| Autor: |
Tao, Jin-Hua, Du, Zi-Dong, Guo, Qi, Lan, Hui-Ying, Zhang, Lei, Zhou, Sheng-Yuan, Xu, Ling-Jie, Liu, Cong, Liu, Hai-Feng, Tang, Shan, Rush, Allen, Chen, Willian, Liu, Shao-Li, Chen, Yun-Ji, Chen, Tian-Shi |
| Předmět: |
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| Zdroj: |
Journal of Computer Science & Technology (10009000); Jan2018, Vol. 33 Issue 1, p1-23, 23p |
| Abstrakt: |
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 B enchIP, a benchmark suite and benchmarking methodology for intelligence processors. The benchmark suite in B enchIP 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. B enchIP is utilized for evaluating various hardware platforms, including CPUs, GPUs, and accelerators. B enchIP will be open-sourced soon. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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