{"title":"EDAML 2022特邀演讲嘉宾1:机器学习在高级合成中的应用","authors":"Ankush Sood","doi":"10.1109/IPDPSW55747.2022.00194","DOIUrl":null,"url":null,"abstract":"Traditionally high-level optimizations like CSA and sharing have been done technology independent. Doing word level optimizations PPA aware require accurate models for power, timing and area and that needs mapping to technology library and iterations which are runtime intensive and not suitable for multimillion instance designs. In this talk, we discuss how machine learning could help make the right power/area/delay tradeoffs early in the synthesis flow not sacrificing on turnaround time.","PeriodicalId":286968,"journal":{"name":"2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"EDAML 2022 Invited Speaker 1: Application of Machine Learning in High Level Synthesis\",\"authors\":\"Ankush Sood\",\"doi\":\"10.1109/IPDPSW55747.2022.00194\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Traditionally high-level optimizations like CSA and sharing have been done technology independent. Doing word level optimizations PPA aware require accurate models for power, timing and area and that needs mapping to technology library and iterations which are runtime intensive and not suitable for multimillion instance designs. In this talk, we discuss how machine learning could help make the right power/area/delay tradeoffs early in the synthesis flow not sacrificing on turnaround time.\",\"PeriodicalId\":286968,\"journal\":{\"name\":\"2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IPDPSW55747.2022.00194\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPDPSW55747.2022.00194","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
EDAML 2022 Invited Speaker 1: Application of Machine Learning in High Level Synthesis
Traditionally high-level optimizations like CSA and sharing have been done technology independent. Doing word level optimizations PPA aware require accurate models for power, timing and area and that needs mapping to technology library and iterations which are runtime intensive and not suitable for multimillion instance designs. In this talk, we discuss how machine learning could help make the right power/area/delay tradeoffs early in the synthesis flow not sacrificing on turnaround time.