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Learning from BDDs in SAT-based bounded model checking
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Source Annual ACM IEEE Design Automation Conference archive
Proceedings of the 40th annual Design Automation Conference table of contents
Anaheim, CA, USA
SESSION: Advances in SAT table of contents
Pages: 824 - 829  
Year of Publication: 2003
ISBN:1-58113-688-9
Authors
Aarti Gupta  NEC Labs America, Princeton, NJ
Malay Ganai  NEC Labs America, Princeton, NJ
Chao Wang  University of Colorado, Boulder, CO
Zijiang Yang  NEC Labs America, Princeton, NJ
Pranav Ashar  NEC Labs America, Princeton, NJ
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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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.


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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Collaborative Colleagues:
Aarti Gupta: colleagues
Malay Ganai: colleagues
Chao Wang: colleagues
Zijiang Yang: colleagues
Pranav Ashar: colleagues