用于雷达分类的轻量级、深度rnn

Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation Pub Date : 2019-11-13 DOI:10.1145/3360322.3361000

Dhrubojyoti Roy, S. Srivastava, Pranshu Jain, Aditya Kusupati, M. Varma, A. Arora

{"title":"用于雷达分类的轻量级、深度rnn","authors":"Dhrubojyoti Roy, S. Srivastava, Pranshu Jain, Aditya Kusupati, M. Varma, A. Arora","doi":"10.1145/3360322.3361000","DOIUrl":null,"url":null,"abstract":"We demonstrate Multi-Scale, Cascaded RNN (MSC-RNN)1, an energy-efficient recurrent neural network for real-time micro-power radar classification. Its two-tier architecture is jointly trained to reject clutter and discriminate displacing sources at different time-scales, with a lighter lower tier running continuously and a heavier upper tier invoked infrequently on an on-demand basis. It offers for single microcontroller devices a better trade-off in accuracy and efficiency, as well as in clutter suppression and detectability, over competitive shallow and deep alternatives.","PeriodicalId":128826,"journal":{"name":"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","volume":"422 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Lightweight, Deep RNNs for Radar Classification\",\"authors\":\"Dhrubojyoti Roy, S. Srivastava, Pranshu Jain, Aditya Kusupati, M. Varma, A. Arora\",\"doi\":\"10.1145/3360322.3361000\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We demonstrate Multi-Scale, Cascaded RNN (MSC-RNN)1, an energy-efficient recurrent neural network for real-time micro-power radar classification. Its two-tier architecture is jointly trained to reject clutter and discriminate displacing sources at different time-scales, with a lighter lower tier running continuously and a heavier upper tier invoked infrequently on an on-demand basis. It offers for single microcontroller devices a better trade-off in accuracy and efficiency, as well as in clutter suppression and detectability, over competitive shallow and deep alternatives.\",\"PeriodicalId\":128826,\"journal\":{\"name\":\"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation\",\"volume\":\"422 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3360322.3361000\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3360322.3361000","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 5

摘要

我们展示了多尺度级联RNN (MSC-RNN)1，一种用于实时微功率雷达分类的节能递归神经网络。它的两层结构被联合训练以拒绝杂波和区分不同时间尺度的位移源，较轻的下层连续运行，较重的上层在按需基础上不频繁调用。它为单个微控制器设备提供了精度和效率的更好权衡，以及杂波抑制和可探测性，而不是竞争性的浅层和深层替代方案。

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

Lightweight, Deep RNNs for Radar Classification

We demonstrate Multi-Scale, Cascaded RNN (MSC-RNN)1, an energy-efficient recurrent neural network for real-time micro-power radar classification. Its two-tier architecture is jointly trained to reject clutter and discriminate displacing sources at different time-scales, with a lighter lower tier running continuously and a heavier upper tier invoked infrequently on an on-demand basis. It offers for single microcontroller devices a better trade-off in accuracy and efficiency, as well as in clutter suppression and detectability, over competitive shallow and deep alternatives.

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

Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

自引率

0.00%

发文量