3D(slice));
-}
-
-BorderSize CLArithmeticDivisionKernel::border_size() const
-{
-    const unsigned int replicateSize = _output->info()->dimension(0) - std::min(_input1->info()->dimension(0), _input2->info()->dimension(0));
-    const unsigned int border        = std::min<unsigned int>(num_elems_processed_per_iteration - 1U, replicateSize);
-    return BorderSize(0, border, 0, 0);
-}
diff --git a/src/core/CL/kernels/CLArithmeticSubtractionKernel.cpp b/src/core/CL/kernels/CLArithmeticSubtractionKernel.cpp
deleted file mode 100644
index 95d201104d..0000000000
--- a/src/core/CL/kernels/CLArithmeticSubtractionKernel.cpp
+++ /dev/null
@@ -1,232 +0,0 @@
-/*
- * Copyright (c) 2016-2018 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/CL/kernels/CLArithmeticSubtractionKernel.h"
-
-#include "arm_compute/core/CL/CLHelpers.h"
-#include "arm_compute/core/CL/CLKernelLibrary.h"
-#include "arm_compute/core/CL/CLValidate.h"
-#include "arm_compute/core/CL/ICLTensor.h"
-#include "arm_compute/core/CL/OpenCL.h"
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/IAccessWindow.h"
-#include "arm_compute/core/TensorInfo.h"
-#include "arm_compute/core/Window.h"
-
-#include <set>
-#include <string>
-
-namespace arm_compute
-{
-namespace
-{
-constexpr unsigned int num_elems_processed_per_iteration = 16;
-
-Status validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output, ConvertPolicy policy)
-{
-    ARM_COMPUTE_UNUSED(policy);
-    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input1);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input2);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input2, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32);
-    const bool is_qasymm = is_data_type_quantized_asymmetric(input1.data_type()) || is_data_type_quantized_asymmetric(input2.data_type());
-    if(is_qasymm)
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &input2);
-    }
-
-    const TensorShape out_shape = TensorShape::broadcast_shape(input1.tensor_shape(), input2.tensor_shape());
-
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");
-
-    // Validate in case of configured output
-    if(output.total_size() > 0)
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&output);
-        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&output, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32);
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG((output.data_type() == DataType::U8) && ((input1.data_type() != DataType::U8) || (input2.data_type() != DataType::U8)),
-                                        "Output can only be U8 if both inputs are U8");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output.tensor_shape(), 0),
-                                        "Wrong shape for output");
-        if(is_qasymm)
-        {
-            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &output);
-        }
-    }
-
-    return Status{};
-}
-
-std::pair<Status, Window> validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output)
-{
-    const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(input1, input2);
-    const TensorShape &out_shape    = broadcast_pair.first;
-    const ValidRegion &valid_region = broadcast_pair.second;
-
-    // Auto initialize output if not initialized
-    {
-        set_shape_if_empty(output, out_shape);
-
-        if(input1.data_type() == DataType::S16 || input2.data_type() == DataType::S16)
-        {
-            set_format_if_unknown(output, Format::S16);
-        }
-        else if(input1.data_type() == DataType::F16 && input2.data_type() == DataType::F16)
-        {
-            set_format_if_unknown(output, Format::F16);
-        }
-        else if(input1.data_type() == DataType::F32 || input2.data_type() == DataType::F32)
-        {
-            set_format_if_unknown(output, Format::F32);
-        }
-    }
-
-    Window win        = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration));
-    Window win_input1 = win.broadcast_if_dimension_le_one(input1);
-    Window win_input2 = win.broadcast_if_dimension_le_one(input2);
-
-    AccessWindowHorizontal input1_access(&input1, 0, num_elems_processed_per_iteration);
-    AccessWindowHorizontal input2_access(&input2, 0, num_elems_processed_per_iteration);
-    AccessWindowHorizontal output_access(&output, 0, num_elems_processed_per_iteration);
-
-    bool window_changed = update_window_and_padding(win_input1, input1_access)
-                          || update_window_and_padding(win_input2, input2_access)
-                          || update_window_and_padding(win, output_access);
-
-    output_access.set_valid_region(win, valid_region);
-
