Concurrent Sign-off Timing Optimization via Deep Steiner Points Refinement

Siting Liu, Ziyi Wang, Fangzhou Liu, Yibo Lin, Bei Yu, Martin Wong

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


Timing closure is crucial across the circuit design flow. Since obtaining sign-off performance needs a time-consuming routing flow, all the previous early-stage timing optimization works only focus on improving early timing metrics, e.g., rough timing estimation using linear RC model or pre-routing path-length. However, there is no consistency guarantee between early-stage metrics and sign-off timing performance. To enable explicit early-stage optimization on the sign-off timing metrics, we propose a novel timing optimization framework, TSteiner. This paper demonstrates the ability of the learning framework to perform robust and efficient timing optimization in the early stage with comprehensive and convincing experimental results on real-world designs.

Original languageEnglish (US)
Title of host publication2023 60th ACM/IEEE Design Automation Conference, DAC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350323481
StatePublished - 2023
Externally publishedYes
Event60th ACM/IEEE Design Automation Conference, DAC 2023 - San Francisco, United States
Duration: Jul 9 2023Jul 13 2023

Publication series

NameProceedings - Design Automation Conference
ISSN (Print)0738-100X


Conference60th ACM/IEEE Design Automation Conference, DAC 2023
Country/TerritoryUnited States
CitySan Francisco

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation


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