PowerNet: Transferable Dynamic IR Drop Estimation via Maximum Convolutional Neural Network

• Published in: Proceedings of the ASP-DAC ... Asia and South Pacific Design Automation Conference pp. 13 - 18
• Main Authors: Xie, Zhiyao, Ren, Haoxing, Khailany, Brucek, Sheng, Ye, Santosh, Santosh, Hu, Jiang, Chen, Yiran
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.01.2020
• Subjects: Convolutional neural networks; Design automation; Machine learning; Maximum likelihood estimation; Power grids; Power system dynamics
• ISSN: 2153-697X
• DOI: 10.1109/ASP-DAC47756.2020.9045574

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 30× 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.