{"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}
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.