1. [Publications](/publications) 2. PowerNet: Transferable Dynamic IR Drop Estimation via Maximum Convolutional Neural Network # PowerNet: Transferable Dynamic IR Drop Estimation via Maximum Convolutional Neural Network ![](/sites/default/files/styles/wide/public/publications/PowerNet_capture_0.PNG?itok=iRRY-L9G) IR drop is a fundamental constraint required by almost all chip designs. However, its evaluation usually takes a long time that hinders mitigation techniques for fixing its violations. In this work, we develop a fast dynamic IR drop estimation technique, named PowerNet, based on a convolutional neural network (CNN). It can handle both vector-based and vectorless IR analyses. Moreover, the proposed CNN model is general and transferable to different designs. This is in contrast to most existing machine learning (ML) approaches, where a model is applicable only to a specific design. Experimental results show that PowerNet outperforms the latest ML method by 9% in accuracy for the challenging case of vectorless IR drop and achieves a 30x speedup compared to an accurate IR drop commercial tool. Further, a mitigation tool guided by PowerNet reduces IR drop hotspots by 26% and 31% on two industrial designs, respectively, with very limited modification on their power grids. ## Authors Zhiyao Xie (Duke) Mark Haoxing Ren (NVIDIA) [Brucek Khailany](/person/brucek-khailany) Ye Sheng (NVIDIA) Santosh Santosh (NVIDIA) Jiang Hu (Texas A&M) Yiran Chen (Duke) ## Publication Date Monday, January 13, 2020 ## Published in [ASP-DAC 2020](https://aspdac2020.github.io/aspdac20/welcome/index.html) ## Research Area [Circuits and VLSI Design](/research-area/circuits) [Artificial Intelligence and Machine Learning ](/research-area/machine-learning-artificial-intelligence) ## Copyright This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to .