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/*
* Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright 2017 The TensorFlow Authors. 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.
*/
#ifndef __NNFW_CKER_GEMMLOWP_GEMM_SUPPORT_H__
#define __NNFW_CKER_GEMMLOWP_GEMM_SUPPORT_H__
#include <public/gemmlowp.h>
#include <memory>
#include <thread>
namespace nnfw
{
namespace cker
{
namespace gemm_support
{
struct GemmContext
{
std::unique_ptr<gemmlowp::GemmContext> gemm_context;
constexpr static int default_num_threadpool_threads = 4;
GemmContext()
{
int num_threads = std::thread::hardware_concurrency() / 2;
if (num_threads == 0)
{
num_threads = default_num_threadpool_threads;
}
gemm_context.reset(new gemmlowp::GemmContext());
gemm_context->set_max_num_threads(num_threads);
}
static inline GemmContext &GetGemmLowpContext()
{
static GemmContext instance;
return instance;
}
};
inline gemmlowp::GemmContext *GetGemmLowpContext()
{
auto &ctx = GemmContext::GetGemmLowpContext();
return ctx.gemm_context.get();
}
} // namespace gemm_support
} // namespace cker
} // namespace nnfw
#endif // __NNFW_CKER_GEMMLOWP_GEMM_SUPPORT_H__
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