COMPMID-1816: Use parallel reduction on 0 axis in CL ARG_MIN/ARG_MAX

Introducing new CLArgMinMax kernel

Change-Id: I0b8254207cc3859d19ceef9b6429cf5c1c586db0
Signed-off-by: Manuel Bottini <manuel.bottini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/2202
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michalis Spyrou <michalis.spyrou@arm.com>

1 files changed, 431 insertions, 0 deletions

diff --git a/src/core/CL/cl_kernels/arg_min_max.cl b/src/core/CL/cl_kernels/arg_min_max.cl
new file mode 100644
index 0000000000..3f75377636
--- /dev/null
+++ b/src/core/CL/cl_kernels/arg_min_max.cl
@@ -0,0 +1,431 @@
+/*
+ * Copyright (c) 2019 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"
+
+#if defined(ARG_MAX)
+#define CONDITION_TO_USE(x, y) ISGREATER(x, y)
+#elif defined(ARG_MIN)
+#define CONDITION_TO_USE(x, y) ISLESS(x, y)
+#else // !(defined(ARG_MAX) || defined(ARG_MIN))
+#error "Unsupported reduction operation!"
+#endif // defined(ARG_MAX)
+
+#if defined(DATA_TYPE_OUTPUT)
+#if defined(WIDTH)
+#if defined(ARG_MIN)
+#if defined(PREV_OUTPUT)
+/** Find index minimum value of a vector
+ *
+ * @param[in] input Pointer to the first value.
+ *
+ * @return index of the vector.
+ */
+inline DATA_TYPE_OUTPUT arg_idx_min_prev_out(__global const DATA_TYPE *input, __global const DATA_TYPE_OUTPUT *prev_res, const int x_idx)
+{
+    int end_elem = (x_idx + 1) * 16;
+    if(end_elem > WIDTH)
+    {
+        end_elem = WIDTH - x_idx * 16;
+    }
+    DATA_TYPE_OUTPUT res = prev_res[0];
+    for(int x_v = 1; x_v < end_elem; ++x_v)
+    {
+        res = select(res, prev_res[x_v], *(input + prev_res[x_v]) < * (input + res));
+    }
+    return res;
+}
+#else // !defined(PREV_OUTPUT)
+/** Find index minimum value of a vector
+ *
+ * @param[in] input Pointer to the first value.
+ *
+ * @return index of the vector.
+ */
+inline DATA_TYPE_OUTPUT arg_idx_min(__global const DATA_TYPE *input, const int x_idx)
+{
+#if WIDTH < 16
+    DATA_TYPE_OUTPUT res = 0;
+    for(DATA_TYPE_OUTPUT x_v = res + 1; x_v < WIDTH; ++x_v)
+    {
+        res = select(res, x_v, *(input + x_v) < * (input + res));
+    }
+    return res;
+#else  // WIDTH >= 16
+    int       x_elem   = x_idx * 16;
+    const int x_goback = select(0, 16 - WIDTH % 16, x_elem + 16 > WIDTH);
+    x_elem -= x_goback;
+
+    VEC_DATA_TYPE(DATA_TYPE, 16)
+    in = vload16(0, input - x_goback);
+    VEC_DATA_TYPE(DATA_TYPE_OUTPUT, 16)
+    res = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 };
+
+    VEC_DATA_TYPE(COND_DATA_TYPE, 8)
+    idx_sel       = (in.s01234567 <= in.s89abcdef);
+    in.s01234567  = select(in.s89abcdef, in.s01234567, idx_sel);
+    res.s01234567 = select(res.s89abcdef, res.s01234567, CONVERT(idx_sel, int8));
+
+    idx_sel.s0123 = (in.s0123 < in.s4567) || (in.s0123 == in.s4567 && CONVERT((res.s0123 < res.s4567), VEC_DATA_TYPE(COND_DATA_TYPE, 4)));
+    in.s0123      = select(in.s4567, in.s0123, idx_sel.s0123);
+    res.s0123     = select(res.s4567, res.s0123, CONVERT(idx_sel.s0123, int4));
+
+    idx_sel.s01 = (in.s01 < in.s23) || (in.s01 == in.s23 && CONVERT((res.s01 < res.s23), VEC_DATA_TYPE(COND_DATA_TYPE, 2)));
+    in.s01      = select(in.s23, in.s01, idx_sel.s01);
+    res.s01     = select(res.s23, res.s01, CONVERT(idx_sel.s01, int2));
+
+    idx_sel.s0 = (in.s0 < in.s1) || (in.s0 == in.s1 && CONVERT((res.s0 < res.s1), COND_DATA_TYPE));
+    res.s0     = select(res.s1, res.s0, CONVERT(idx_sel.s0, int));
+
+    return res.s0 + x_elem;
+#endif // WIDTH < 16
+}
+#endif // defined(PREV_OUTPUT)
+#endif // defined(ARG_MIN)
+#if defined(ARG_MAX)
+#if defined(PREV_OUTPUT)
+/** Find index maximum value of a vector
+ *
+ * @param[in] input Pointer to the first value.
