Fast IR Drop Estimation with Machine Learning
This review addresses the problem of slow IR drop evaluation for chip designers, which can significantly increase design turnaround time.
This paper reviews recent progress in machine learning (ML) techniques for fast IR drop estimation, a critical and time-consuming step in chip design. It discusses how ML models can be integrated with conventional techniques to improve the efficiency of Electronic Design Automation (EDA) tools.
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.