Authors: Zhengyuan Shi, Tiebing Tang, Sadaf Khan, Hui-Ling Zhen, Mingxuan Yuan, Zhufei Chu and Qiang Xu
Effective formulation of problems into Conjunctive Normal Form (CNF) is critical in modern SAT (Boolean Satisfiability) solving for optimizing solver performance. Addressing the limitations of existing methods, our EDA-driven preprocessing framework introduces a novel methodology for preparing SAT problems, 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. Our framework demonstrates substantial performance improvements, evidenced by a 96.14% reduction in average solving time for a set of circuit-based logic equivalence checking problems and a 52.42% reduction for non-circuit SAT problem instances, compared to applying CNF-based solver directly.
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See abc official repo to install abc
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See DeepGate official repo to install python-deepgate
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Install mockturtle (cost-customized mapper) and kissat (baseline solver)
bash install.sh Train your own RL agent with training dataset. Please copy the .aiger format training samples to folder dataset/train. We also provide three training samples reported in our papers.
To train the RL agent, run the following scripe. The trained network will be stored in ./exp/train/qnet_last.pth
bash run/train.shWe provide same testing samples reported in Section 4.1 (I1-I5). These samples are collected from industrial logic equvalence checking (LEC) problems. Run the following scripe to test our framework:
bash run/test.sh