+ *
+ * @return index of the vector.
+ */
+inline DATA_TYPE_OUTPUT arg_idx_max_prev_out(__global const DATA_TYPE *input, __global const DATA_TYPE_OUTPUT *prev_res, const int x_idx)
+{
+    int end_elem = (x_idx + 1) * 16;
+    if(end_elem > WIDTH)
+    {
+        end_elem = WIDTH - x_idx * 16;
+    }
+    DATA_TYPE_OUTPUT res = prev_res[0];
+    for(int x_v = 1; x_v < end_elem; ++x_v)
+    {
+        res = select(res, prev_res[x_v], *(input + prev_res[x_v]) > *(input + res));
+    }
+    return res;
+}
+#else // !defined(PREV_OUTPUT)
+/** Find index maximum value of a vector
+ *
+ * @param[in] input Pointer to the first value.
+ *
+ * @return index of the vector.
+ */
+inline DATA_TYPE_OUTPUT arg_idx_max(__global const DATA_TYPE *input, const int x_idx)
+{
+#if WIDTH < 16
+    DATA_TYPE_OUTPUT res = 0;
+    for(DATA_TYPE_OUTPUT x_v = res + 1; x_v < WIDTH; ++x_v)
+    {
+        res = select(res, x_v, *(input + x_v) > *(input + res));
+    }
+    return res;
+#else  // WIDTH >= 16
+    int       x_elem   = x_idx * 16;
+    const int x_goback = select(0, 16 - WIDTH % 16, x_elem + 16 > WIDTH);
+    x_elem -= x_goback;
+
+    VEC_DATA_TYPE(DATA_TYPE, 16)
+    in = vload16(0, input - x_goback);
+    VEC_DATA_TYPE(DATA_TYPE_OUTPUT, 16)
+    res = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 };
+
+    VEC_DATA_TYPE(COND_DATA_TYPE, 8)
+    idx_sel       = (in.s01234567 >= in.s89abcdef);
+    in.s01234567  = select(in.s89abcdef, in.s01234567, idx_sel);
+    res.s01234567 = select(res.s89abcdef, res.s01234567, CONVERT(idx_sel, int8));
+
+    idx_sel.s0123 = (in.s0123 > in.s4567) || (in.s0123 == in.s4567 && CONVERT((res.s0123 < res.s4567), VEC_DATA_TYPE(COND_DATA_TYPE, 4)));
+    in.s0123      = select(in.s4567, in.s0123, idx_sel.s0123);
+    res.s0123     = select(res.s4567, res.s0123, CONVERT(idx_sel.s0123, int4));
+
+    idx_sel.s01 = (in.s01 > in.s23) || (in.s01 == in.s23 && CONVERT((res.s01 < res.s23), VEC_DATA_TYPE(COND_DATA_TYPE, 2)));
+    in.s01      = select(in.s23, in.s01, idx_sel.s01);
+    res.s01     = select(res.s23, res.s01, CONVERT(idx_sel.s01, int2));
+
+    idx_sel.s0 = (in.s0 > in.s1) || (in.s0 == in.s1 && CONVERT((res.s0 < res.s1), COND_DATA_TYPE));
+    res.s0     = select(res.s1, res.s0, CONVERT(idx_sel.s0, int));
+
+    return res.s0 + x_elem;
+#endif // WIDTH < 16
+}
+#endif // defined(PREV_OUTPUT)
+#endif // defined(ARG_MAX)
+
+/** This kernel performs parallel reduction given an operation on x-axis.
