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 proceedingpeer-review

2 Citations (Scopus)


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
Title of host publication60th ACM/IEEE Design Automation Conference - Proceedings 2023
Number of pages6
ISBN (Electronic)9798350323481
ISBN (Print)9798350323498
Publication statusPublished - 13 Jul 2023
Event60th ACM/IEEE Design Automation Conference, DAC 2023 - Moscone West, San Francisco, United States
Duration: 9 Jul 202313 Jul 2023

Publication series

NameACM/IEEE Design Automation Conference - Proceedings
ISSN (Print)0738-100X


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

Scopus Subject Areas

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

User-Defined Keywords

  • Estimation
  • Measurement
  • Pins
  • Routing
  • Runtime
  • Steiner trees
  • Timing


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