Less is More: Hop-Wise Graph Attention for Scalable and Generalizable Learning on Circuits

arXiv:2403.01317v1 Announce Type: new
Abstract: While graph neural networks (GNNs) have gained popularity for learning circuit representations in various electronic design automation (EDA) tasks, they face challenges in scalability when applied to large graphs and exhibit limited generalizability to new designs. These limitations make them less practical for addressing large-scale, complex circuit problems. In this work we propose HOGA, a novel attention-based model for learning circuit representations in a scalable and generalizable manner. HOGA first computes hop-wise features per node …

abstract arxiv attention automation challenges cs.ar cs.lg design designs eda electronic electronic design automation face gnns graph graph neural networks graphs limitations networks neural networks practical scalability scalable tasks them type wise