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diff --git a/tools/kbenchmark/kernels/acl_cl/Convolution.cpp b/tools/kbenchmark/kernels/acl_cl/Convolution.cpp
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+++ b/tools/kbenchmark/kernels/acl_cl/Convolution.cpp
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+/*
+ * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * @file Conv2D benchmark with various algorithms (draft version)
+ */
+
+#include <nonius/nonius.h++>
+
+#include <arm_compute/core/Types.h>
+#include <arm_compute/runtime/CL/CLScheduler.h>
+#include <arm_compute/runtime/CL/CLFunctions.h>
+
+#include <cstdint>
+#include <cassert>
+#include <stdexcept>
+
+using namespace arm_compute;
+
+//
+// Helpers
+//
+namespace
+{
+
+enum Layout
+{
+ NCHW,
+ NHWC
+};
+
+struct Initializer
+{
+ Initializer() { CLScheduler::get().default_init(); }
+};
+
+Initializer initializer;
+
+TensorInfo make_info(uint32_t N)
+{
+ TensorShape shape{N};
+ return TensorInfo{shape, 1, DataType::F32};
+}
+
+template <enum Layout> TensorInfo make_info(uint32_t N, uint32_t C, uint32_t H, uint32_t W);
+
+template <> TensorInfo make_info<NCHW>(uint32_t N, uint32_t C, uint32_t H, uint32_t W)
+{
+ TensorShape shape{W, H, C, N};
+ TensorInfo info{shape, 1, DataType::F32};
+ info.set_data_layout(DataLayout::NCHW);
+ return info;
+}
+
+template <> TensorInfo make_info<NHWC>(uint32_t N, uint32_t C, uint32_t H, uint32_t W)
+{
+ TensorShape shape{C, W, H, N};
+ TensorInfo info{shape, 1, DataType::F32};
+ info.set_data_layout(DataLayout::NHWC);
+ return info;
+}
+
+inline void check(const Status &status)
+{
+ if (!status)
+ {
+ std::cerr << status.error_description() << std::endl;
+ throw std::runtime_error{"ERROR"};
+ }
+}
+
+inline bool is_odd(uint32_t n) { return (n % 2 != 0) ? true : false; }
+
+} // namespace
+
+//
+// Benchmark Parameters
+//
+NONIUS_PARAM(BATCH, 1);
+
+NONIUS_PARAM(IFM_C, 3);
+NONIUS_PARAM(IFM_H, 244);
+NONIUS_PARAM(IFM_W, 244);
+
+NONIUS_PARAM(OFM_C, 3);
+NONIUS_PARAM(OFM_H, 244);
+NONIUS_PARAM(OFM_W, 244);
+
+NONIUS_PARAM(KER_H, 3);
+NONIUS_PARAM(KER_W, 3);
+
+NONIUS_PARAM(STRIDE_H, 1);
+NONIUS_PARAM(STRIDE_W, 1);
+
+NONIUS_PARAM(PADDING, std::string{"SAME"})
+NONIUS_PARAM(FUSED_ACT, std::string{"RELU"})
+
+//
+// Configuration Helpers
+//
+namespace
+{
+
+struct Configuration
+{
+ uint32_t ifm_N;
+ uint32_t ifm_C;
+ uint32_t ifm_H;
+ uint32_t ifm_W;
+
+ uint32_t ofm_N;
+ uint32_t ofm_C;
+ uint32_t ofm_H;
+ uint32_t ofm_W;
+
+ uint32_t ker_N;
+ uint32_t ker_C;
+ uint32_t ker_H;
+ uint32_t ker_W;
+
+ uint32_t vertical_stride;
+ uint32_t horizontal_stride;
+
+ std::string padding;
+ std::string fused_act;
+
+ uint32_t top_padding;
+ uint32_t bottom_padding;
+ uint32_t left_padding;
+ uint32_t right_padding;
+
+ Configuration(nonius::chronometer meter)
+ {
+ ifm_N = meter.param<BATCH>();
