使用机器学习的三维集成系统的黑盒优化

2017 IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS) Pub Date : 2017-10-01 DOI:10.1109/EPEPS.2017.8329698

H. Torun, M. Swaminathan

{"title":"使用机器学习的三维集成系统的黑盒优化","authors":"H. Torun, M. Swaminathan","doi":"10.1109/EPEPS.2017.8329698","DOIUrl":null,"url":null,"abstract":"Increasing complexity of electronics originates new challenges to system optimization. This work proposes a new black box optimization algorithm based on machine learning to address these challenges and analyzes its performance for clock skew minimization of 3D integrated systems.","PeriodicalId":397179,"journal":{"name":"2017 IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Black-box optimization of 3D integrated systems using machine learning\",\"authors\":\"H. Torun, M. Swaminathan\",\"doi\":\"10.1109/EPEPS.2017.8329698\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Increasing complexity of electronics originates new challenges to system optimization. This work proposes a new black box optimization algorithm based on machine learning to address these challenges and analyzes its performance for clock skew minimization of 3D integrated systems.\",\"PeriodicalId\":397179,\"journal\":{\"name\":\"2017 IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EPEPS.2017.8329698\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EPEPS.2017.8329698","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 6

摘要

电子产品日益复杂，对系统优化提出了新的挑战。本文提出了一种新的基于机器学习的黑盒优化算法来解决这些挑战，并分析了其在3D集成系统时钟偏差最小化方面的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Black-box optimization of 3D integrated systems using machine learning

Increasing complexity of electronics originates new challenges to system optimization. This work proposes a new black box optimization algorithm based on machine learning to address these challenges and analyzes its performance for clock skew minimization of 3D integrated systems.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2017 IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)

自引率

0.00%

发文量