BOOM-Explorer: RISC-V BOOM Microarchitecture Design Space Exploration

被引:1
|
作者
Bai, Chen [1 ]
Sun, Qi [2 ]
Zhai, Jianwang [3 ]
Ma, Yuzhe [4 ]
Yu, Bei [5 ]
Wong, Martin D. F. [6 ]
机构
[1] Chinese Univ Hong Kong, Dept Comp Sci & Engn, Room 122,122 Ho Sin Hang Engn Bldg, Hong Kong, Peoples R China
[2] ZJU Hangzhou Global Sci & Technol Innovat Ctr, Bd A04,2118 Pinglan Rd, Hangzhou, Peoples R China
[3] Beijing Univ Posts & Telecommun, Sch Integrated Circuits, Room 111,Sci Res Bldg, Beijing, Peoples R China
[4] Hong Kong Univ Sci & Technol Guangzhou, W4-511,1 Duxue Rd, Guangzhou, Peoples R China
[5] Chinese Univ Hong Kong, Dept Comp Sci & Engn, Room 907,Ho Sin Hang Engn Bldg, Hong Kong, Peoples R China
[6] Hong Kong Baptist Univ, Dept Comp Sci, Kowloon Tong, Kowloon, Room 801B,Shaw Tower, Hong Kong, Peoples R China
基金
国家重点研发计划;
关键词
Microprocessor; microarchitecture; design space exploration;
D O I
10.1145/3630013
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Microarchitecture parameters tuning is critical in the microprocessor design cycle. It is a non-trivial design space exploration (DSE) problem due to the large solution space, cycle-accurate simulators' modeling inaccuracy, and high simulation runtime for performance evaluations. Previous methods require massive expert efforts to construct interpretable equations or high computing resource demands to train black-box prediction models. This article follows the black-box methods due to better solution qualities than analytical methods in general. We summarize two learned lessons and propose BOOM-Explorer accordingly. First, embedding microarchitecture domain knowledge in the DSE improves the solution quality. Second, BOOM-Explorer makes the microarchitecture DSE for register-transfer-level designs within the limited time budget feasible. We enhance BOOM-Explorer with the diversity-guidance, further improving the algorithm performance. Experimental results with RISC-V Berkeley-Out-of-Order Machine under 7-nm technology show that our proposed methodology achieves an average of 18.75% higher Pareto hypervolume, 35.47% less average distance to reference set, and 65.38% less overall running time compared to previous approaches.
引用
收藏
页数:23
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