Learning from BDDs in SAT-based bounded model checking

  • Authors:
  • Aarti Gupta;Malay Ganai;Chao Wang;Zijiang Yang;Pranav Ashar

  • Affiliations:
  • NEC Labs America, Princeton, NJ;NEC Labs America, Princeton, NJ;University of Colorado, Boulder, CO;NEC Labs America, Princeton, NJ;NEC Labs America, Princeton, NJ

  • Venue:
  • Proceedings of the 40th annual Design Automation Conference
  • Year:
  • 2003

Quantified Score

Hi-index 0.00

Visualization

Abstract

Bounded Model Checking (BMC) based on Boolean Satisfiability (SAT) procedures has recently gained popularity as an alternative to BDD-based model checking techniques for finding bugs in large designs. In this paper, we explore the use of learning from BDDs, where learned clauses generated by BDD-based analysis are added to the SAT solver, to supplement its other learning mechanisms. We propose several heuristics for guiding this process, aimed at increasing the usefulness of the learned clauses, while reducing the overheads. We demonstrate the effectiveness of our approach on several industrial designs, where BMC performance is improved and the design can be searched up to a greater depth by use of BDD-based learning.