Multitasking and Memcomputing in Memristor Cellular Nonlinear Networks: Insights into the Underlying Mechanisms

I. Messaris, A. Ascoli, D. Prousalis, V. Ntinas, A. S. Demirkol, R. Tetzlaff
{"title":"Multitasking and Memcomputing in Memristor Cellular Nonlinear Networks: Insights into the Underlying Mechanisms","authors":"I. Messaris, A. Ascoli, D. Prousalis, V. Ntinas, A. S. Demirkol, R. Tetzlaff","doi":"10.1109/SMACD58065.2023.10192210","DOIUrl":null,"url":null,"abstract":"Memristor Cellular Nonlinear Networks (M-CNNs) represent a significant leap in computational technology compared to traditional Cellular Nonlinear Networks (CNNs), thanks to their multi-tasking and memcomputing capabilities. Recent studies have demonstrated various configurations of M-CNNs that utilize these capabilities to perform image processing tasks. This paper employs the Dynamic Route Map circuit-theoretic analysis tool to investigate the dynamic features of M-CNNs and shed light on the underlying mechanisms responsible for their ability to handle multiple tasks. The findings from this theoretical study offer valuable insights for the development of more compact and highly efficient data processing M-CNNs that possess such versatile properties.","PeriodicalId":239306,"journal":{"name":"2023 19th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD)","volume":"333 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 19th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SMACD58065.2023.10192210","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Memristor Cellular Nonlinear Networks (M-CNNs) represent a significant leap in computational technology compared to traditional Cellular Nonlinear Networks (CNNs), thanks to their multi-tasking and memcomputing capabilities. Recent studies have demonstrated various configurations of M-CNNs that utilize these capabilities to perform image processing tasks. This paper employs the Dynamic Route Map circuit-theoretic analysis tool to investigate the dynamic features of M-CNNs and shed light on the underlying mechanisms responsible for their ability to handle multiple tasks. The findings from this theoretical study offer valuable insights for the development of more compact and highly efficient data processing M-CNNs that possess such versatile properties.
忆阻器细胞非线性网络中的多任务处理和Memcomputing:对潜在机制的洞察
与传统的细胞非线性网络(cnn)相比,忆阻器细胞非线性网络(m - cnn)代表了计算技术的重大飞跃,这要归功于其多任务和记忆计算能力。最近的研究已经展示了利用这些能力执行图像处理任务的m - cnn的各种配置。本文采用动态路由图电路理论分析工具来研究m - cnn的动态特征,并揭示其处理多任务能力的潜在机制。这项理论研究的发现为开发更紧凑、更高效的数据处理m - cnn提供了有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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