  IR drop constraint is a fundamental requirement enforced in almost all chip
designs. However, its evaluation takes a long time, and mitigation techniques
for fixing violations may require numerous iterations. As such, fast and
accurate IR drop prediction becomes critical for reducing design turnaround
time. Recently, machine learning (ML) techniques have been actively studied for
fast IR drop estimation due to their promise and success in many fields. These
studies target at various design stages with different emphasis, and
accordingly, different ML algorithms are adopted and customized. This paper
provides a review to the latest progress in ML-based IR drop estimation
techniques. It also serves as a vehicle for discussing some general challenges
faced by ML applications in electronics design automation (EDA), and
demonstrating how to integrate ML models with conventional techniques for the
better efficiency of EDA tools.
