T. Shimobaba, Yota Yamamoto, I. Hoshi, T. Kakue, T. Ito
{"title":"基于二元神经网络的全息存储器数据页分类","authors":"T. Shimobaba, Yota Yamamoto, I. Hoshi, T. Kakue, T. Ito","doi":"10.1109/INDIN45582.2020.9442176","DOIUrl":null,"url":null,"abstract":"This study investigates the performance of a binary neural network, which is a lightweight neural network, for classification problems in holographic applications. We performed data classification in holographic memory using XNOR-Net as one of the binary neural networks. We compared the performance of the binary neural network with convolutional neural networks.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Data page classification in holographic memory using binary neural network\",\"authors\":\"T. Shimobaba, Yota Yamamoto, I. Hoshi, T. Kakue, T. Ito\",\"doi\":\"10.1109/INDIN45582.2020.9442176\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study investigates the performance of a binary neural network, which is a lightweight neural network, for classification problems in holographic applications. We performed data classification in holographic memory using XNOR-Net as one of the binary neural networks. We compared the performance of the binary neural network with convolutional neural networks.\",\"PeriodicalId\":185948,\"journal\":{\"name\":\"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-07-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INDIN45582.2020.9442176\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INDIN45582.2020.9442176","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Data page classification in holographic memory using binary neural network
This study investigates the performance of a binary neural network, which is a lightweight neural network, for classification problems in holographic applications. We performed data classification in holographic memory using XNOR-Net as one of the binary neural networks. We compared the performance of the binary neural network with convolutional neural networks.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
