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
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
A machine program for theorem-proving
Communications of the ACM
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
An Algorithm to Evaluate Quantified Boolean Formulae and Its Experimental Evaluation
Journal of Automated Reasoning
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
BooM: a decision procedure for boolean matching with abstraction and dynamic learning
Proceedings of the 47th Design Automation Conference
Proceedings of the Conference on Design, Automation and Test in Europe
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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Boolean matching for multiple-output functions determines whether two given (in)completely-specified function vectors can be identical to each other under permutation and/or negation of their inputs and outputs. Despite its importance in design rectification, technology mapping, and other logic synthesis applications, there is no much direct study on this subject due to its generality and consequent computational complexity. This paper extends our prior Boolean matching decision procedure BooM to consider multiple-output functions. Through conflict-driven learning and partial assignment reduction, Boolean matching in the most general setting can still be accomplishable even when all other techniques lose their foundation and become unapplicable. Experiments demonstrate the indispensable power of strengthened learning for practical applications.