{"title":"更安全&最安全:用于汽车soc在线老化监测的数据注释和预警的单老化因子增强环和阴影树","authors":"Cho-Sheng Lin, Jing Huang, Po-Sheng Chang, Chun-Yen Tsai, Tsung-Chu Huang","doi":"10.1109/ICCE-TW52618.2021.9603149","DOIUrl":null,"url":null,"abstract":"AI techniques have been widely applied in consumer electronics, especially automotive advanced driver assistant systems. The reliability of the AI processors in the harsh environment is facing a critical challenge. In this paper we explore and exploit high correlation between single-aging-factor (SAF) enhanced oscillating rings (SAFERs) for online data annotation in early stage and SAF shadow trees (SAFESTs) for training and on-line aging monitoring later. Compared with previous work, 88% of accuracy for high correlation supervised classification will be more reliable than 99% of meaningless clustering.","PeriodicalId":141850,"journal":{"name":"2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"SAFER & SAFEST: Single-Aging-Factor Enhanced Rings and Shadow Trees for Data Annotation and Early Warning in Online Aging Monitors of Automotive SoCs\",\"authors\":\"Cho-Sheng Lin, Jing Huang, Po-Sheng Chang, Chun-Yen Tsai, Tsung-Chu Huang\",\"doi\":\"10.1109/ICCE-TW52618.2021.9603149\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"AI techniques have been widely applied in consumer electronics, especially automotive advanced driver assistant systems. The reliability of the AI processors in the harsh environment is facing a critical challenge. In this paper we explore and exploit high correlation between single-aging-factor (SAF) enhanced oscillating rings (SAFERs) for online data annotation in early stage and SAF shadow trees (SAFESTs) for training and on-line aging monitoring later. Compared with previous work, 88% of accuracy for high correlation supervised classification will be more reliable than 99% of meaningless clustering.\",\"PeriodicalId\":141850,\"journal\":{\"name\":\"2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCE-TW52618.2021.9603149\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCE-TW52618.2021.9603149","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
SAFER & SAFEST: Single-Aging-Factor Enhanced Rings and Shadow Trees for Data Annotation and Early Warning in Online Aging Monitors of Automotive SoCs
AI techniques have been widely applied in consumer electronics, especially automotive advanced driver assistant systems. The reliability of the AI processors in the harsh environment is facing a critical challenge. In this paper we explore and exploit high correlation between single-aging-factor (SAF) enhanced oscillating rings (SAFERs) for online data annotation in early stage and SAF shadow trees (SAFESTs) for training and on-line aging monitoring later. Compared with previous work, 88% of accuracy for high correlation supervised classification will be more reliable than 99% of meaningless clustering.
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