GPU-accelerated Critical Path Generation with Path Constraints

Guannan Guo, Tsung-Wei Huang, Yibo Lin, Martin Wong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Path-based Analysis (PBA) is a pivotal step in Static Timing Analysis (STA) for reducing slack pessimism and improving quality of results. Optimization flows often invoke PBA repeatedly with different critical path constraints to verify correct timing behavior under certain logic cone. However, PBA is extremely time consuming and state-of-the-art PBA algorithms are hardly scaled beyond a few CPU threads under constrained search space. In order to achieve new performance milestone, in this work, we propose a new GPU-accelerated PBA algorithm which can handle extensive path constraints and quickly report arbitrary number of critical paths in constrained search space. Experimental results show that our algorithm can generated identical path report and achieve up to 102× speed up on a million-gate design compared to the state-of-the-art algorithm.

Original languageEnglish (US)
Title of host publication2021 40th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665445078
DOIs
StatePublished - 2021
Externally publishedYes
Event40th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2021 - Munich, Germany
Duration: Nov 1 2021Nov 4 2021

Publication series

NameIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
Volume2021-November
ISSN (Print)1092-3152

Conference

Conference40th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2021
Country/TerritoryGermany
CityMunich
Period11/1/2111/4/21

ASJC Scopus subject areas

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

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