@inproceedings{40b1eb2011b64ea096cb07700c923c12,
title = "XGBIR: An XGBoost-based IR Drop Predictor for Power Delivery Network",
abstract = "This work utilizes the XGBoost to build a machine-learning-based IR drop predictor, XGBIR, for the power grid. To capture the behavior of power grid, we extract its several features and employ its locality property to save the extraction time. XGBIR can be effectively applied to large designs and the average error of predicted IR drops is less than 6 mV.",
author = "Pao, {Chi Hsien} and Su, {An Yu} and Lee, {Yu Min}",
note = "Publisher Copyright: {\textcopyright} 2020 EDAA.; 2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020 ; Conference date: 09-03-2020 Through 13-03-2020",
year = "2020",
month = mar,
doi = "10.23919/DATE48585.2020.9116327",
language = "English",
series = "Proceedings of the 2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1307--1310",
editor = "{Di Natale}, Giorgio and Cristiana Bolchini and Elena-Ioana Vatajelu",
booktitle = "Proceedings of the 2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020",
address = "美國",
}