{"title":"基于近传感器应用的机器学习计算和通信约简技术","authors":"M. A. Neggaz, S. Niar, F. Kurdahi","doi":"10.1109/ICM.2018.8704033","DOIUrl":null,"url":null,"abstract":"State-of-the-art Convolutional Neural Networks (CNN) are used to process images. In most cases, videos are streamed and processed frame by frame using a CNN. In this paper we present a two-step approach to process images in a real-life streaming environment. We exploit size-reduction and data encoding to reduce the computational and communication load. A near-sensor architecture is proposed. The final design reaches 14 EPS for the full Faster R-CNN pipeline.","PeriodicalId":305356,"journal":{"name":"2018 30th International Conference on Microelectronics (ICM)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Computational and Communication Reduction Technique in Machine Learning Based Near Sensor Applications\",\"authors\":\"M. A. Neggaz, S. Niar, F. Kurdahi\",\"doi\":\"10.1109/ICM.2018.8704033\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"State-of-the-art Convolutional Neural Networks (CNN) are used to process images. In most cases, videos are streamed and processed frame by frame using a CNN. In this paper we present a two-step approach to process images in a real-life streaming environment. We exploit size-reduction and data encoding to reduce the computational and communication load. A near-sensor architecture is proposed. The final design reaches 14 EPS for the full Faster R-CNN pipeline.\",\"PeriodicalId\":305356,\"journal\":{\"name\":\"2018 30th International Conference on Microelectronics (ICM)\",\"volume\":\"55 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 30th International Conference on Microelectronics (ICM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICM.2018.8704033\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 30th International Conference on Microelectronics (ICM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICM.2018.8704033","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Computational and Communication Reduction Technique in Machine Learning Based Near Sensor Applications
State-of-the-art Convolutional Neural Networks (CNN) are used to process images. In most cases, videos are streamed and processed frame by frame using a CNN. In this paper we present a two-step approach to process images in a real-life streaming environment. We exploit size-reduction and data encoding to reduce the computational and communication load. A near-sensor architecture is proposed. The final design reaches 14 EPS for the full Faster R-CNN pipeline.
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