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 proceedingpeer-review

2 Citations (Scopus)


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
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

  • Benchmark testing
  • Design automation
  • Optical flow
  • Optimization methods
  • Predictive models
  • Routing
  • Timing


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