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Chaff: engineering an efficient SAT solver
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Source Annual ACM IEEE Design Automation Conference archive
Proceedings of the 38th annual Design Automation Conference table of contents
Las Vegas, Nevada, United States
Pages: 530 - 535  
Year of Publication: 2001
ISBN:1-58113-297-2
Authors
Matthew W. Moskewicz  Department of EECS, UC Berkeley
Conor F. Madigan  Department of EECS, MIT
Ying Zhao  Department of Electrical Engineering, Princeton University
Lintao Zhang  Department of Electrical Engineering, Princeton University
Sharad Malik  Department of Electrical Engineering, Princeton University
Sponsors
EDAC : Electronic Design Automation Consortium
IEEE-CAS : Circuits & Systems
SIGDA: ACM Special Interest Group on Design Automation
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 42,   Downloads (12 Months): 125,   Citation Count: 401
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ABSTRACT

Boolean Satisfiability is probably the most studied of combinatorial optimization/search problems. Significant effort has been devoted to trying to provide practical solutions to this problem for problem instances encountered in a range of applications in Electronic Design Automation (EDA), as well as in Artificial Intelligence (AI). This study has culminated in the development of several SAT packages, both proprietary and in the public domain (e.g. GRASP, SATO) which find significant use in both research and industry. Most existing complete solvers are variants of the Davis-Putnam (DP) search algorithm. In this paper we describe the development of a new complete solver, Chaff, which achieves significant performance gains through careful engineering of all aspects of the search - especially a particularly efficient implementation of Boolean constraint propagation (BCP) and a novel low overhead decision strategy. Chaff has been able to obtain one to two orders of magnitude performance improvement on difficult SAT benchmarks in comparison with other solvers (DP or otherwise), including GRASP and SATO.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Bayardo, R. and Schrag, R.: Using CSP look-back techniques to solve real-world SAT instances, in Proc. of the 14th Nat. (US) Conf. on Artificial Intelligence (AAAI-97), AAAI Press/The MIT Press, 1997, pp. 203-208.
 
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DIMACS benchmarks available at ftp://dimacs.rutgers.edu/pub/challenge/sat/benchmarks
 
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McAllester, D., Selman, B. and Kautz, H.: Evidence for invariants in local search, in Proceedings of AAAI'97, MIT Press, 1997, pp. 321-326.
 
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Stephan, P., Brayton, R., and Sangiovanni-Vencentelli, A., "Combinational Test Generation Using Satisfiability," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 15, 1167-1176, 1996.
 
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Velev, M., FVP-UNSAT.1.0, FVP-UNSAT.2.0, VLIW- SAT. 1.0, SSS-SAT.1.0, Superscalar Suite 1.0, Superscalar Suite 1.0a, Available from: http://www.ece.cmu.edu/mvelev
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CITED BY  401

Collaborative Colleagues:
Matthew W. Moskewicz: colleagues
Conor F. Madigan: colleagues
Ying Zhao: colleagues
Lintao Zhang: colleagues
Sharad Malik: colleagues