Conflict driven learning in a quantified Boolean Satisfiability solver

Within the verification community, there has been a recent increase in interest in Quantified Boolean Formula evaluation (QBF) as many interesting sequential circuit verification problems can be formulated as QBF instances. A closely related research area to QBF is Boolean Satisfiability (SAT). Rece...

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Vydáno v:Digest of technical papers - IEEE/ACM International Conference on Computer-Aided Design s. 442 - 449
Hlavní autoři: Zhang, Lintao, Malik, Sharad
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: New York, NY, USA ACM 10.11.2002
IEEE
Edice:ACM Conferences
Témata:
Applied sciences
Computing methodologies > Symbolic and algebraic manipulation
Computing methodologies > Symbolic and algebraic manipulation > Symbolic and algebraic algorithms
Design. Technologies. Operation analysis. Testing
Electronics
Exact sciences and technology
Hardware > Hardware validation > Functional verification
Hardware > Integrated circuits > Logic circuits > Sequential circuits
Integrated circuits
Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices
Testing, measurement, noise and reliability
Theory of computation > Design and analysis of algorithms
Learning
Sequential circuit
Circuit design
Test
Constraint satisfaction
Computer aided design
Satisfiability
ISBN:0780376072, 9780780376076
ISSN:1092-3152
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Shrnutí:Within the verification community, there has been a recent increase in interest in Quantified Boolean Formula evaluation (QBF) as many interesting sequential circuit verification problems can be formulated as QBF instances. A closely related research area to QBF is Boolean Satisfiability (SAT). Recent advances in SAT research have resulted in some very efficient SAT solvers. One of the critical techniques employed in these solvers is Conflict Driven Learning. In this paper, we adapt conflict driven learning for application in a QBF setting. We show that conflict driven learning can be regarded as a resolution process on the clauses. We prove that under certain conditions, tautology clauses obtained from resolution in QBF also obey the rules for implication and conflicts of regular (non-tautology) clauses; and therefore they can be treated as regular clauses and used in future search. We have implemented this idea in a new QBF solver called Quaffle and our initial experiments show that conflict driven learning can greatly speed up the solution process for most of the benchmarks we tested.
Bibliografie:SourceType-Conference Papers & Proceedings-1
ObjectType-Conference Paper-1
content type line 25
ISBN:0780376072
9780780376076
ISSN:1092-3152
DOI:10.1145/774572.774637