Rei Sumikawa;Atsutake Kosuge;Yao-Chung Hsu;Kota Shiba;Mototsugu Hamada;Tadahiro Kuroda
{"title":"采用算法-电路协同优化技术的 A183.4-nJ/Inference 152.8-μW 35 语音命令识别有线逻辑处理器","authors":"Rei Sumikawa;Atsutake Kosuge;Yao-Chung Hsu;Kota Shiba;Mototsugu Hamada;Tadahiro Kuroda","doi":"10.1109/LSSC.2023.3334625","DOIUrl":null,"url":null,"abstract":"A 183.4-nJ/inference single-chip wired-logic DNN processor that is capable of recognizing all 35 commands defined in the industrial standard voice recognition data set (Google speech command dataset) is developed. The algorithm-circuit co-optimized processor recognizes 3.5 times more commands with 1.6 times better-energy efficiency than the state-of-the-art analog processor while keeping design cost low. By implementing all the processing circuits and wiring required for the 16-layer DNN onto a single chip (\n<inline-formula> <tex-math>$7.63 {\\mathrm{ mm}}^{2}$ </tex-math></inline-formula>\n in 40 nm), the need to store weight coefficients and intermediate data in DRAM/SRAM is eliminated. Owing to the proposed architecture, a low-power consumption of \n<inline-formula> <tex-math>$152.8 \\mu \\text{W}$ </tex-math></inline-formula>\n is achieved, which is low enough for always-on applications on battery-powered IoT devices.","PeriodicalId":13032,"journal":{"name":"IEEE Solid-State Circuits Letters","volume":"7 ","pages":"22-25"},"PeriodicalIF":2.2000,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A183.4-nJ/Inference 152.8-μW 35-Voice Commands Recognition Wired-Logic Processor Using Algorithm-Circuit Co-Optimization Technique\",\"authors\":\"Rei Sumikawa;Atsutake Kosuge;Yao-Chung Hsu;Kota Shiba;Mototsugu Hamada;Tadahiro Kuroda\",\"doi\":\"10.1109/LSSC.2023.3334625\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A 183.4-nJ/inference single-chip wired-logic DNN processor that is capable of recognizing all 35 commands defined in the industrial standard voice recognition data set (Google speech command dataset) is developed. The algorithm-circuit co-optimized processor recognizes 3.5 times more commands with 1.6 times better-energy efficiency than the state-of-the-art analog processor while keeping design cost low. By implementing all the processing circuits and wiring required for the 16-layer DNN onto a single chip (\\n<inline-formula> <tex-math>$7.63 {\\\\mathrm{ mm}}^{2}$ </tex-math></inline-formula>\\n in 40 nm), the need to store weight coefficients and intermediate data in DRAM/SRAM is eliminated. Owing to the proposed architecture, a low-power consumption of \\n<inline-formula> <tex-math>$152.8 \\\\mu \\\\text{W}$ </tex-math></inline-formula>\\n is achieved, which is low enough for always-on applications on battery-powered IoT devices.\",\"PeriodicalId\":13032,\"journal\":{\"name\":\"IEEE Solid-State Circuits Letters\",\"volume\":\"7 \",\"pages\":\"22-25\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2023-11-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Solid-State Circuits Letters\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10324346/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Solid-State Circuits Letters","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10324346/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
A 183.4-nJ/inference single-chip wired-logic DNN processor that is capable of recognizing all 35 commands defined in the industrial standard voice recognition data set (Google speech command dataset) is developed. The algorithm-circuit co-optimized processor recognizes 3.5 times more commands with 1.6 times better-energy efficiency than the state-of-the-art analog processor while keeping design cost low. By implementing all the processing circuits and wiring required for the 16-layer DNN onto a single chip (
$7.63 {\mathrm{ mm}}^{2}$
in 40 nm), the need to store weight coefficients and intermediate data in DRAM/SRAM is eliminated. Owing to the proposed architecture, a low-power consumption of
$152.8 \mu \text{W}$
is achieved, which is low enough for always-on applications on battery-powered IoT devices.