A Database Dependent Framework for K-Input Maximum Fanout-Free Window Rewriting

Xuliang Zhu, Ruofei Tang, Lei Chen*, Xing Li, Xin Huang, Mingxuan Yuan, Weihua Sheng, Jianliang Xu

*Corresponding author for this work

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


Rewriting is a widely used logic optimization approach incorporated in most commercial logic synthesis tools. In this paper, we present a new rewriting method based on AndInverted Graph (AIG). Rather than focusing on cut rewriting, it considers a novel sub-structure called Maximum Fanout-Free Window (MFFW) and rewrites with a more compact implementation. Both exact synthesis and heuristic methods can be adopted to optimize MFFWs. A database dependent framework is proposed to store the optimal sub-structures to accelerate the processing. We further propose the semi-canonicalization to reduce the scale of the database, which could reduce more than 98% of the 4-input MFFW database. Extensive experiments on benchmark datasets demonstrate both the effectiveness and efficiency of our proposed framework.
Original languageEnglish
Title of host publication60th ACM/IEEE Design Automation Conference - Proceedings 2023
Number of pages6
ISBN (Electronic)9798350323481
ISBN (Print)9798350323498
Publication statusPublished - 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


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