3.37 μW/Ch模块化可扩展神经记录系统,嵌入式无损压缩,用于动态降低功耗

Sung-Yun Park, Jihyun Cho, E. Yoon
{"title":"3.37 μW/Ch模块化可扩展神经记录系统,嵌入式无损压缩,用于动态降低功耗","authors":"Sung-Yun Park, Jihyun Cho, E. Yoon","doi":"10.23919/VLSIC.2017.8008468","DOIUrl":null,"url":null,"abstract":"We report a neural recording system with embedded lossless compression using spatiotemporal correlation and sparsity of neural signals to reduce dynamic power (Pd) dissipation for data transmission in high-density neural recording systems. We could successfully compress the data rate of neural signals by a factor of 5.35 (local field potential, LFP) and 10.54 (action potential, AP), respectively. Consequently we reduced Pd consumption by 89% while achieving the state-of-the-art recording performance of 3.37 μW/Ch, 5.18 μVrms input-referred noise, and 3.41NEF2Vdd.","PeriodicalId":176340,"journal":{"name":"2017 Symposium on VLSI Circuits","volume":"115 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"3.37 μW/Ch modular scalable neural recording system with embedded lossless compression for dynamic power reduction\",\"authors\":\"Sung-Yun Park, Jihyun Cho, E. Yoon\",\"doi\":\"10.23919/VLSIC.2017.8008468\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We report a neural recording system with embedded lossless compression using spatiotemporal correlation and sparsity of neural signals to reduce dynamic power (Pd) dissipation for data transmission in high-density neural recording systems. We could successfully compress the data rate of neural signals by a factor of 5.35 (local field potential, LFP) and 10.54 (action potential, AP), respectively. Consequently we reduced Pd consumption by 89% while achieving the state-of-the-art recording performance of 3.37 μW/Ch, 5.18 μVrms input-referred noise, and 3.41NEF2Vdd.\",\"PeriodicalId\":176340,\"journal\":{\"name\":\"2017 Symposium on VLSI Circuits\",\"volume\":\"115 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 Symposium on VLSI Circuits\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/VLSIC.2017.8008468\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Symposium on VLSI Circuits","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/VLSIC.2017.8008468","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

摘要

我们报道了一种嵌入式无损压缩的神经记录系统,利用神经信号的时空相关性和稀疏性来减少高密度神经记录系统中数据传输的动态功率(Pd)耗散。我们可以成功地将神经信号的数据率分别压缩5.35倍(局部场电位,LFP)和10.54倍(动作电位,AP)。因此,我们将Pd消耗降低了89%,同时实现了最先进的记录性能:3.37 μW/Ch, 5.18 μVrms输入参考噪声和3.41NEF2Vdd。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
3.37 μW/Ch modular scalable neural recording system with embedded lossless compression for dynamic power reduction
We report a neural recording system with embedded lossless compression using spatiotemporal correlation and sparsity of neural signals to reduce dynamic power (Pd) dissipation for data transmission in high-density neural recording systems. We could successfully compress the data rate of neural signals by a factor of 5.35 (local field potential, LFP) and 10.54 (action potential, AP), respectively. Consequently we reduced Pd consumption by 89% while achieving the state-of-the-art recording performance of 3.37 μW/Ch, 5.18 μVrms input-referred noise, and 3.41NEF2Vdd.
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
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信