{"title":"利用现场学习功能改造边缘硬件","authors":"Peng Yao, Bin Gao, Huaqiang Wu","doi":"10.1038/s44287-024-00031-y","DOIUrl":null,"url":null,"abstract":"Memristor devices have shown notable superiority in the realm of neuromorphic computing chips, particularly in artificial intelligence (AI) inference tasks. Researchers are now grappling with the intricacies of incorporating in situ learning capabilities into memristor-based chips, paving the way for more powerful edge intelligence.","PeriodicalId":501701,"journal":{"name":"Nature Reviews Electrical Engineering","volume":"1 3","pages":"141-142"},"PeriodicalIF":0.0000,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Transforming edge hardware with in situ learning features\",\"authors\":\"Peng Yao, Bin Gao, Huaqiang Wu\",\"doi\":\"10.1038/s44287-024-00031-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Memristor devices have shown notable superiority in the realm of neuromorphic computing chips, particularly in artificial intelligence (AI) inference tasks. Researchers are now grappling with the intricacies of incorporating in situ learning capabilities into memristor-based chips, paving the way for more powerful edge intelligence.\",\"PeriodicalId\":501701,\"journal\":{\"name\":\"Nature Reviews Electrical Engineering\",\"volume\":\"1 3\",\"pages\":\"141-142\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Reviews Electrical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.nature.com/articles/s44287-024-00031-y\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Reviews Electrical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://www.nature.com/articles/s44287-024-00031-y","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Transforming edge hardware with in situ learning features
Memristor devices have shown notable superiority in the realm of neuromorphic computing chips, particularly in artificial intelligence (AI) inference tasks. Researchers are now grappling with the intricacies of incorporating in situ learning capabilities into memristor-based chips, paving the way for more powerful edge intelligence.
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