-    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
-    return std::make_pair(err, win);
-}
-} // namespace
-
-CLArithmeticSubtractionKernel::CLArithmeticSubtractionKernel()
-    : _input1(nullptr), _input2(nullptr), _output(nullptr)
-{
-}
-
-void CLArithmeticSubtractionKernel::configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, ConvertPolicy policy)
-{
-    ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*input1->info(), *input2->info(), *output->info(), policy));
-
-    // Configure kernel window
-    auto win_config = validate_and_configure_window(*input1->info(), *input2->info(), *output->info());
-    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
-
-    _input1 = input1;
-    _input2 = input2;
-    _output = output;
-
-    bool has_float_out = is_data_type_float(output->info()->data_type());
-
-    // Setup kernel
-    std::string kernel_name = "arithmetic_sub";
-
-    // Set kernel build options
-    CLBuildOptions build_opts;
-    build_opts.add_option_if_else(policy == ConvertPolicy::WRAP || has_float_out, "-DWRAP", "-DSATURATE");
-    build_opts.add_option("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(input1->info()->data_type()));
-    build_opts.add_option("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(input2->info()->data_type()));
-    build_opts.add_option("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output->info()->data_type()));
-    if(is_data_type_quantized_asymmetric(input1->info()->data_type()))
-    {
-        build_opts.add_option("-DOFFSET_IN1=" + support::cpp11::to_string(input1->info()->quantization_info().offset));
-        build_opts.add_option("-DOFFSET_IN2=" + support::cpp11::to_string(input2->info()->quantization_info().offset));
-        build_opts.add_option("-DOFFSET_OUT=" + support::cpp11::to_string(output->info()->quantization_info().offset));
-        build_opts.add_option("-DSCALE_IN1=" + support::cpp11::to_string(input1->info()->quantization_info().scale));
-        build_opts.add_option("-DSCALE_IN2=" + support::cpp11::to_string(input2->info()->quantization_info().scale));
-        build_opts.add_option("-DSCALE_OUT=" + support::cpp11::to_string(output->info()->quantization_info().scale));
-        kernel_name += "_quantized";
-    }
-
-    // Create kernel
-    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
-
-    // Configure kernel window
-    ICLKernel::configure_internal(win_config.second);
-}
-
-Status CLArithmeticSubtractionKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output);
-
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(*input1, *input2, *output, policy));
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(*input1->clone(), *input2->clone(), *output->clone()).first);
-
-    return Status{};
-}
-
-void CLArithmeticSubtractionKernel::run(const Window &window, cl::CommandQueue &queue)
-{
-    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
-    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
-
-    const TensorShape &in_shape1 = _input1->info()->tensor_shape();
-    const TensorShape &in_shape2 = _input2->info()->tensor_shape();
-    const TensorShape &out_shape = _output->info()->tensor_shape();
-
-    // Collapse only if broadcast dimensions is less than 2, or in case of no broadcasting
-    bool can_collapse = true;
-    if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1)
-    {
-        can_collapse = (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ);
-        for(size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); d++)
-        {
-            can_collapse = (in_shape1[d] == in_shape2[d]);
-        }
-    }
-
-    bool   has_collapsed = false;
-    Window collapsed     = can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed) : window;
-
-    const TensorShape &in_shape1_collapsed = has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1;
-    const TensorShape &in_shape2_collapsed = has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2;
-
-    Window slice        = collapsed.first_slice_window_3D();
-    Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed);
-    Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed);
-
-    do
-    {
-        unsigned int idx = 0;
-
-        add_3D_tensor_argument(idx, _input1, slice_input1);
-        add_3D_tensor_argument(idx, _input2, slice_input2);
-        add_3D_tensor_argument(idx, _output, slice);
-
-        enqueue(queue, *this, slice);
-
-        collapsed.slide_window_slice_3D(slice_input1);
-        collapsed.slide_window_slice_3D(slice_input2);
-    }
-    while(collapsed.slide_window_slice_3D(slice));
-}
-
-BorderSize CLArithmeticSubtractionKernel::border_size() const
-{
