Bench IP: Benchmarking Intelligence Processors
文献类型:期刊论文
作者 | Tao, Jin-Hua2,3,4; Du, Zi-Dong2,4,5; Guo, Qi2,4,5; Lan, Hui-Ying2,4; Zhang, Lei2,4; Zhou, Sheng-Yuan2,4; Xu, Ling-Jie6; Liu, Cong7; Liu, Hai-Feng8; Tang, Shan9 |
刊名 | JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
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出版日期 | 2018 |
卷号 | 33期号:1页码:1-23 |
关键词 | deep learning intelligence processor benchmark |
ISSN号 | 1000-9000 |
DOI | 10.1007/s11390-018-1805-8 |
英文摘要 | 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. |
资助项目 | National Key Research and Development Program of China[2017YFB1003101] ; National Natural Science Foundation of China[61472396] ; National Natural Science Foundation of China[61432016] ; National Natural Science Foundation of China[61473275] ; National Natural Science Foundation of China[61522211] ; National Natural Science Foundation of China[61532016] ; National Natural Science Foundation of China[61521092] ; National Natural Science Foundation of China[61502446] ; National Natural Science Foundation of China[61672491] ; National Natural Science Foundation of China[61602441] ; National Natural Science Foundation of China[61602446] ; National Natural Science Foundation of China[61732002] ; National Natural Science Foundation of China[61702478] ; Beijing Science and Technology Projects[Z151100000915072] ; Science and Technology Service Network Initiative (STS) Projects of Chinese Academy of Sciences ; National Basic Research 973 Program of China[2015CB358800] |
WOS研究方向 | Computer Science |
语种 | 英语 |
WOS记录号 | WOS:000423587100001 |
出版者 | SCIENCE PRESS |
源URL | [http://119.78.100.204/handle/2XEOYT63/5599] ![]() |
专题 | 中国科学院计算技术研究所期刊论文_英文 |
通讯作者 | Tao, Jin-Hua |
作者单位 | 1.Adv Micro Devices Inc, Sunnyvale, CA 94085 USA 2.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Intelligent Processor Res Ctr, Beijing 100190, Peoples R China 5.Cambricon Ltd, Beijing 100190, Peoples R China 6.Alibaba Grp, Alibaba Infrastruct Serv, Hangzhou 311121, Zhejiang, Peoples R China 7.Iflytek Co Ltd, Hefei 230088, Anhui, Peoples R China 8.Beijing Jingdong Century Trading Co Ltd, Beijing 100176, Peoples R China 9.RDA Microelect Inc, Shanghai 201203, Peoples R China |
推荐引用方式 GB/T 7714 | Tao, Jin-Hua,Du, Zi-Dong,Guo, Qi,et al. Bench IP: Benchmarking Intelligence Processors[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2018,33(1):1-23. |
APA | Tao, Jin-Hua.,Du, Zi-Dong.,Guo, Qi.,Lan, Hui-Ying.,Zhang, Lei.,...&Chen, Tian-Shi.(2018).Bench IP: Benchmarking Intelligence Processors.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,33(1),1-23. |
MLA | Tao, Jin-Hua,et al."Bench IP: Benchmarking Intelligence Processors".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 33.1(2018):1-23. |
入库方式: OAI收割
来源:计算技术研究所
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