From Global Route to Detailed Route: ML for Fast and Accurate Wire Parasitics and Timing Prediction

Vidya A Chhabria, Wenjing Jiang, Andrew B. Kahng, Sachin S. Sapatnekar

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

8 Scopus citations

Abstract

Timing prediction and optimization are challenging in design stages prior to detailed routing (DR) due to the unavailability of routing information. Inaccurate timing prediction wastes design effort, hurts circuit performance, and may lead to design failure. This work focuses on timing prediction after clock tree synthesis and placement legalization, which is the earliest opportunity to time and optimize a "complete"netlist. The paper first documents that having "oracle knowledge"of the final post-DR parasitics enables post-global routing (GR) optimization to produce improved final timing outcomes. Machine learning (ML)-based models are proposed to bridge the gap between GR-based parasitic and timing estimation and post-DR results during post-GR optimization. These models show higher accuracy than GR-based timing estimation and, when used during post-GR optimization, show demonstrable improvements in post-DR circuit performance. Results on open 45nm and 130nm enablements using OpenROAD show efficient improvements in post-DR WNS and TNS metrics without increasing congestion.

Original languageEnglish (US)
Title of host publicationMLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD
PublisherAssociation for Computing Machinery, Inc
Pages7-14
Number of pages8
ISBN (Electronic)9781450394864
DOIs
StatePublished - Sep 12 2022
Event4th ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2022 - Snowbird, United States
Duration: Sep 12 2022Sep 13 2022

Publication series

NameMLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD

Conference

Conference4th ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2022
Country/TerritoryUnited States
CitySnowbird
Period9/12/229/13/22

Bibliographical note

Funding Information:
This work was supported in part by DARPA HR0011-18-2-0032 (The OpenROAD Project). The work of ABK is also supported in part by NSF CCF-2122665.

Publisher Copyright:
© 2022 ACM.

Keywords

  • machine learning
  • static timing analysis
  • timing optimization

Fingerprint

Dive into the research topics of 'From Global Route to Detailed Route: ML for Fast and Accurate Wire Parasitics and Timing Prediction'. Together they form a unique fingerprint.

Cite this