{"title":"CapsNet中的跳零。值得吗?","authors":"R. Sharifi, Pouya Shiri, A. Baniasadi","doi":"10.29007/cd8h","DOIUrl":null,"url":null,"abstract":"Capsule networks (CapsNet) are the next generation of neural networks. CapsNet can be used for classification of data of different types. Today’s General Purpose Graphical Processing Units (GPGPUs) are more capable than before and let us train these complex networks. However, time and energy consumption remains a challenge. In this work, we investigate if skipping trivial operations i.e. multiplication by zero in CapsNet, can possibly save energy. We base our analysis on the number of multiplications by zero detected while training CapsNet on MNIST and FashionMNIST datasets.","PeriodicalId":264035,"journal":{"name":"International Conference on Computers and Their Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Zero-skipping in CapsNet. Is it worth it?\",\"authors\":\"R. Sharifi, Pouya Shiri, A. Baniasadi\",\"doi\":\"10.29007/cd8h\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Capsule networks (CapsNet) are the next generation of neural networks. CapsNet can be used for classification of data of different types. Today’s General Purpose Graphical Processing Units (GPGPUs) are more capable than before and let us train these complex networks. However, time and energy consumption remains a challenge. In this work, we investigate if skipping trivial operations i.e. multiplication by zero in CapsNet, can possibly save energy. We base our analysis on the number of multiplications by zero detected while training CapsNet on MNIST and FashionMNIST datasets.\",\"PeriodicalId\":264035,\"journal\":{\"name\":\"International Conference on Computers and Their Applications\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-03-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Computers and Their Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.29007/cd8h\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Computers and Their Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.29007/cd8h","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Capsule networks (CapsNet) are the next generation of neural networks. CapsNet can be used for classification of data of different types. Today’s General Purpose Graphical Processing Units (GPGPUs) are more capable than before and let us train these complex networks. However, time and energy consumption remains a challenge. In this work, we investigate if skipping trivial operations i.e. multiplication by zero in CapsNet, can possibly save energy. We base our analysis on the number of multiplications by zero detected while training CapsNet on MNIST and FashionMNIST datasets.
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