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.

中文翻译：

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PowerNet：通过最大卷积神经网络的可传输动态IR降估计

IR下降是几乎所有芯片设计都需要的基本约束。但是，对其评估通常要花费很长时间，这阻碍了解决违规问题的缓解技术。在这项工作中，我们基于卷积神经网络（CNN）开发了一种名为PowerNet的快速动态IR下降估计技术。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，可以转移到不同的设计中。这与大多数现有的机器学习（ML）方法相反，后者的模型仅适用于特定设计。实验结果表明，在具有挑战性的无矢量IR下降情况下，PowerNet的精度优于最新的ML方法，与精确的IR下降商用工具相比，其速度提高了30倍。进一步，