一种新的双相神经元编码器实现

Madhuvanthi Srivatsav R, B. Kailath
{"title":"一种新的双相神经元编码器实现","authors":"Madhuvanthi Srivatsav R, B. Kailath","doi":"10.1109/AIIoT52608.2021.9454233","DOIUrl":null,"url":null,"abstract":"In an effort to address the issue of low power and area constraints which are important pre-requisites to many applications in the field of signal processing, this work focuses on the implementation of a novel biphasic neuron architecture, that is proven to be energy efficient, and highly compact. The low power Adaptive exponential integrate and fire neuron (ADEx I&F), is implemented as a biphasic encoder in 180 nm CMOS Technology, and a SER of upto 60 dB and a figure of merit (FOM) of 0.26 pJ/conversion is achieved. The proposed biphasic encoder is found to exhibit similar performance characteristics with respect to the existing architectures while comprising of 52 % lesser number of transistors than the conventional biphasic neuron encoder models.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Novel Biphasic Neuron Encoder Implementation\",\"authors\":\"Madhuvanthi Srivatsav R, B. Kailath\",\"doi\":\"10.1109/AIIoT52608.2021.9454233\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In an effort to address the issue of low power and area constraints which are important pre-requisites to many applications in the field of signal processing, this work focuses on the implementation of a novel biphasic neuron architecture, that is proven to be energy efficient, and highly compact. The low power Adaptive exponential integrate and fire neuron (ADEx I&F), is implemented as a biphasic encoder in 180 nm CMOS Technology, and a SER of upto 60 dB and a figure of merit (FOM) of 0.26 pJ/conversion is achieved. The proposed biphasic encoder is found to exhibit similar performance characteristics with respect to the existing architectures while comprising of 52 % lesser number of transistors than the conventional biphasic neuron encoder models.\",\"PeriodicalId\":443405,\"journal\":{\"name\":\"2021 IEEE World AI IoT Congress (AIIoT)\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-05-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE World AI IoT Congress (AIIoT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AIIoT52608.2021.9454233\",\"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 World AI IoT Congress (AIIoT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AIIoT52608.2021.9454233","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

为了解决低功耗和面积限制的问题,这是信号处理领域许多应用的重要先决条件,这项工作的重点是实现一种新的双相神经元结构,该结构被证明是节能的,并且高度紧凑。采用180nm CMOS技术实现了低功耗自适应指数积分和火神经元(ADEx I&F)作为双相编码器,实现了高达60 dB的SER和0.26 pJ/转换的品质因数(FOM)。与现有架构相比,所提出的双相编码器显示出相似的性能特征,同时包含的晶体管数量比传统的双相神经元编码器模型少52%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Biphasic Neuron Encoder Implementation
In an effort to address the issue of low power and area constraints which are important pre-requisites to many applications in the field of signal processing, this work focuses on the implementation of a novel biphasic neuron architecture, that is proven to be energy efficient, and highly compact. The low power Adaptive exponential integrate and fire neuron (ADEx I&F), is implemented as a biphasic encoder in 180 nm CMOS Technology, and a SER of upto 60 dB and a figure of merit (FOM) of 0.26 pJ/conversion is achieved. The proposed biphasic encoder is found to exhibit similar performance characteristics with respect to the existing architectures while comprising of 52 % lesser number of transistors than the conventional biphasic neuron encoder models.
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:481959085
Book学术官方微信