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

Author:

Bai Chen1ORCID,Sun Qi2ORCID,Zhai Jianwang3ORCID,Ma Yuzhe4ORCID,Yu Bei1ORCID,Wong Martin D. F.5ORCID

Affiliation:

1. The Chinese University of Hong Kong, Hong Kong SAR

2. Zhejiang University, China

3. Beijing University of Posts and Telecommunications, China

4. Hong Kong University of Science and Technology (Guangzhou), China

5. Hong Kong Baptist University, Hong Kong SAR

Abstract

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.

Funder

National Key R&D Program of China

The Research Grants Council of Hong Kong SAR

The Zhejiang University Education Foundation Qizhen Scholar Foundation

Publisher

Association for Computing Machinery (ACM)

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Computer Science Applications

Reference54 articles.

1. RISC-V. 2023. Wikipedia Wikimedia Foundation. https://en.wikipedia.org/wiki/RISC-V

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3. An Agile Approach to Building RISC-V Microprocessors

4. James E. Smith. 1998. A study of branch prediction strategies. In IEEE/ACM International Symposium on Computer Architecture (ISCA’98). 202–215.

5. George Z. Chrysos and Joel S. Emer. 1998. Memory dependence prediction using store sets. In IEEE/ACM International Symposium on Computer Architecture (ISCA’98). 142–153.

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