Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Verification of large synthesized designs
ICCAD '93 Proceedings of the 1993 IEEE/ACM international conference on Computer-aided design
Tight integration of combinational verification methods
Proceedings of the 1998 IEEE/ACM international conference on Computer-aided design
A machine program for theorem-proving
Communications of the ACM
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Formal Equivalence Checking and Design DeBugging
Formal Equivalence Checking and Design DeBugging
Equivalence Checking Combining a Structural SAT-Solver, BDDs, and Simulation
ICCD '00 Proceedings of the 2000 IEEE International Conference on Computer Design: VLSI in Computers & Processors
Learning from BDDs in SAT-based bounded model checking
Proceedings of the 40th annual Design Automation Conference
Resolution cannot polynomially simulate compressed-BFS
Annals of Mathematics and Artificial Intelligence
Journal of Automated Reasoning
Designing safe, reliable systems using scade
ISoLA'04 Proceedings of the First international conference on Leveraging Applications of Formal Methods
Using stålmarck’s algorithm to prove inequalities
ICFEM'05 Proceedings of the 7th international conference on Formal Methods and Software Engineering
IJCAR'12 Proceedings of the 6th international joint conference on Automated Reasoning
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There are many approaches available for solving combinational design automation problems encoded as tautology or satisfiability checks. Unfortunately there exists no single analysis that gives adequate performance for all problems of interest, and it is therefore critical to be able to combine approaches.In this paper, we present a proof engine framework where individual analyses are viewed as strategies---functions between different proof states. By defining our proof engine in such a way that we can compose strategies to form new, more powerful, strategies we achieve synergistic effects between the individual methods. The resulting framework has enabled us to develop a small set of powerful composite default strategies.We describe several strategies and their interplay; one of the strategies, variable instantiation, is new. The strength of our approach is demonstrated with experimental results showing that our default strategies can achieve up to several magnitudes of speed-up compared to BDD-based techniques and search-based satisfiability solvers such as ZChaff.