CHOP: Clustered Hybrid Optimization for Logic Synthesis With Self-Supervised Prediction

Hybrid optimization is emerging as a promising approach for enhancing logic synthesis by combining multiple logic optimization methods. This paper introduces CHOP, a novel framework that leverages vertex-level feature extraction and optimization-aware clustering to achieve significant improvements in the efficiency and quality of logic synthesis. Unlike existing cluster-level approaches that follow a “partition-then-predict” paradigm, CHOP operates under a “predict-then-partition” approach where the optimization method most appropriate for a given circuit vertex is determined by analyzing its local structural and functional context. To capitalize on this insight, CHOP extracts a subgraph centered on each vertex and calculates its circuit cost, which serves as a vertex feature for the subsequent circuit partitioning process. Furthermore, we introduce a self-supervised prediction model to obtain these vertex features efficiently. Following circuit partitioning, CHOP determines the optimization method for each partition and then iteratively applies these methods to derive the optimized circuit. The experimental results from logic optimization and LUT mapping tasks on the EPFL benchmark demonstrate the effectiveness and efficiency of CHOP.