PGAU: Static IR Drop Analysis for Power Grid using Attention U-Net Architecture and Label Distribution Smoothing

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

Abstract: As feature sizes shrink, the on-chip power grid (PG) faces serious power integrity issues, and static IR drop analysis becomes critical for PG design and optimization. Many machine learning (ML) based methods have been proposed to address the inefficiencies of traditional numerical methods. However, many previous works have ignored the problems of feature confusion and imbalance IR drop distribution. In this work, we propose novel feature augmentation and selection methods to solve the feature confusion problem and use the label distribution smoothing (LDS) technique to handle unbalanced labels. Importantly, we design a static IR drop analysis model for PG using the Attention U-Net architecture (PGAU). Furthermore, two real-world datasets are used for evaluation. Experiments show that our model outperforms baselines, with a 2.6% improvement in the correlation coefficient (CC) and a 22.2% reduction in the mean absolute error (MAE). Moreover, our model is highly transferable and performs better against never-before-seen designs.