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

Chen Bai, Qi Sun, Jianwang Zhai, Yuzhe Ma, Bei Yu, Martin D. F. Wong

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

21 Citations (Scopus)

Abstract

The microarchitecture design of a processor has been increasingly difficult due to the large design space and time-consuming verification flow. Previously, researchers rely on prior knowledge and cycle-accurate simulators to analyze the performance of different microarchitecture designs but lack sufficient discussions on methodologies to strike a good balance between power and performance. This work proposes an automatic framework to explore microarchitecture designs of the RISC-V Berkeley Out-of-Order Machine (BOOM), termed as BOOM-Explorer, achieving a good trade-off on power and performance. Firstly, the framework utilizes an advanced microarchitecture-aware active learning (MicroAL) algorithm to generate a diverse and representative initial design set. Secondly, a Gaussian process model with deep kernel learning functions (DKL-GP) is built to characterize the design space. Thirdly, correlated multi-objective Bayesian optimization is leveraged to explore Pareto-optimal designs. Experimental results show that BOOM-Explorer can search for designs that dominate previous arts and designs developed by senior engineers in terms of power and performance within a much shorter time.

Original languageEnglish
Title of host publicationProceedings of The 40th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2021
PublisherIEEE
Number of pages9
ISBN (Electronic)9781665445078
ISBN (Print)9781665445085
DOIs
Publication statusPublished - 1 Nov 2021
Event40th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2021 - Munich, Germany
Duration: 1 Nov 20214 Nov 2021
https://www.informatik.uni-bremen.de/iccad2021/index.php (Conference website)
https://www.informatik.uni-bremen.de/iccad2021/agenda.php (Conference programme)
https://ieeexplore.ieee.org/xpl/conhome/9643423/proceeding (Conference proceedings )

Publication series

NameIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
Volume2021-November
ISSN (Print)1933-7760
ISSN (Electronic)1558-2434

Conference

Conference40th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2021
Country/TerritoryGermany
CityMunich
Period1/11/214/11/21
Internet address

Scopus Subject Areas

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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