  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.
