Fast Dynamic IR-Drop Prediction with Dual-Path Spatial-Temporal Attention

Bangqi Fu, Lixin Liu, Qijing Wang, Yutao Wang, Martin D. F. Wong, Evangeline F. Y. Young

Abstract: The analysis of IR-drop stands as a fundamental step in optimizing the power distribution network (PDN), and subsequently influences the design performance. However, traditional IR-drop analysis using commercial tools proves to be exceedingly time-consuming. Fast and accurate IR-drop analysis is desperately in demand to achieve high performance on timing and power. Recently, machine learning approaches have garnered attention owing to their remarkable speed and extensibility in IC designs. However, prior works for dynamic IR-drop prediction presented limited performance since they did not exploit the time-varying activities. In this paper, we proposed a dual-path model with spatial-temporal transformers to extract the static spatial features and dynamic time-variant activities for dynamic IR drop prediction. Experimental results on the large-scale advanced dataset CircuitNet show that our model significantly outperforms the state-of-the-art works.