Fast IR Drop Estimation with Machine Learning

TL;DR

This paper reviews recent machine learning approaches for fast and accurate IR drop estimation in chip design, highlighting their advantages, challenges, and integration with traditional methods to improve EDA efficiency.

Contribution

It provides a comprehensive review of the latest ML-based IR drop estimation techniques and discusses integration strategies with conventional methods.

Findings

ML techniques enable faster IR drop prediction.

Integration of ML with traditional methods improves accuracy and efficiency.

Challenges include data quality and model generalization.

Abstract

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.