+ ifm_C = meter.param<IFM_C>();
+ ifm_H = meter.param<IFM_H>();
+ ifm_W = meter.param<IFM_W>();
+
+ ofm_N = meter.param<BATCH>();
+ ofm_C = meter.param<OFM_C>();
+ ofm_H = meter.param<OFM_H>();
+ ofm_W = meter.param<OFM_W>();
+
+ ker_N = meter.param<OFM_C>();
+ ker_C = meter.param<IFM_C>();
+ ker_H = meter.param<KER_H>();
+ ker_W = meter.param<KER_W>();
+
+ vertical_stride = meter.param<STRIDE_H>();
+ horizontal_stride = meter.param<STRIDE_W>();
+
+ padding = meter.param<PADDING>();
+ fused_act = meter.param<FUSED_ACT>();
+
+ assert((ifm_H - ker_H) % vertical_stride == 0);
+ assert((ifm_W - ker_H) % horizontal_stride == 0);
+
+ uint32_t const effective_ofm_H = (ifm_H - ker_H) / vertical_stride + 1;
+ uint32_t const effective_ofm_W = (ifm_W - ker_H) / horizontal_stride + 1;
+
+ assert(ofm_H >= effective_ofm_H);
+ assert(ofm_W >= effective_ofm_W);
+
+ uint32_t const pad_H = ofm_H - effective_ofm_H;
+ uint32_t const pad_W = ofm_W - effective_ofm_W;
+
+ top_padding = pad_H / 2;
+ bottom_padding = pad_H / 2;
+ left_padding = pad_W / 2;
+ right_padding = pad_W / 2;
+
+ if (is_odd(pad_H))
+ top_padding += 1;
+ if (is_odd(pad_W))
+ left_padding += 1;
+ }
+
+ template <Layout L> TensorInfo src_info() const
+ {
+ return make_info<L>(ifm_N, ifm_C, ifm_H, ifm_W);
+ }
+ template <Layout L> TensorInfo dst_info() const
+ {
+ return make_info<L>(ofm_N, ofm_C, ofm_H, ofm_W);
+ }
+ template <Layout L> TensorInfo ker_info() const
+ {
+ return make_info<L>(ker_N, ker_C, ker_H, ker_W);
+ }
+ TensorInfo bias_info(void) const { return make_info(ker_N); }
+
+ PadStrideInfo pad_stride_info(void) const
+ {
+ return PadStrideInfo{horizontal_stride,
+ vertical_stride,
+ left_padding,
+ right_padding,
+ top_padding,
+ bottom_padding,
+ DimensionRoundingType::FLOOR};
+ }
+};
+
+} // namespace
+
+//
+// Benchmark Implementations
+//
+namespace
+{
+
+inline nonius::benchmark_registry &local_benchmark_registry()
+{
+ static nonius::benchmark_registry registry;
+ return registry;
+}
+
+} // namespace
+
+#define NONIUS_LOCAL_BENCHMARK(name, ...) \
+ namespace \
+ { \
+ static ::nonius::benchmark_registrar \
+ NONIUS_DETAIL_UNIQUE_NAME(benchmark_registrar)(local_benchmark_registry(), name, \
+ __VA_ARGS__); \
+ }
+
+NONIUS_LOCAL_BENCHMARK("CLDirectConvolutionLayer_NCHW", [](nonius::chronometer meter) {
+ CLDirectConvolutionLayer conv;
+
+ // Configure
+ Configuration p{meter};
+
+ CLTensor src_tensor{};
+ CLTensor dst_tensor{};
+ CLTensor ker_tensor{};
+ CLTensor bias_tensor{};
+
+ src_tensor.allocator()->init(p.src_info<NCHW>());
+ dst_tensor.allocator()->init(p.dst_info<NCHW>());
+ ker_tensor.allocator()->init(p.ker_info<NCHW>());
+ bias_tensor.allocator()->init(p.bias_info());
+
+ try
+ {
+ check(conv.validate(src_tensor.info(), ker_tensor.info(), bias_tensor.info(), dst_tensor.info(),
+ p.pad_stride_info()));
+ }
+ catch (...)
