BENCHIP： Benchmarking Intelligence Processors  

作　　者：Jin-Hua Tao Zi-Dong Du Qi Guo Hui-Ying Lan Lei Zhang Sheng-Yuan Zhou Ling-Jie Xu Cong Liu Hai-Feng Liu Shah Tang Allen Rush Willian Chen Shao-Li Liu Yun-Ji Chen Tian-Shi Chen 

机构地区：[1]State Key Laboratory of Computer Architecture, Institute of Computing Technology Chinese Academy of Sciences, Beijing 100190, China [2]School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100049, China [3]Intelligent Processor Research Center, Institute of Computing Technology, Chinese Academy of Sciences Beijing 100190, China [4]Cambricon Ltd., Beijing 100190, China [5]A libaba Infrastructure Service, A libaba Group, Hangzhou 311121, China [6]Iflytek Co., Ltd., Hefei 230088, China [7]Beijing Jingdong Century Trading Co., Ltd., Beijing 100176, China [8]RDA Microdectronics, Inc., Shanghai 201203, China [9]Advanced Micro Devices Inc., Sunnyvale, CA 94085, U.S.A.

出　　处：《Journal of Computer Science & Technology》2018年第1期1-23,共23页计算机科学技术学报（英文版）

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.

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.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.

关 键 词：deep learning intelligence processor BENCHMARK 

分 类 号：TP332[自动化与计算机技术—计算机系统结构] B848.5[自动化与计算机技术—计算机科学与技术]