基于tucker和张量列分解的新型张量模型压缩方法

2017 IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS) Pub Date : 2017-10-01 DOI:10.1109/EPEPS.2017.8329724

Cong Chen, Kim Batselier, N. Wong

{"title":"基于tucker和张量列分解的新型张量模型压缩方法","authors":"Cong Chen, Kim Batselier, N. Wong","doi":"10.1109/EPEPS.2017.8329724","DOIUrl":null,"url":null,"abstract":"We develop a novel tensor-based Tucker-Tensor-Train-Model-Compression (T3MC) scheme for speeding up nonlinear circuit simulation. Experiment shows that T3MC achieves high efficiency with significantly higher accuracy than state-of-the-art nonlinear model order reduction (MOR) methods.","PeriodicalId":397179,"journal":{"name":"2017 IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A novel tensor-based model compression method via tucker and tensor train decompositions\",\"authors\":\"Cong Chen, Kim Batselier, N. Wong\",\"doi\":\"10.1109/EPEPS.2017.8329724\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We develop a novel tensor-based Tucker-Tensor-Train-Model-Compression (T3MC) scheme for speeding up nonlinear circuit simulation. Experiment shows that T3MC achieves high efficiency with significantly higher accuracy than state-of-the-art nonlinear model order reduction (MOR) methods.\",\"PeriodicalId\":397179,\"journal\":{\"name\":\"2017 IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)\",\"volume\":\"55 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"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.2017.8329724\",\"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.2017.8329724","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

摘要

提出了一种新的基于张量的塔克-张量-训练-模型-压缩(T3MC)方案，以加速非线性电路的仿真。实验表明，T3MC算法效率高，精度显著高于当前的非线性模型降阶(MOR)算法。

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

A novel tensor-based model compression method via tucker and tensor train decompositions

We develop a novel tensor-based Tucker-Tensor-Train-Model-Compression (T3MC) scheme for speeding up nonlinear circuit simulation. Experiment shows that T3MC achieves high efficiency with significantly higher accuracy than state-of-the-art nonlinear model order reduction (MOR) methods.

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