BENCHIP： Benchmarking Intelligence Processors
BENCHIP： Benchmarking Intelligence Processors作者机构：State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100049 China Intelligent Processor Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Cambricon Ltd. Beijing 100190 China A libaba Infrastructure Service A libaba Group Hangzhou 311121 China Iflytek Co. Ltd. Hefei 230088 China Beijing Jingdong Century Trading Co. Ltd. Beijing 100176 China RDA Microdectronics Inc. Shanghai 201203 China Advanced Micro Devices Inc. Sunnyvale CA 94085 U.S.A.
出 版 物：《Journal of Computer Science & Technology》 (计算机科学技术学报（英文版）)
年 卷 期：2018年第33卷第1期
页 面：1-23页
核心收录：
学科分类：0402[教育学-心理学(可授教育学、理学学位）] 040203[教育学-应用心理学] 04[教育学] 08[工学] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：This work is partially supported by the National Key Research and Development Program of China under Grant No. 2017YFB1003101 the National Natural Science Foundation of China under Grant Nos. 61472396 61432016 61473275 61522211 61532016 61521092 61502446 61672491 61602441 61602446 61732002 and 61702478 Beijing Science and Technology Projects under Grant No. Z151100000915072 the Science and Technology Service Network Initiative (STS) Projects of Chinese Academy of Sciences and the National Basic Research 973 Program of China under Grant No. 2015CB358800
主 题：deep learning intelligence processor benchmark
摘 要：The increasing attention on deep learning has tremendously spurred the design of intelligence processing hardware. The variety of emerging intelligence processors requires standard benchmarks for fair comparison and system optimization （in both software and hardware）. However, existing benchmarks are unsuitable for benchmarking intelligence processors due to their non-diversity and nonrepresentativeness. Also, the lack of a standard benchmarking methodology further exacerbates this problem. In this paper, we propose BENCHIP, a benchmark suite and benchmarking methodology for intelligence processors. The benchmark suite in BENCHIP consists of two sets of benchmarks： microbenchmarks and macrobenchmarks. The microbenchmarks consist of single-layer networks, They are mainly designed for bottleneck analysis and system optimization. The macrobenchmarks contain state-of-the-art industrial networks, so as to offer a realistic comparison of different platforms. We also propose a standard benchmarking methodology built upon an industrial software stack and evaluation metrics that comprehensively reflect various characteristics of the evaluated intelligence processors, BENCHIP is utilized for evaluating various hardware platforms, including CPUs, GPUs, and accelerators. BENCHIP will be open-sourced soon.
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