-    const unsigned int replicateSize = _output->info()->dimension(0) - std::min(_input1->info()->dimension(0), _input2->info()->dimension(0));
-    const unsigned int border        = std::min<unsigned int>(num_elems_processed_per_iteration - 1U, replicateSize);
-    return BorderSize(0, border, 0, 0);
-}
-} // namespace arm_compute
\ No newline at end of file
diff --git a/src/core/CL/kernels/CLElementwiseOperationKernel.cpp b/src/core/CL/kernels/CLElementwiseOperationKernel.cpp
new file mode 100644
index 0000000000..5dc5b7e13f
--- /dev/null
+++ b/src/core/CL/kernels/CLElementwiseOperationKernel.cpp
@@ -0,0 +1,337 @@
+/*
+ * 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/CL/kernels/CLElementwiseOperationKernel.h"
+
+#include "arm_compute/core/CL/CLHelpers.h"
+#include "arm_compute/core/CL/CLValidate.h"
+#include "arm_compute/core/CL/ICLTensor.h"
+#include <map>
+
+namespace arm_compute
+{
+namespace
+{
+constexpr unsigned int num_elems_processed_per_iteration = 16;
+
+std::map<ArithmeticOperation, std::string> supported_arithmetic_ops =
+{
+    { ArithmeticOperation::ADD, "ADD" },
+    { ArithmeticOperation::SUB, "SUB" },
+    { ArithmeticOperation::DIV, "DIV" },
+    { ArithmeticOperation::SQUARED_DIFF, "SQUARED_DIFF" },
+    { ArithmeticOperation::MIN, "MIN" },
+    { ArithmeticOperation::MAX, "MAX" },
+};
+
+std::map<ArithmeticOperation, std::string> supported_sat_arithmetic_ops =
+{
+    { ArithmeticOperation::ADD, "ADD" },
+    { ArithmeticOperation::SUB, "SUB" },
+};
+
+std::string generate_id_for_tuning_common(const std::string &kernel_name, const ITensorInfo &input1, const ITensorInfo &output)
+{
+    std::string config_id;
+    // Set config_id for enabling LWS tuning
+    config_id = kernel_name;
+    config_id += "_";
+    config_id += lower_string(string_from_data_type(input1.data_type()));
+    config_id += "_";
+    config_id += support::cpp11::to_string(output.dimension(0));
+    config_id += "_";
+    config_id += support::cpp11::to_string(output.dimension(1));
+    return config_id;
+}
+
+Status validate_arguments_with_arithmetic_rules(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input1);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input2);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input2, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32);
+
+    const bool is_qasymm = is_data_type_quantized_asymmetric(input1.data_type()) || is_data_type_quantized_asymmetric(input2.data_type());
+    if(is_qasymm)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &input2);
+    }
+
+    const TensorShape out_shape = TensorShape::broadcast_shape(input1.tensor_shape(), input2.tensor_shape());
+
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");
+
+    // Validate in case of configured output
+    if(output.total_size() > 0)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&output);
+        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&output, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32);
+        ARM_COMPUTE_RETURN_ERROR_ON_MSG((output.data_type() == DataType::U8) && ((input1.data_type() != DataType::U8) || (input2.data_type() != DataType::U8)),
+                                        "Output can only be U8 if both inputs are U8");
+        ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output.tensor_shape(), 0),
+                                        "Wrong shape for output");
+        if(is_qasymm)
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &output);
+        }
+    }
+    return Status{};
+}
+
+CLBuildOptions generate_build_options_with_arithmetic_rules(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output, const std::string &operation_string)
+{
+    CLBuildOptions build_opts;
+
+    build_opts.add_option("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(input1.data_type()));
+    build_opts.add_option("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(input2.data_type()));
+    build_opts.add_option("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output.data_type()));
+    build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration));
+    build_opts.add_option("-DOP=" + operation_string);
+    if(is_data_type_quantized_asymmetric(input1.data_type()))
+    {
+        build_opts.add_option("-DOFFSET_IN1=" + support::cpp11::to_string(input1.quantization_info().offset));
+        build_opts.add_option("-DOFFSET_IN2=" + support::cpp11::to_string(input2.quantization_info().offset));
+        build_opts.add_option("-DOFFSET_OUT=" + support::cpp11::to_string(output.quantization_info().offset));
+        build_opts.add_option("-DSCALE_IN1=" + float_to_string_with_full_precision(input1.quantization_info().scale));
