Integrating CNF and BDD based SAT solvers

  • Authors:
  • S. Gopalakrishnan;V. Durairaj;P. Kalla

  • Affiliations:
  • Dept. of Electr. & Comput. Eng., Utah Univ., Salt Lake City, UT, USA;Dept. of Electr. & Comput. Eng., Utah Univ., Salt Lake City, UT, USA;Dept. of Electr. & Comput. Eng., Utah Univ., Salt Lake City, UT, USA

  • Venue:
  • HLDVT '03 Proceedings of the Eighth IEEE International Workshop on High-Level Design Validation and Test Workshop
  • Year:
  • 2003

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Abstract

This paper presents an integrated infrastructure of CNF and BDD based tools to solve the Boolean Satisfiability problem. We use both CNF and BDDs not only as a means of representation, but also to efficiently analyze, prune and guide the search. We describe a method to successfully re-orient the decision making strategies of contemporary CNF tools in a manner that enables an efficient integration with BDDs. Keeping in mind that BDDs suffer from memory explosion problems, we describe learning-based search space pruning techniques that augment the already employed conflict analysis procedures of CNF tools. Our infrastructure is targeted towards solving those hard-to-solve instances where contemporary CNF tools invest significant search times. Experiments conducted over a wide range of benchmarks demonstrate the promise of our approach.