Machine Learning-Based IR Drop Prediction Approach

A new technical paper titled “Estimating Voltage Drop: Models, Features and Data Representation Towards a Neural Surrogate” was published by researchers at KTH Royal Institute of Technology and Ericsson Research.

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.”

Find the technical paper here. February 2025.

Jin, Yifei, Dimitrios Koutlis, Hector Bandala, and Marios Daoutis. “Estimating Voltage Drop: Models, Features and Data Representation Towards a Neural Surrogate.” arXiv preprint arXiv:2502.05345 (2025).