+ *
+ * @note In case the results of previous stages are passed the flag PREV_OUTPUT has to be passed using -DPREV_OUTPUT
+ * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
+ * @note The data type of the output must be passed at compile time using -DDATA_TYPE_OUTPUT: e.g. -DDATA_TYPE_OUTPUT=uint
+ * @note The arg_max flag must be passed at compile time using -DARG_MAX if we want to compute the ArgMax
+ * @note The arg_min flag must be passed at compile time using -DARG_MIN if we want to compute the ArgMin
+ *
+ * @param[in] src_ptr                                   Pointer to the source tensor. Supported data types: S32/F16/F32
+ * @param[in] src_stride_x                              Stride of the source tensor in X dimension (in bytes)
+ * @param[in] src_step_x                                src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y                              Stride of the source tensor in Y dimension (in bytes)
+ * @param[in] src_step_y                                src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes         The offset of the first element in the source tensor
+ * @param[in] prev_res_ptr                              (Optional) Pointer to previous results tensor. Supported data types: U32/S32
+ * @param[in] prev_res_stride_x                         (Optional) Stride of the output tensor in X dimension (in bytes)
+ * @param[in] prev_res_step_x                           (Optional) prev_res_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] prev_res_stride_y                         (Optional) Stride of the output tensor in Y dimension (in bytes)
+ * @param[in] prev_res_step_y                           (Optional) prev_res_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] prev_res_offset_first_element_in_bytes    (Optional) The offset of the first element in the previous results tensor
+ * @param[in] partial_res_ptr                           The local buffer to hold partial result values. Supported data types: U32/S32
+ * @param[in] partial_res_stride_x                      Stride of the output tensor in X dimension (in bytes)
+ * @param[in] partial_res_step_x                        partial_res_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] partial_res_stride_y                      Stride of the output tensor in Y dimension (in bytes)
+ * @param[in] partial_res_step_y                        partial_res_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] partial_res_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[in] local_results                             Local buffer for storing the partial result
+ */
+__kernel void arg_min_max_x(
+    IMAGE_DECLARATION(src),
+#if defined(PREV_OUTPUT)
+    IMAGE_DECLARATION(prev_res),
+#endif // defined(PREV_OUTPUT)
+    IMAGE_DECLARATION(partial_res),
+    __local DATA_TYPE_OUTPUT *local_results)
+{
+#if defined(PREV_OUTPUT)
+    Image src      = CONVERT_TO_IMAGE_STRUCT_NO_STEP(src);
+    Image prev_res = CONVERT_TO_IMAGE_STRUCT(prev_res);
+#else  // !defined(PREV_OUTPUT)
+    Image src                      = CONVERT_TO_IMAGE_STRUCT(src);
+#endif // defined(PREV_OUTPUT)
+    Image partial_res = CONVERT_TO_IMAGE_STRUCT(partial_res);
+
+    unsigned int lsize = get_local_size(0);
+    unsigned int lid   = get_local_id(0);
+
+    const uint     x_idx                 = get_global_id(0);
+    const uint     y_idx                 = get_global_id(1);
+    const __global DATA_TYPE *src_in_row = (const __global DATA_TYPE *)(src_ptr + src_offset_first_element_in_bytes + y_idx * src_step_y);
+
+    for(unsigned int y = 0; y < get_local_size(1); ++y)
+    {
+#if defined(ARG_MAX)
+#if defined(PREV_OUTPUT)
+        local_results[lid] = arg_idx_max_prev_out(src_in_row, (__global DATA_TYPE_OUTPUT *)offset(&prev_res, 0, y), x_idx);
+#else  // !defined(PREV_OUTPUT)
+        local_results[lid] = arg_idx_max((__global DATA_TYPE *)offset(&src, 0, y), x_idx);
+#endif // defined(PREV_OUTPUT)
+#else  // defined(ARG_MIN)
+#if defined(PREV_OUTPUT)
+        local_results[lid]         = arg_idx_min_prev_out(src_in_row, (__global DATA_TYPE_OUTPUT *)offset(&prev_res, 0, y), x_idx);
+#else  // !defined(PREV_OUTPUT)
+        local_results[lid] = arg_idx_min((__global DATA_TYPE *)offset(&src, 0, y), x_idx);
+#endif // defined(PREV_OUTPUT)
+#endif // defined(ARG_MAX) || defined(ARG_MIN)
+
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        // Perform parallel reduction
+        for(unsigned int i = lsize >> 1; i > 0; i >>= 1)
+        {
+            if(lid < i)
+            {
+                DATA_TYPE tmp0 = *(src_in_row + local_results[lid]);
+                DATA_TYPE tmp1 = *(src_in_row + local_results[lid + i]);
+#if defined(ARG_MAX)
+                local_results[lid] = select(
+                                         local_results[lid],
+                                         local_results[lid + i],
+                                         ((tmp0 == tmp1) && (local_results[lid + i] < local_results[lid])) || (tmp0 < tmp1));
+#else  // defined(ARG_MIN)
+                local_results[lid] = select(
+                                         local_results[lid],
+                                         local_results[lid + i],
+                                         ((tmp0 == tmp1) && (local_results[lid + i] < local_results[lid])) || (tmp0 > tmp1));
+#endif // defined(ARG_MAX) || defined(ARG_MIN)
+            }
+            barrier(CLK_LOCAL_MEM_FENCE);
+        }
+
+        if(lid == 0)
+        {
+            ((__global DATA_TYPE_OUTPUT *)offset(&partial_res, get_group_id(0), y))[0] = local_results[0];
+        }
+    }
+}
+#endif // defined(WIDTH)
+
+#if defined(HEIGHT)
+/** This kernel performs reduction on y-axis.
