PowerNet：基于最大卷积神经网络的可传递动态IR液滴估计
PowerNet: Transferable Dynamic IR Drop Estimation via Maximum Convolutional Neural Network
摘要：
IR压降是几乎所有芯片设计所需的基本约束。然而，其评估通常需要很长时间，这阻碍了修复其违规行为的缓解技术。在这项工作中，我们开发了一种快速的动态IR下降估计技术，命名为PowerNet，基于卷积神经网络（CNN）。它可以处理基于矢量和无矢量的IR分析。此外，所提出的CNN模型是通用的，并可转移到不同的设计。这与大多数现有的机器学习（ML）方法相反，其中模型仅适用于特定的设计。实验结果表明，PowerNet比最新的ML方法的准确率高出9%，对于具有挑战性的无矢量IR下降的情况，并实现了30倍的加速比准确的IR下降的商业工具。此外，由PowerNet指导的缓解工具在两个工业设计上分别将IR压降热点减少了26%和31%，而对其电网的修改非常有限。
英文摘要：
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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