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

• URL: http://arxiv.org/abs/2011.13494v1
• Date: Thu, 26 Nov 2020 23:14:17 GMT
• Title: PowerNet: Transferable Dynamic IR Drop Estimation via Maximum Convolutional Neural Network
• Authors: Zhiyao Xie, Haoxing Ren, Brucek Khailany, Ye Sheng, Santosh Santosh, Jiang Hu, Yiran Chen
• Abstract summary: We develop a fast dynamic IR drop estimation technique, named PowerNet, based on a convolutional neural network (CNN) We show that PowerNet outperforms the latest machine learning (ML) method by 9% in accuracy for the challenging case of vectorless IR drop. A mitigation tool guided by PowerNet reduces IR drop hotspots by 26% and 31% on two industrial designs, respectively.
• Score: 28.555489230660488
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 times 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.

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