+ *
+ * @note The input data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
+ * @note The data type of the output must be passed at compile time using -DDATA_TYPE_OUTPUT: e.g. -DDATA_TYPE_OUTPUT=uint
+ * @note The data type of the intermediate results must be passed at compile time using -DDATA_TYPE_PROMOTED: e.g. -DDATA_TYPE_PROMOTED=uint
+ * @note The height size must be passed at compile time using -DHEIGHT e.g. -DHEIGHT=128
+ *
+ * @param[in] src_ptr                              Pointer to the source tensor. Supported data types: S32/F16/F32
+ * @param[in] src_stride_x                         Stride of the source tensor in X dimension (in bytes)
+ * @param[in] src_step_x                           src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y                         Stride of the source tensor in Y dimension (in bytes)
+ * @param[in] src_step_y                           src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes    The offset of the first element in the source tensor
+ * @param[in] output_ptr                           The local buffer to hold sumed values. Supported data types: U32/S32
+ * @param[in] output_stride_x                      Stride of the output tensor in X dimension (in bytes)
+ * @param[in] output_step_x                        output_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] output_stride_y                      Stride of the output tensor in Y dimension (in bytes)
+ * @param[in] output_step_y                        output_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] output_offset_first_element_in_bytes The offset of the first element in the source tensor
+ */
+__kernel void arg_min_max_y(
+    IMAGE_DECLARATION(src),
+    IMAGE_DECLARATION(output))
+{
+    Image src    = CONVERT_TO_IMAGE_STRUCT(src);
+    Image output = CONVERT_TO_IMAGE_STRUCT(output);
+
+    VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16)
+    res = CONVERT(vload16(0, (__global DATA_TYPE *)offset(&src, 0, 0)), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16));
+
+    VEC_DATA_TYPE(DATA_TYPE_OUTPUT, 16)
+    indx = 0;
+    for(unsigned int y = 1; y < HEIGHT; ++y)
+    {
+        VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16)
+        in = CONVERT(vload16(0, (__global DATA_TYPE *)offset(&src, 0, y)), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16));
+
+        VEC_DATA_TYPE(DATA_TYPE_OUTPUT, 16)
+        cond_conv = CONVERT(CONDITION_TO_USE(in, res), VEC_DATA_TYPE(DATA_TYPE_OUTPUT, 16));
+        indx      = select(indx, y, cond_conv);
+        res       = select(res, in, CONDITION_TO_USE(in, res));
+    }
+
+    // Store result
+    vstore16(indx, 0, (__global DATA_TYPE_OUTPUT *)output.ptr);
+}
+#endif // defined(HEIGHT)
+
+#if defined(DEPTH)
+/** This kernel performs reduction on z-axis.
+ *
+ * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
+ * @note The data type of the intermediate results must be passed at compile time using -DDATA_TYPE_PROMOTED: e.g. -DDATA_TYPE_PROMOTED=uint
+ * @note The depth size must be passed at compile time using -DDEPTH e.g. -DDEPTH=128
+ *
+ * @param[in] input_ptr                            Pointer to the source tensor. Supported data types: S32/F16/F32
+ * @param[in] input_stride_x                       Stride of the source tensor in X dimension (in bytes)
+ * @param[in] input_step_x                         input_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] input_stride_y                       Stride of the source tensor in Y dimension (in bytes)
+ * @param[in] input_step_y                         input_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] input_stride_z                       Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] input_step_z                         input_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] input_offset_first_element_in_bytes  The offset of the first element in the source tensor
+ * @param[in] output_ptr                           The local buffer to hold sumed values. Supported data types: U32/S32
+ * @param[in] output_stride_x                      Stride of the output tensor in X dimension (in bytes)
+ * @param[in] output_step_x                        output_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] output_stride_y                      Stride of the output tensor in Y dimension (in bytes)
+ * @param[in] output_step_y                        output_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] output_stride_z                      Stride of the output tensor in Z dimension (in bytes)
+ * @param[in] output_step_z                        output_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] output_offset_first_element_in_bytes The offset of the first element in the source tensor
+ */
+__kernel void arg_min_max_z(
+    TENSOR3D_DECLARATION(input),
+    TENSOR3D_DECLARATION(output))
+{
+    Tensor3D input  = CONVERT_TO_TENSOR3D_STRUCT(input);
+    Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output);
+
+    VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16)
+    res = CONVERT(vload16(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0)), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16));
+
+    VEC_DATA_TYPE(DATA_TYPE_OUTPUT, 16)
+    indx = 0;
+    for(DATA_TYPE_OUTPUT z = 1; z < DEPTH; ++z)
+    {
+        VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16)
+        in = CONVERT(vload16(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, z)), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16));
+
+        VEC_DATA_TYPE(DATA_TYPE_OUTPUT, 16)
+        cond_conv = CONVERT(CONDITION_TO_USE(in, res), VEC_DATA_TYPE(DATA_TYPE_OUTPUT, 16));
+        indx      = select(indx, z, cond_conv);
+        res       = select(res, in, CONDITION_TO_USE(in, res));
+    }
+
+    // Store result
+    vstore16(indx, 0, (__global DATA_TYPE_OUTPUT *)output.ptr);
+}
+#endif /* defined(DEPTH) */
+
+#if defined(BATCH) && defined(DEPTH)
+/** This kernel performs reduction on w-axis.
