Learn to Floorplan through Acquisition of Effective Local Search Heuristics

Zhuolun He, Yuzhe Ma, Lu Zhang, Peiyu Liao, Ngai Wong, Bei Yu, Martin D.F. Wong

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

Abstract

Automatic heuristic design through reinforcement learning opens a promising direction for solving computationally difficult problems. Unlike most previous works that aimed at solution construction, we explore the possibility of acquiring local search heuristics through massive search experiments. To illustrate the applicability, an agent is trained to perform a walk in the search space by selecting a candidate neighbor solution at each step. Specifically, we target the floorplanning problem, where a neighbor solution is generated through perturbing the sequence pair encoding of a floorplan. Experimental results demonstrate the efficacy of the acquired heuristics as well as the potential of automatic heuristic design.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE 38th International Conference on Computer Design, ICCD 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages324-331
Number of pages8
ISBN (Electronic)9781728197104
DOIs
StatePublished - Oct 2020
Externally publishedYes
Event38th IEEE International Conference on Computer Design, ICCD 2020 - Hartford, United States
Duration: Oct 18 2020Oct 21 2020

Publication series

NameProceedings - IEEE International Conference on Computer Design: VLSI in Computers and Processors
Volume2020-October
ISSN (Print)1063-6404

Conference

Conference38th IEEE International Conference on Computer Design, ICCD 2020
CountryUnited States
CityHartford
Period10/18/2010/21/20

Keywords

  • Floorplanning
  • reinforcement learning
  • sequence pair

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering

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