Xplace: An Extremely Fast and Extensible Global Placement Framework

Lixin Liu, Bangqi Fu, Martin D. F. Wong, Evangeline F. Y. Young

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

6 Citations (Scopus)

Abstract

Placement serves as a fundamental step in VLSI physical design. Recently, GPU-based global placer DREAMPlace[1] demonstrated its superiority over CPU-based global placers. In this work, we develop an extremely fast GPU accelerated global placer Xplace which achieves around 2x speedup with better solution quality compared to DREAMPlace. We also plug a novel Fourier neural network into Xplace as an extension to further improve the solution quality. We believe this work not only proposes a new, fast, extensible placement framework but also illustrates a possibility to incorporate a neural network component into a GPU accelerated analytical placer.

Original languageEnglish
Title of host publication59th ACM/IEEE Design Automation Conference, DAC 2022
PublisherAssociation for Computing Machinery (ACM)
Pages1309-1314
Number of pages6
ISBN (Print)9781450391429
DOIs
Publication statusPublished - 13 Jul 2022
Event59th ACM/IEEE Design Automation Conference, DAC 2022 - San Francisco, United States
Duration: 10 Jul 202214 Jul 2022
https://www.dac.com/About/Conference-Archive/59th-DAC-2022 (Conference website)
https://www.dac.com/Portals/0/DAC%2059/59DAC%20Onsite%20Guide_v3.pdf?ver=GbBS5sBuhmEVJWVEz9CNIg%3d%3d (Conference programme)
https://dl.acm.org/doi/proceedings/10.1145/3489517 (Conference proceedings)

Publication series

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

Conference

Conference59th ACM/IEEE Design Automation Conference, DAC 2022
Country/TerritoryUnited States
CitySan Francisco
Period10/07/2214/07/22
Internet address

Scopus Subject Areas

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

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