+        build_opts.add_option("-DSCALE_IN2=" + float_to_string_with_full_precision(input2.quantization_info().scale));
+        build_opts.add_option("-DSCALE_OUT=" + float_to_string_with_full_precision(output.quantization_info().scale));
+    }
+    return build_opts;
+}
+
+std::pair<Status, Window> validate_and_configure_window_for_arithmetic_operators(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output)
+{
+    const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(input1, input2);
+    const TensorShape &out_shape    = broadcast_pair.first;
+    const ValidRegion &valid_region = broadcast_pair.second;
+
+    set_shape_if_empty(output, out_shape);
+
+    if(input1.data_type() == DataType::S16 || input2.data_type() == DataType::S16)
+    {
+        set_format_if_unknown(output, Format::S16);
+    }
+    else if(input1.data_type() == DataType::F16 && input2.data_type() == DataType::F16)
+    {
+        set_format_if_unknown(output, Format::F16);
+    }
+    else if(input1.data_type() == DataType::F32 || input2.data_type() == DataType::F32)
+    {
+        set_format_if_unknown(output, Format::F32);
+    }
+
+    Window win        = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration));
+    Window win_input1 = win.broadcast_if_dimension_le_one(input1);
+    Window win_input2 = win.broadcast_if_dimension_le_one(input2);
+
+    AccessWindowHorizontal input1_access(&input1, 0, num_elems_processed_per_iteration);
+    AccessWindowHorizontal input2_access(&input2, 0, num_elems_processed_per_iteration);
+    AccessWindowHorizontal output_access(&output, 0, num_elems_processed_per_iteration);
+
+    bool window_changed = update_window_and_padding(win_input1, input1_access)
+                          || update_window_and_padding(win_input2, input2_access)
+                          || update_window_and_padding(win, output_access);
+
+    output_access.set_valid_region(win, valid_region);
+
+    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+    return std::make_pair(err, win);
+}
+} // namespace
+
+CLElementwiseOperationKernel::CLElementwiseOperationKernel()
+    : _input1(nullptr), _input2(nullptr), _output(nullptr)
+{
+}
+
+void CLElementwiseOperationKernel::configure_common(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*input1->info(), *input2->info(), *output->info()));
+
+    // Configure kernel window
+    auto win_config = validate_and_configure_window(*input1->info(), *input2->info(), *output->info());
+    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+
+    _input1 = input1;
+    _input2 = input2;
+    _output = output;
+
+    std::string kernel_name = "elementwise_operation_" + name();
+    if(is_data_type_quantized_asymmetric(input1->info()->data_type()))
+    {
+        kernel_name += "_quantized";
+    }
+
+    // Set kernel build options
+    CLBuildOptions build_opts = generate_build_options(*input1->info(), *input2->info(), *output->info());
+
+    // Create kernel
+    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
+
+    ICLKernel::configure_internal(win_config.second);
+
+    _config_id = generate_id_for_tuning(kernel_name, *input1->info(), *output->info());
+}
+
+void CLElementwiseOperationKernel::run(const Window &window, cl::CommandQueue &queue)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
+
+    const TensorShape &in_shape1 = _input1->info()->tensor_shape();
+    const TensorShape &in_shape2 = _input2->info()->tensor_shape();
+    const TensorShape &out_shape = _output->info()->tensor_shape();
+
+    bool       can_collapse = true;
+    const bool is_vector    = in_shape1.num_dimensions() == 1 || in_shape2.num_dimensions() == 1;
+    if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1 && !is_vector)
+    {
+        can_collapse = (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ);
+        for(size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); d++)
+        {
+            can_collapse = (in_shape1[d] == in_shape2[d]);
+        }
+    }
+
+    bool   has_collapsed = false;
+    Window collapsed     = can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed) : window;
+
+    const TensorShape &in_shape1_collapsed = has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1;
+    const TensorShape &in_shape2_collapsed = has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2;
+
+    Window slice        = collapsed.first_slice_window_3D();
+    Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed);
+    Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed);
+
+    do
+    {
+        unsigned int idx = 0;
+
+        add_3D_tensor_argument(idx, _input1, slice_input1);
