基于记忆装置的神经形态视觉系统的概念

S. Shchanikov, I. Bordanov
{"title":"基于记忆装置的神经形态视觉系统的概念","authors":"S. Shchanikov, I. Bordanov","doi":"10.1109/DCNA56428.2022.9923295","DOIUrl":null,"url":null,"abstract":"Here we propose the concept of neuromorphic analog memristive vision systems. The main feature of this concept is the rejection of analog-to-digital and digital-to-analog conversions when capturing input visual data for a spiking neural network (SNN) based on memristive devices. This can be achieved by combining photodiodes and memristors and directly feeding analog pulses from the output of such a circuit to the input of a SNN circuit. This concept relates to the field of in-memory and in-sensor computing and will makes it possible to create more compact, energy-efficient visual processing units for wearable, on-board and embedded electronics for such areas as robotics, the Internet of Things, neuroprosthetics and other practical applications in the field of artificial intelligence.","PeriodicalId":110836,"journal":{"name":"2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Concept of Neuromorphic Vision Systems based on Memristive Devices\",\"authors\":\"S. Shchanikov, I. Bordanov\",\"doi\":\"10.1109/DCNA56428.2022.9923295\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Here we propose the concept of neuromorphic analog memristive vision systems. The main feature of this concept is the rejection of analog-to-digital and digital-to-analog conversions when capturing input visual data for a spiking neural network (SNN) based on memristive devices. This can be achieved by combining photodiodes and memristors and directly feeding analog pulses from the output of such a circuit to the input of a SNN circuit. This concept relates to the field of in-memory and in-sensor computing and will makes it possible to create more compact, energy-efficient visual processing units for wearable, on-board and embedded electronics for such areas as robotics, the Internet of Things, neuroprosthetics and other practical applications in the field of artificial intelligence.\",\"PeriodicalId\":110836,\"journal\":{\"name\":\"2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA)\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-09-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DCNA56428.2022.9923295\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DCNA56428.2022.9923295","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

在此,我们提出了神经形态模拟记忆视觉系统的概念。该概念的主要特点是在为基于记忆器件的峰值神经网络(SNN)捕获输入视觉数据时,拒绝模数和数模转换。这可以通过结合光电二极管和忆阻器并直接将模拟脉冲从这种电路的输出馈送到SNN电路的输入来实现。这一概念与内存和传感器计算领域有关,并将为可穿戴、机载和嵌入式电子产品创造更紧凑、更节能的视觉处理单元,用于机器人、物联网、神经假肢和人工智能领域的其他实际应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Concept of Neuromorphic Vision Systems based on Memristive Devices
Here we propose the concept of neuromorphic analog memristive vision systems. The main feature of this concept is the rejection of analog-to-digital and digital-to-analog conversions when capturing input visual data for a spiking neural network (SNN) based on memristive devices. This can be achieved by combining photodiodes and memristors and directly feeding analog pulses from the output of such a circuit to the input of a SNN circuit. This concept relates to the field of in-memory and in-sensor computing and will makes it possible to create more compact, energy-efficient visual processing units for wearable, on-board and embedded electronics for such areas as robotics, the Internet of Things, neuroprosthetics and other practical applications in the field of artificial intelligence.
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