Limits of using signatures for permutation independent Boolean comparison
ASP-DAC '95 Proceedings of the 1995 Asia and South Pacific Design Automation Conference
A survey of Boolean matching techniques for library binding
ACM Transactions on Design Automation of Electronic Systems (TODAES)
GRASP: A Search Algorithm for Propositional Satisfiability
IEEE Transactions on Computers
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
The Boolean isomorphism problem
FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
Fast Boolean Matching with Don't Cares
ISQED '06 Proceedings of the 7th International Symposium on Quality Electronic Design
Incremental learning approach and SAT model for Boolean matching with don't cares
Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design
Signature based Boolean matching in the presence of don't cares
Proceedings of the 45th annual Design Automation Conference
Simulation and SAT-based Boolean matching for large Boolean networks
Proceedings of the 46th Annual Design Automation Conference
A transform-parametric approach to Boolean matching
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
DeltaSyn: an efficient logic difference optimizer for ECO synthesis
Proceedings of the 2009 International Conference on Computer-Aided Design
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Match and replace: a functional ECO engine for multi-error circuit rectification
Proceedings of the International Conference on Computer-Aided Design
Boolean matching of function vectors with strengthened learning
Proceedings of the International Conference on Computer-Aided Design
ICCAD-2013 CAD contest in technology mapping for macro blocks and benchmark suite
Proceedings of the International Conference on Computer-Aided Design
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Boolean matching determines whether two given (in)completely-specified Boolean functions can be identical or complementary to each other under permutation and/or negation of their input variables. Due to its broad applications in logic synthesis and verification, it attracted much attention. Most prior efforts however were incomplete and/or restricted to certain special matching conditions. In contrast, this paper focuses on the computation kernel of Boolean matching and proposes a complete generic framework. Through conflict-driven learning and abstraction, the capacity of Boolean matching scales up due to the effective pruning of infeasible matching solutions. Experiments show encouraging results in resolving hard instances that are otherwise unsolvable.