{"title":"电子设计自动化的强化学习:成功与机遇","authors":"Matthew E. Taylor","doi":"10.1145/3439706.3446882","DOIUrl":null,"url":null,"abstract":"Reinforcement learning is a machine learning technique that has been applied in many domains, including robotics, game playing, and finance. This talk will briefly introduce reinforcement learning with two use cases related to compiler optimization and chip design. Interested participants will also have materials suggested to learn a more at a technical or non-technical level about this exciting tool.","PeriodicalId":184050,"journal":{"name":"Proceedings of the 2021 International Symposium on Physical Design","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Reinforcement Learning for Electronic Design Automation: Successes and Opportunities\",\"authors\":\"Matthew E. Taylor\",\"doi\":\"10.1145/3439706.3446882\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Reinforcement learning is a machine learning technique that has been applied in many domains, including robotics, game playing, and finance. This talk will briefly introduce reinforcement learning with two use cases related to compiler optimization and chip design. Interested participants will also have materials suggested to learn a more at a technical or non-technical level about this exciting tool.\",\"PeriodicalId\":184050,\"journal\":{\"name\":\"Proceedings of the 2021 International Symposium on Physical Design\",\"volume\":\"49 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-03-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2021 International Symposium on Physical Design\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3439706.3446882\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2021 International Symposium on Physical Design","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3439706.3446882","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Reinforcement Learning for Electronic Design Automation: Successes and Opportunities
Reinforcement learning is a machine learning technique that has been applied in many domains, including robotics, game playing, and finance. This talk will briefly introduce reinforcement learning with two use cases related to compiler optimization and chip design. Interested participants will also have materials suggested to learn a more at a technical or non-technical level about this exciting tool.