Summary

AIBench Micro is a part of the AIBench benchmarking suite. AIBench Micro and other AIBench benchmarks together resolve the AI benchmarking challenges. Training an entire session is mandatory when evaluating a system's performance, which is a prohibitive cost for early-stage accelerators' design. AIBench Micro selects the basic AI operators and widely used hotspot kernel functions from AIBench Training and AIBench Inference as microbenchmarks to tackle the cost challenge. It can also provide different input sizes to cover different design spaces.

The Methodology

Through investigating neural networks from AIBench Training and AIBench Inference, we abstract commonly used AI operators, including conv, relu, etc. Also, we use NVProf to analyze AIBench Training and AIBench Inference and select the most time-costly kernel functions as benchmarks. Optimizing these hotspot functions can help design better hardware and software.

The Benchmarks

AIBench Micro now contains twelve AI basic operators, and more AI operators like LSTM will be added. Moreover, hotspot functions are under development. The following are twelve AI basic operators:


Contributors

Prof. Jianfeng Zhan, ICT, Chinese Academy of Sciences, and BenchCouncil    
Dr. Wanling Gao, ICT, Chinese Academy of Sciences    
Fei Tang, ICT, Chinese Academy of Sciences    
Dr. Lei Wang, ICT, Chinese Academy of Sciences    
Xu Wen, ICT, Chinese Academy of Sciences    
Chuanxin Lan, ICT, Chinese Academy of Sciences    
Chunjie Luo, ICT, Chinese Academy of Sciences
Yunyou Huang, ICT, Chinese Academy of Sciences
Dr. Chen Zheng, ICT, Chinese Academy of Sciences, and BenchCouncil    
Dr. Zheng Cao, Alibaba     
Hainan Ye, Beijing Academy of Frontier Sciences and BenchCouncil     
Jiahui Dai, Beijing Academy of Frontier Sciences and BenchCouncil     
Daoyi Zheng, Baidu     
Haoning Tang, Tencent     
Kunlin Zhan, 58.com     
Biao Wang, NetEase     
Defei Kong, ByteDance     
Tong Wu, China National Institute of Metrology     
Minghe Yu, Zhihu     
Chongkang Tan, Lenovo     
Huan Li, Paypal     
Dr. Xinhui Tian, Moqi     
Yatao Li, Microsoft Research Asia     
Junchao Shao, JD.com     
Zhenyu Wang, CloudTa     
Xiaoyu Wang, Intellifusion     

License

AIBench is available for researchers interested in AI. Software components of AIBench are all available as open-source software and governed by their own licensing terms. Researchers intending to use AIBench are required to fully understand and abide by the licensing terms of the various components. AIBench is open-source under the Apache License, Version 2.0. Please use all files in compliance with the License. Our AIBench Software components are all available as open-source software and governed by their own licensing terms. If you want to use our AIBench you must understand and comply with their licenses. Software developed externally (not by AIBench group)

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