mlsanityData to 14 Oct 2026

CFIRSTNET: Comprehensive Features for Static IR Drop Estimation with Neural Network

arXiv 2502.12168 ↗First posted 13 Feb 2025cs.LG cs.CVcited by 1

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Abstract (arXiv)

IR drop estimation is now considered a first-order metric due to the concern about reliability and performance in modern electronic products. Since traditional solution involves lengthy iteration and simulation flow, how to achieve fast yet accurate estimation has become an essential demand. In this work, with the help of modern AI acceleration techniques, we propose a comprehensive solution to combine both the advantages of image-based and netlist-based features in neural network framework and obtain high-quality IR drop prediction very effectively in modern designs. A customized convolutional neural network (CNN) is developed to extract PDN features and make static IR drop estimations. Trained and evaluated with the open-source dataset, experiment results show that we have obtained the best quality in the benchmark on the problem of IR drop estimation in ICCAD CAD Contest 2023, proving the effectiveness of this important design topic.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 27 references that are in the map. First posted on arXiv 13 Feb 2025.

Builds on

11 of 27 references.

References · 11

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  11. 2020MAVIREC: ML-Aided Vectored IR-DropEstimation and Classificationcited by 10

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025WACA-UNet: Weakness-Aware Channel Attention for Static IR Drop Prediction in Integrated Circuit Designcited by 0