+ {
+ meter.measure([&](int) {
+ // DO NOTHING
+ volatile int x = 0;
+ return x;
+ });
+ return;
+ }
+
+ conv.configure(&src_tensor, &ker_tensor, &bias_tensor, &dst_tensor, p.pad_stride_info());
+
+ src_tensor.allocator()->allocate();
+ ker_tensor.allocator()->allocate();
+ bias_tensor.allocator()->allocate();
+ dst_tensor.allocator()->allocate();
+
+ // Run!
+ meter.measure([&](int) {
+ conv.run();
+ CLScheduler::get().sync();
+ });
+})
+
+NONIUS_LOCAL_BENCHMARK("CLDirectConvolutionLayer_NHWC", [](nonius::chronometer meter) {
+ CLDirectConvolutionLayer conv;
+
+ // Configure
+ Configuration p{meter};
+
+ CLTensor src_tensor{};
+ CLTensor dst_tensor{};
+ CLTensor ker_tensor{};
+ CLTensor bias_tensor{};
+
+ src_tensor.allocator()->init(p.src_info<NHWC>());
+ dst_tensor.allocator()->init(p.dst_info<NHWC>());
+ ker_tensor.allocator()->init(p.ker_info<NHWC>());
+ bias_tensor.allocator()->init(p.bias_info());
+
+ try
+ {
+ check(conv.validate(src_tensor.info(), ker_tensor.info(), bias_tensor.info(), dst_tensor.info(),
+ p.pad_stride_info()));
+ }
+ catch (...)
+ {
+ meter.measure([&](int) {
+ // DO NOTHING
+ volatile int x = 0;
+ return x;
+ });
+ return;
+ }
+
+ conv.configure(&src_tensor, &ker_tensor, &bias_tensor, &dst_tensor, p.pad_stride_info());
+
+ src_tensor.allocator()->allocate();
+ ker_tensor.allocator()->allocate();
+ bias_tensor.allocator()->allocate();
+ dst_tensor.allocator()->allocate();
+
+ // Run!
+ meter.measure([&](int) {
+ conv.run();
+ CLScheduler::get().sync();
+ });
+})
+
+NONIUS_LOCAL_BENCHMARK("CLGEMMConvolutionLayer_NCHW", [](nonius::chronometer meter) {
+ CLGEMMConvolutionLayer conv;
+
+ // Configure
+ Configuration p{meter};
+
+ CLTensor src_tensor{};
+ CLTensor dst_tensor{};
+ CLTensor ker_tensor{};
+ CLTensor bias_tensor{};
+
+ src_tensor.allocator()->init(p.src_info<NCHW>());
+ dst_tensor.allocator()->init(p.dst_info<NCHW>());
+ ker_tensor.allocator()->init(p.ker_info<NCHW>());
+ bias_tensor.allocator()->init(p.bias_info());
+
+ try
+ {
+ check(conv.validate(src_tensor.info(), ker_tensor.info(), bias_tensor.info(), dst_tensor.info(),
+ p.pad_stride_info()));
+ }
+ catch (...)
+ {
+ meter.measure([&](int) {
+ // DO NOTHING
+ volatile int x = 0;
+ return x;
+ });
+ return;
+ }
+
+ conv.configure(&src_tensor, &ker_tensor, &bias_tensor, &dst_tensor, p.pad_stride_info());
+
+ src_tensor.allocator()->allocate();
+ ker_tensor.allocator()->allocate();
+ bias_tensor.allocator()->allocate();
+ dst_tensor.allocator()->allocate();
+
+ // Run
+ meter.measure([&](int) {
+ conv.run();
+ CLScheduler::get().sync();
+ });
+})
+
+NONIUS_LOCAL_BENCHMARK("CLGEMMConvolutionLayer_NHWC", [](nonius::chronometer meter) {
+ CLGEMMConvolutionLayer conv;
+
+ // Configure
+ Configuration p{meter};
+
+ CLTensor src_tensor{};
+ CLTensor dst_tensor{};
+ CLTensor ker_tensor{};
+ CLTensor bias_tensor{};
+
+ src_tensor.allocator()->init(p.src_info<NHWC>());
+ dst_tensor.allocator()->init(p.dst_info<NHWC>());
+ ker_tensor.allocator()->init(p.ker_info<NHWC>());
+ bias_tensor.allocator()->init(p.bias_info());
+
+ try
+ {
+ check(conv.validate(src_tensor.info(), ker_tensor.info(), bias_tensor.info(), dst_tensor.info(),
+ p.pad_stride_info()));
+ }
+ catch (...)
