{"title":"基于xgboost的输电网红外下降预测器","authors":"C. Pao, An-Yu Su, Yu-Min Lee","doi":"10.23919/DATE48585.2020.9116327","DOIUrl":null,"url":null,"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.","PeriodicalId":289525,"journal":{"name":"2020 Design, Automation & Test in Europe Conference & Exhibition (DATE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"XGBIR: An XGBoost-based IR Drop Predictor for Power Delivery Network\",\"authors\":\"C. Pao, An-Yu Su, Yu-Min Lee\",\"doi\":\"10.23919/DATE48585.2020.9116327\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":289525,\"journal\":{\"name\":\"2020 Design, Automation & Test in Europe Conference & Exhibition (DATE)\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 Design, Automation & Test in Europe Conference & Exhibition (DATE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/DATE48585.2020.9116327\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 Design, Automation & Test in Europe Conference & Exhibition (DATE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/DATE48585.2020.9116327","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
XGBIR: An XGBoost-based IR Drop Predictor for Power Delivery Network
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.
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