Late Breaking Results: Hybrid Logic Optimization with Predictive Self-Supervision

Hybrid optimization is an emerging approach in logic synthesis, focusing on applying diverse optimization methods to different parts of a logic circuit. This paper analyzes the relationship between each vertex and its corresponding optimization method. We extract a subgraph centered on each vertex and quantify the logic optimization results of these subgraphs as vertex features. Based on these features, we propose a circuit partitioning method to cluster the logic circuit, enabling the final optimized circuit to be constructed by merging clusters optimized with their respective methods. Additionally, we introduce a self-supervised prediction model to efficiently obtain vertex features. The experimental results targeting LUT mapping demonstrate that our method achieves improvements of $8.48 \%$ in area and 9.81% in delay compared to the state-of-the-art.