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Boolean functional synthesis is a fundamental problem in computer sciencewith wide-ranging applications and has witnessed a surge of interest resultingin progressively improved techniques over the past decade. Despite intensealgorithmic development, a large number of problems remain beyond the reach ofthe state of the art techniques. Motivated by the progress in machine learning,we propose Manthan, a novel data-driven approach to Boolean functionalsynthesis. Manthan views functional synthesis as a classification problem,relying on advances in constrained sampling for data generation, and advancesin automated reasoning for a novel proof-guided refinement and provableverification. On an extensive and rigorous evaluation over 609 benchmarks, wedemonstrate that Manthan significantly improves upon the current state of theart, solving 356 benchmarks in comparison to 280, which is the most solved by astate of the art technique; thereby, we demonstrate an increase of 76benchmarks over the current state of the art. Furthermore, Manthan solves 60benchmarks that none of the current state of the art techniques could solve.The significant performance improvements, along with our detailed analysis,highlights several interesting avenues of future work at the intersection ofmachine learning, constrained sampling, and automated reasoning.

Manthan: A Data Driven Approach for Boolean Function Synthesis