WideGate: Beyond Directed Acyclic Graph Learning in Subcircuit Boundary Prediction

Jiawei Liu, Zhiyan Liu, Xun He, Jianwang Zhai, Zhengyuan Shi, Qiang Xu, Bei Yu, Chuan Shi

Abstract: Subcircuit boundary prediction is an important application of machine learning in logical analysis, effectively supporting tasks such as functional verification and logic optimization. Existing methods often convert circuits into and-inverter graphs and then use directed acyclic graph neural networks to perform this task. However, two key characteristics of subcircuit boundary prediction do not align with the fundamental assumptions of directed acyclic graph (DAG) learning, which limits the model's expressiveness and generalization capabilities. To break these assumptions, we propose WideGate, which includes a receptive field generation module that extends beyond the fanin cone and fanout cone, as well as an adaptive aggregation module that focuses on boundaries. Extensive experiments show that WideGate significantly outperforms existing methods in terms of prediction accuracy and training efficiency for sub circuit boundary prediction. The code is available at https://github.com/BUPT-GAMMA/WideGate.