Mitigating Distribution Shift for Congestion Optimization in Global Placement

Su Zheng, Lancheng Zou, Siting Liu, Yibo Lin, Bei Yu, Martin Wong

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

Abstract

The placement and routing (PnR) flow plays a critical role in physical design. Poor routing congestion is a possible problem causing severe routing detours, which can lead to deteriorated timing performance or even routing failure. Deep-learning-based congestion prediction model is designed to guide the global placement process in previous work. However, the distribution shift problem in this method limits its performance. In this paper, we mitigate the distribution shift problem with a look-ahead mechanism inspired by optical flow prediction and an invariant feature space learning technique. With the proposed method, we can achieve better congestion prediction performance and less-congested placement results.

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
DOIs
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
Volume2023-July
ISSN (Print)0738-100X

Conference

Conference60th ACM/IEEE Design Automation Conference, DAC 2023
Country/TerritoryUnited States
CitySan Francisco
Period7/9/237/13/23

ASJC Scopus subject areas

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

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