BDD based decomposition of logic functions with application to FPGA synthesis
DAC '93 Proceedings of the 30th international Design Automation Conference
An algorithm for bi-decomposition of logic functions
Proceedings of the 38th annual Design Automation Conference
Functional Decomposition with Application to FPGA Synthesis
Functional Decomposition with Application to FPGA Synthesis
Propositional Logic: Deduction and Algorithms
Propositional Logic: Deduction and Algorithms
Bi-decomposing large Boolean functions via interpolation and satisfiability solving
Proceedings of the 45th annual Design Automation Conference
To SAT or not to SAT: Ashenhurst decomposition in a large scale
Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
To SAT or Not to SAT: Scalable Exploration of Functional Dependency
IEEE Transactions on Computers
Bi-decomposition of large Boolean functions using blocking edge graphs
Proceedings of the International Conference on Computer-Aided Design
Technology mapping for TLU FPGAs based on decomposition of binary decision diagrams
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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Boolean function bi-decomposition is pervasive in logic synthesis. Bi-decomposition entails the decomposition of a Boolean function into two other simpler functions connected by a simple two-input gate. Existing solutions are based either on Binary Decision Diagrams (BDDs) or Boolean Satisfiability (SAT). Furthermore, the partition of the input set of variables is either assumed, or an automatic derivation is required. Most recent work on bi-decomposition proposed the use of Minimally Unsatisfiable Subformulas (MUSes) or Quantified Boolean Formulas (QBF) for computing, respectively, variable partitions of either approximate or optimum quality. This paper develops new group-oriented MUS-based models for addressing both the performance and the quality of bi-decompositions. The paper shows that approximate MUS search can be guided by the quality of well-known metrics. In addition, the paper improves on recent high-performance approximate models and versatile exact models, to address the practical requirements of bi-decomposition in logic synthesis. Experimental results obtained on representative benchmarks demonstrate significant improvement in performance as well as in the quality of decompositions.