+ {
+ meter.measure([&](int) {
+ // DO NOTHING
+ volatile int x = 0;
+ return x;
+ });
+ return;
+ }
+
+ conv.configure(&src_tensor, &ker_tensor, &bias_tensor, &dst_tensor, p.pad_stride_info());
+
+ src_tensor.allocator()->allocate();
+ ker_tensor.allocator()->allocate();
+ bias_tensor.allocator()->allocate();
+ dst_tensor.allocator()->allocate();
+
+ // Run
+ meter.measure([&](int) {
+ conv.run();
+ CLScheduler::get().sync();
+ });
+})
+
+NONIUS_LOCAL_BENCHMARK("CLWinogradConvolutionLayer_NCHW", [](nonius::chronometer meter) {
+ CLWinogradConvolutionLayer conv;
+
+ // Configure
+ Configuration p{meter};
+
+ CLTensor src_tensor{};
+ CLTensor dst_tensor{};
+ CLTensor ker_tensor{};
+ CLTensor bias_tensor{};
+
+ src_tensor.allocator()->init(p.src_info<NCHW>());
+ dst_tensor.allocator()->init(p.dst_info<NCHW>());
+ ker_tensor.allocator()->init(p.ker_info<NCHW>());
+ bias_tensor.allocator()->init(p.bias_info());
+
+ try
+ {
+ check(conv.validate(src_tensor.info(), ker_tensor.info(), bias_tensor.info(), dst_tensor.info(),
+ p.pad_stride_info()));
+ }
+ catch (...)
+ {
+ meter.measure([&](int) {
+ // DO NOTHING
+ volatile int x = 0;
+ return x;
+ });
+ return;
+ }
+
+ conv.configure(&src_tensor, &ker_tensor, &bias_tensor, &dst_tensor, p.pad_stride_info());
+
+ src_tensor.allocator()->allocate();
+ ker_tensor.allocator()->allocate();
+ bias_tensor.allocator()->allocate();
+ dst_tensor.allocator()->allocate();
+
+ // Run
+ meter.measure([&](int) {
+ conv.run();
+ CLScheduler::get().sync();
+ });
+})
+
+NONIUS_LOCAL_BENCHMARK("CLWinogradConvolutionLayer_NHWC", [](nonius::chronometer meter) {
+ CLWinogradConvolutionLayer conv;
+
+ // Configure
+ Configuration p{meter};
+
+ CLTensor src_tensor{};
+ CLTensor dst_tensor{};
+ CLTensor ker_tensor{};
+ CLTensor bias_tensor{};
+
+ src_tensor.allocator()->init(p.src_info<NHWC>());
+ dst_tensor.allocator()->init(p.dst_info<NHWC>());
+ ker_tensor.allocator()->init(p.ker_info<NHWC>());
+ bias_tensor.allocator()->init(p.bias_info());
+
+ try
+ {
+ check(conv.validate(src_tensor.info(), ker_tensor.info(), bias_tensor.info(), dst_tensor.info(),
+ p.pad_stride_info()));
+ }
+ catch (...)
+ {
+ meter.measure([&](int) {
+ // DO NOTHING
+ volatile int x = 0;
+ return x;
+ });
+ return;
+ }
+
+ conv.configure(&src_tensor, &ker_tensor, &bias_tensor, &dst_tensor, p.pad_stride_info());
+
+ src_tensor.allocator()->allocate();
+ ker_tensor.allocator()->allocate();
+ bias_tensor.allocator()->allocate();
+ dst_tensor.allocator()->allocate();
+
+ // Run
+ meter.measure([&](int) {
+ conv.run();
+ CLScheduler::get().sync();
+ });
+})
+
+extern "C" nonius::benchmark_registry &benchmark_functions(void)
+{
+ return local_benchmark_registry();
+}
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