IRGNN: A Graph-based Framework Integrating Numerical Solution and Point Cloud for Static IR Drop Prediction

Feng Guo, Yueyue Xi, Jianwang Zhai, Jingyu Jia, Jiawei Liu, Kang Zhao, Chuan Shi

Abstract: With the continued scaling of integrated circuits (ICs), IR drop analysis for on-chip power grids (PGs) is crucial but increasingly computationally demanding. Traditional numerical methods deliver high accuracy but are prohibitively time-intensive, while various machine learning (ML) methods have been introduced to alleviate these computational burdens. However, most CNN-based methods ignore the fine structure and topological information of PGs, and face interpretability or scalability issues. In this work, we propose a novel graphbased framework, IRGNN, leveraging the PG topology with the integration of numerical solutions and point clouds. Our framework applies a numerical solver, AMG-PCG, to generate rough numerical solutions as a reliable interpretability foundation for ML. Then, to capture PG topology, we regard nodes of PG as point clouds and extract point cloud features, and we introduce a novel graph structure, IRGraph. Furthermore, a novel graph-based model IRGNN is designed, incorporating a designed neighbor distance attention (NDA) layer for distanceaware PG features aggregation and graph transformer (GT) layer to capture global information. It should be noted that our framework can analyze the IR drop of each node in PG, which CNN-based methods cannot do. Experimental evaluations demonstrate that our framework achieves significantly higher accuracy than previous CNN-based approaches and numerical solvers while substantially reducing computation time.