EDA-Driven Preprocessing for SAT Solving

Effective formulation of problems into Conjunctive Normal Form (CNF) is critical in modern Boolean Satisfiability (SAT) solving for optimizing solver performance. Addressing the limitations of existing methods, our Electronic Design Automation (EDA)-driven preprocessing framework introduces a novel methodology for preparing SAT instances, leveraging both circuit and CNF formats for enhanced flexibility and efficiency. Central to our approach is the integration of a new logic synthesis technique, guided by a reinforcement learning agent, and a novel cost-customized LUT mapping strategy, enabling efficient handling of diverse SAT challenges. By transforming the SAT competition benchmarks into circuit instances, our framework demonstrates substantial performance improvements, as evidenced by a 52.42 average compared to solving directly. Moreover, our framework achieves a remarkable 96.14 checking problems that exhibit inherent circuit structures. These results highlight the effectiveness and versatility of our approach in handling both CNF and circuit instances. The code is available at https://github.com/cure-lab/EDA4SAT.