+        add_3D_tensor_argument(idx, _input2, slice_input2);
+        add_3D_tensor_argument(idx, _output, slice);
+
+        enqueue(queue, *this, slice, lws_hint());
+
+        collapsed.slide_window_slice_3D(slice_input1);
+        collapsed.slide_window_slice_3D(slice_input2);
+    }
+    while(collapsed.slide_window_slice_3D(slice));
+}
+
+BorderSize CLElementwiseOperationKernel::border_size() const
+{
+    const unsigned int replicateSize = _output->info()->dimension(0) - std::min(_input1->info()->dimension(0), _input2->info()->dimension(0));
+    const unsigned int border        = std::min<unsigned int>(num_elems_processed_per_iteration - 1U, replicateSize);
+    return BorderSize(0, border, 0, 0);
+}
+
+/** Arithmetic operations with saturation*/
+
+void CLSaturatedArithmeticOperationKernel::configure(ArithmeticOperation op, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, const ConvertPolicy &policy)
+{
+    _policy = policy;
+    _op     = op;
+    configure_common(input1, input2, output);
+}
+
+Status CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation op, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, const ConvertPolicy &policy)
+{
+    ARM_COMPUTE_UNUSED(op, policy);
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output);
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_with_arithmetic_rules(*input1, *input2, *output));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_for_arithmetic_operators(*input1->clone(), *input2->clone(), *output->clone()).first);
+
+    return Status{};
+}
+
+std::pair<Status, Window> CLSaturatedArithmeticOperationKernel::validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output)
+{
+    return validate_and_configure_window_for_arithmetic_operators(input1, input2, output);
+}
+
+Status CLSaturatedArithmeticOperationKernel::validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output)
+{
+    return validate_arguments_with_arithmetic_rules(input1, input2, output);
+}
+
+CLBuildOptions CLSaturatedArithmeticOperationKernel::generate_build_options(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output)
+{
+    const bool has_float_out = is_data_type_float(output.data_type());
+    auto       build_options = generate_build_options_with_arithmetic_rules(input1, input2, output, name());
+    build_options.add_option((_policy == ConvertPolicy::WRAP || has_float_out) ? "-DWRAP" : "-DSATURATE");
+    return build_options;
+}
+std::string CLSaturatedArithmeticOperationKernel::generate_id_for_tuning(const std::string &kernel_name, const ITensorInfo &input1, const ITensorInfo &output)
+{
+    auto config_id = generate_id_for_tuning_common(kernel_name, input1, output);
+    config_id += (_policy == ConvertPolicy::WRAP) ? "_wrap_" : "_saturate_";
+    config_id += lower_string(string_from_data_layout(input1.data_layout()));
+    return config_id;
+}
+
+std::string CLSaturatedArithmeticOperationKernel::name()
+{
+    return supported_sat_arithmetic_ops[_op];
+}
+
+/** Arithmetic operations*/
+
+void CLArithmeticOperationKernel::configure(ArithmeticOperation op, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output)
+{
+    _op = op;
+    configure_common(input1, input2, output);
+}
+
+Status CLArithmeticOperationKernel::validate(ArithmeticOperation op, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output)
+{
+    ARM_COMPUTE_UNUSED(op);
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output);
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_with_arithmetic_rules(*input1, *input2, *output));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_for_arithmetic_operators(*input1->clone(), *input2->clone(), *output->clone()).first);
+    return Status{};
+}
+std::pair<Status, Window> CLArithmeticOperationKernel::validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output)
+{
+    return validate_and_configure_window_for_arithmetic_operators(input1, input2, output);
+}
+Status CLArithmeticOperationKernel::validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output)
+{
+    return validate_arguments_with_arithmetic_rules(input1, input2, output);
+}
+
+CLBuildOptions CLArithmeticOperationKernel::generate_build_options(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output)
+{
+    return generate_build_options_with_arithmetic_rules(input1, input2, output, name());
+}
+std::string CLArithmeticOperationKernel::generate_id_for_tuning(const std::string &kernel_name, const ITensorInfo &input1, const ITensorInfo &output)
+{
+    return generate_id_for_tuning_common(kernel_name, input1, output);
+}
+
+std::string CLArithmeticOperationKernel::name()
+{
+    return supported_arithmetic_ops[_op];
+}
+} // namespace arm_compute