XGBIR: An XGBoost-based IR Drop Predictor for Power Delivery Network

Chi Hsien Pao, An Yu Su, Yu Min Lee

研究成果: Conference contribution同行評審

11 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題Proceedings of the 2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020
編輯Giorgio Di Natale, Cristiana Bolchini, Elena-Ioana Vatajelu
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1307-1310
頁數4
ISBN(電子)9783981926347
DOIs
出版狀態Published - 3月 2020
事件2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020 - Grenoble, France
持續時間: 9 3月 202013 3月 2020

出版系列

名字Proceedings of the 2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020

Conference

Conference2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020
國家/地區France
城市Grenoble
期間9/03/2013/03/20

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