Bingchen Gong, Brendan Jou, Felix X. Yu, Shih-Fu Chang
{"title":"Tamp: A Library for Compact Deep Neural Networks with Structured Matrices","authors":"Bingchen Gong, Brendan Jou, Felix X. Yu, Shih-Fu Chang","doi":"10.1145/2964284.2973802","DOIUrl":null,"url":null,"abstract":"We introduce Tamp, an open source C++ library for reducing the space and time costs of deep neural network models. In particular, Tamp implements several recent works which use structured matrices to replace unstructured matrices which are often bottlenecks in neural networks. Tamp is also designed to serve as a unified development platform with several supported optimization back-ends and abstracted data types. This paper introduces the design and API and also demonstrates the effectiveness with experiments on public datasets.","PeriodicalId":140670,"journal":{"name":"Proceedings of the 24th ACM international conference on Multimedia","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 24th ACM international conference on Multimedia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2964284.2973802","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
We introduce Tamp, an open source C++ library for reducing the space and time costs of deep neural network models. In particular, Tamp implements several recent works which use structured matrices to replace unstructured matrices which are often bottlenecks in neural networks. Tamp is also designed to serve as a unified development platform with several supported optimization back-ends and abstracted data types. This paper introduces the design and API and also demonstrates the effectiveness with experiments on public datasets.