Estimating Voltage Drop: Models, Features and Data Representation Towards a Neural Surrogate

TL;DR

This paper explores how machine learning models, especially Graph Neural Networks, can rapidly and accurately estimate voltage drops in ASICs, outperforming traditional tools in speed and precision, thus aiding efficient chip design.

Contribution

It introduces ML-based models, notably GNNs, for IR drop estimation in ASICs, demonstrating their superior speed and accuracy over conventional methods.

Findings

ML models significantly reduce IR drop estimation time.

GNNs achieve minimal prediction errors.

ML approaches outperform commercial tools in accuracy.

Abstract

Accurate estimation of voltage drop (IR drop) in modern Application-Specific Integrated Circuits (ASICs) is highly time and resource demanding, due to the growing complexity and the transistor density in recent technology nodes. To mitigate this challenge, we investigate how Machine Learning (ML) techniques, including Extreme Gradient Boosting (XGBoost), Convolutional Neural Network (CNN), and Graph Neural Network (GNN) can aid in reducing the computational effort and implicitly the time required to estimate the IR drop in Integrated Circuits (ICs). Traditional methods, including commercial tools, require considerable time to produce accurate approximations, especially for complicated designs with numerous transistors. ML algorithms, on the other hand, are explored as an alternative solution to offer quick and precise IR drop estimation, but in considerably less time. Our approach leverages ASICs' electrical, timing, and physical to train ML models, ensuring adaptability across diverse designs with minimal adjustments. Experimental results underscore the superiority of ML models over commercial tools, greatly enhancing prediction speed. Particularly, GNNs exhibit promising performance with minimal prediction errors in voltage drop estimation. The incorporation of GNNs marks a groundbreaking advancement in accurate IR drop prediction. This study illustrates the effectiveness of ML algorithms in precisely estimating IR drop and optimizing ASIC sign-off. Utilizing ML models leads to expedited predictions, reducing calculation time and improving energy efficiency, thereby reducing environmental impact through optimized power circuits.