Edge AI prospect using the NeuroEdge computing system: Introducing a novel neuromorphic technology
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Nwakanma, Cosmas Ifeanyi | - |
dc.contributor.author | Kim, Jae-Woo | - |
dc.contributor.author | Lee, Jae-Min | - |
dc.contributor.author | Kim, Dong-Seong | - |
dc.date.accessioned | 2021-06-28T02:40:05Z | - |
dc.date.available | 2021-06-28T02:40:05Z | - |
dc.date.created | 2021-06-28 | - |
dc.date.issued | 2021-06 | - |
dc.identifier.issn | 2405-9595 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/19331 | - |
dc.description.abstract | This paper presents a test bed demonstration of NeuroEdge computing for face recognition using a novel neuromorphic chip-NM500. First, a general description and important specifications of the NM500 are presented. Second, a face recognition test-bed case study is used to demonstrate the efficacy and efficiency of the chip. Neuromorphic technology offers scalability and consistent recognition time, which is required by real-time networked systems, and presents a considerable advantage for real-time computations, making them virtually independent of the dataset size. In this study, intelligent edge computing technology was introduced using NeuroEdge. The performance was verified using a face recognition test. The results demonstrated that using neuromorphic technology, such as the NM500 chip, saves the time needed for training systems and does not impose the burden of requiring many datasets for effective training. (C) 2021 The Korean Institute of Communications and Information Sciences (KICS). Publishing Services by Elsevier B.V. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER | - |
dc.title | Edge AI prospect using the NeuroEdge computing system: Introducing a novel neuromorphic technology | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Nwakanma, Cosmas Ifeanyi | - |
dc.contributor.affiliatedAuthor | Kim, Jae-Woo | - |
dc.contributor.affiliatedAuthor | Lee, Jae-Min | - |
dc.contributor.affiliatedAuthor | Kim, Dong-Seong | - |
dc.identifier.doi | 10.1016/j.icte.2021.05.003 | - |
dc.identifier.wosid | 000659127000004 | - |
dc.identifier.bibliographicCitation | ICT EXPRESS, v.7, no.2, pp.152 - 157 | - |
dc.relation.isPartOf | ICT EXPRESS | - |
dc.citation.title | ICT EXPRESS | - |
dc.citation.volume | 7 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 152 | - |
dc.citation.endPage | 157 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | HARDWARE | - |
dc.subject.keywordAuthor | Edge device | - |
dc.subject.keywordAuthor | Embedded systems | - |
dc.subject.keywordAuthor | Edge AI | - |
dc.subject.keywordAuthor | Neuromorphic technology | - |
dc.subject.keywordAuthor | Training time | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
350-27, Gumi-daero, Gumi-si, Gyeongsangbuk-do, Republic of Korea (39253)054-478-7170
COPYRIGHT 2020 Kumoh University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.