Hassan Dbouk, Sujan Kumar Gonugondla, Charbel Sakr, Naresh R Shanbhag
{"title":"KeyRAM:一个0.34 uJ/decision 18k decisions/s的循环注意内存处理器，用于关键字识别","authors":"Hassan Dbouk, Sujan Kumar Gonugondla, Charbel Sakr, Naresh R Shanbhag","doi":"10.1109/CICC48029.2020.9075923","DOIUrl":null,"url":null,"abstract":"This paper presents a 0.34 uJ/decision deep learning-based classifier for keyword spotting (KWS) in 65 nm CMOS with all weights stored on-chip. This work adapts a Recurrent Attention Model (RAM) algorithm for the KWS task, and employs an in-memory computing (IMC) architecture to achieve up to 9× savings in energy/decision and more than 23× savings in EDP of decisions over a state-of-the art IMC IC for KWS using the Google Speech dataset while achieving the highest reported decision throughput of 18.32 k decisions/s.","PeriodicalId":409525,"journal":{"name":"2020 IEEE Custom Integrated Circuits Conference (CICC)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"KeyRAM: A 0.34 uJ/decision 18 k decisions/s Recurrent Attention In-memory Processor for Keyword Spotting\",\"authors\":\"Hassan Dbouk, Sujan Kumar Gonugondla, Charbel Sakr, Naresh R Shanbhag\",\"doi\":\"10.1109/CICC48029.2020.9075923\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a 0.34 uJ/decision deep learning-based classifier for keyword spotting (KWS) in 65 nm CMOS with all weights stored on-chip. This work adapts a Recurrent Attention Model (RAM) algorithm for the KWS task, and employs an in-memory computing (IMC) architecture to achieve up to 9× savings in energy/decision and more than 23× savings in EDP of decisions over a state-of-the art IMC IC for KWS using the Google Speech dataset while achieving the highest reported decision throughput of 18.32 k decisions/s.\",\"PeriodicalId\":409525,\"journal\":{\"name\":\"2020 IEEE Custom Integrated Circuits Conference (CICC)\",\"volume\":\"137 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE Custom Integrated Circuits Conference (CICC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CICC48029.2020.9075923\",\"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 Custom Integrated Circuits Conference (CICC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CICC48029.2020.9075923","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
KeyRAM: A 0.34 uJ/decision 18 k decisions/s Recurrent Attention In-memory Processor for Keyword Spotting
This paper presents a 0.34 uJ/decision deep learning-based classifier for keyword spotting (KWS) in 65 nm CMOS with all weights stored on-chip. This work adapts a Recurrent Attention Model (RAM) algorithm for the KWS task, and employs an in-memory computing (IMC) architecture to achieve up to 9× savings in energy/decision and more than 23× savings in EDP of decisions over a state-of-the art IMC IC for KWS using the Google Speech dataset while achieving the highest reported decision throughput of 18.32 k decisions/s.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
