{"title":"基于crnn的低功耗关键字识别系统的FPGA实现","authors":"Limo Guo, PengXu Lin, Lei Guo, Bo Liu","doi":"10.1109/ASICON52560.2021.9620311","DOIUrl":null,"url":null,"abstract":"A low-power and high-precision reconfigurable processor based on optimized convolutional recurrent neural network is proposed for noise robust keyword recognition. In order to create a low-power and high-precision system, we implemented a reconfigurable CRNN and quantization network on FPGA, which greatly reduced the use of DSP, BRAM, LUT and other resources. Our system can identify some keywords, such as \"yes\", \"no\", \"down\" and \"up\" within 50ms, and at a signal-to-noise ratio of-5dB, the actual accuracy reaches 86.4%.","PeriodicalId":233584,"journal":{"name":"2021 IEEE 14th International Conference on ASIC (ASICON)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Implementation of a CRNN-based low-power keyword recognition system on FPGA\",\"authors\":\"Limo Guo, PengXu Lin, Lei Guo, Bo Liu\",\"doi\":\"10.1109/ASICON52560.2021.9620311\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A low-power and high-precision reconfigurable processor based on optimized convolutional recurrent neural network is proposed for noise robust keyword recognition. In order to create a low-power and high-precision system, we implemented a reconfigurable CRNN and quantization network on FPGA, which greatly reduced the use of DSP, BRAM, LUT and other resources. Our system can identify some keywords, such as \\\"yes\\\", \\\"no\\\", \\\"down\\\" and \\\"up\\\" within 50ms, and at a signal-to-noise ratio of-5dB, the actual accuracy reaches 86.4%.\",\"PeriodicalId\":233584,\"journal\":{\"name\":\"2021 IEEE 14th International Conference on ASIC (ASICON)\",\"volume\":\"43 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE 14th International Conference on ASIC (ASICON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ASICON52560.2021.9620311\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 14th International Conference on ASIC (ASICON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASICON52560.2021.9620311","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Implementation of a CRNN-based low-power keyword recognition system on FPGA
A low-power and high-precision reconfigurable processor based on optimized convolutional recurrent neural network is proposed for noise robust keyword recognition. In order to create a low-power and high-precision system, we implemented a reconfigurable CRNN and quantization network on FPGA, which greatly reduced the use of DSP, BRAM, LUT and other resources. Our system can identify some keywords, such as "yes", "no", "down" and "up" within 50ms, and at a signal-to-noise ratio of-5dB, the actual accuracy reaches 86.4%.
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