A Hierarchical Circuit Symbolic Discovery Framework for Efficient Logic Optimization

Yinqi Bai, Jie Wang, Tong Xialiang, Longdi Pan, Jilai Pan, Mingxuan Yuan, Jianye HAO

Keywords: Electronic Design Automation; Logic Synthesis; Large Language Models;

TL;DR: Chip Design; Logic Optimization; Symbolic Regression; Graph Neural Networks

Abstract: The efficiency of Logic Optimization (LO) has become one of the key bottlenecks in chip design. To prompt efficient LO, many graph-based machine learning (ML) methods, such as graph neural networks (GNNs), have been proposed to predict and prune a large number of ineffective subgraphs of the LO heuristics. However, the high inference cost and limited interpretability of these approaches severely limit their wide application to modern LO tools. To address this challenge, we propose a novel **H**ierarchical C**i**rcuit **S**ymbolic Discovery Framework, namely HIS, to learn a *lightweight* and *interpretable* symbolic function that can *accurately* identify ineffective subgraphs for efficient LO. Specifically, HIS proposes a hierarchical tree structure to represent the circuit symbolic function, where every layer of the symbolic tree performs an efficient and interpretable message passing to capture the structural information of the circuit graph. To learn the hierarchical tree, we propose a circuit symbolic generation framework that leverages reinforcement learning to optimize a structure-aware Transformer model for symbolic token generation. To the best of our knowledge, HIS is *the first* approach to discover an efficient, interpretable, and high-performance symbolic function from the circuit graph for efficient LO. Experiments on two widely used circuit benchmarks show that the learned graph symbolic functions outperform previous state-of-the-art approaches in terms of efficiency and optimization performance. Moreover, we integrate HIS with the Mfs2 heuristic, one of the most time-consuming LO heuristics. Results show that HIS significantly enhances both its efficiency and optimization performance on a CPU-based machine, achieving an average runtime improvement of 27.22% and a 6.95% reduction in circuit size.

Primary Area: neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)

Submission Number: 23006