+ *
+ * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
+ * @note The data type of the intermediate results must be passed at compile time using -DDATA_TYPE_PROMOTED: e.g. -DDATA_TYPE_PROMOTED=uint
+ * @note The batch size must be passed at compile time using -DBATCH e.g. -DBATCH=128
+ * @note The depth size must be passed at compile time using -DBATCH e.g. -DDEPTH=128
+ *
+ * @param[in] input_ptr                            Pointer to the source tensor. Supported data types: S32/F16/F32
+ * @param[in] input_stride_x                       Stride of the source tensor in X dimension (in bytes)
+ * @param[in] input_step_x                         input_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] input_stride_y                       Stride of the source tensor in Y dimension (in bytes)
+ * @param[in] input_step_y                         input_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] input_stride_z                       Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] input_step_z                         input_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] input_stride_w                       Stride of the source tensor in W dimension (in bytes)
+ * @param[in] input_step_w                         input_stride_w * number of elements along W processed per workitem(in bytes)
+ * @param[in] input_offset_first_element_in_bytes  The offset of the first element in the source tensor
+ * @param[in] output_ptr                           The local buffer to hold sumed values. Supported data types: U32/S32
+ * @param[in] output_stride_x                      Stride of the output tensor in X dimension (in bytes)
+ * @param[in] output_step_x                        output_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] output_stride_y                      Stride of the output tensor in Y dimension (in bytes)
+ * @param[in] output_step_y                        output_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] output_stride_z                      Stride of the output tensor in Z dimension (in bytes)
+ * @param[in] output_step_z                        output_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] output_stride_w                      Stride of the output tensor in W dimension (in bytes)
+ * @param[in] output_step_w                        output_stride_w * number of elements along W processed per workitem(in bytes)
+ * @param[in] output_offset_first_element_in_bytes The offset of the first element in the source tensor
+ */
+__kernel void arg_min_max_w(
+    TENSOR4D_DECLARATION(input),
+    TENSOR4D_DECLARATION(output))
+{
+    Tensor4D input  = CONVERT_TO_TENSOR4D_STRUCT(input, DEPTH);
+    Tensor4D output = CONVERT_TO_TENSOR4D_STRUCT(output, DEPTH);
+
+    VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16)
+    res = CONVERT(vload16(0, (__global DATA_TYPE *)tensor4D_offset(&input, 0, 0, 0, 0)), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16));
+
+    VEC_DATA_TYPE(DATA_TYPE_OUTPUT, 16)
+    indx = 0;
+    for(DATA_TYPE_OUTPUT w = 1; w < BATCH; ++w)
+    {
+        VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16)
+        in = CONVERT(vload16(0, (__global DATA_TYPE *)tensor4D_offset(&input, 0, 0, 0, w)), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16));
+
+        VEC_DATA_TYPE(DATA_TYPE_OUTPUT, 16)
+        cond_conv = CONVERT(CONDITION_TO_USE(in, res), VEC_DATA_TYPE(DATA_TYPE_OUTPUT, 16));
+        indx      = select(indx, w, cond_conv);
+        res       = select(res, in, CONDITION_TO_USE(in, res));
+    }
+
+    // Store result
+    vstore16(indx, 0, (__global DATA_TYPE_OUTPUT *)output.ptr);
+}
+#endif /* defined(BATCH) && defined(DEPTH) */
+#endif // defined(DATA_TYPE_OUTPUT)
\ No newline at end of file