ETRI-Knowledge Sharing Plaform

KOREAN
논문 검색
Type SCI
Year ~ Keyword

Detail

Journal Article 인공지능 뉴로모픽 반도체 기술 동향
Cited - time in scopus Download 283 time Share share facebook twitter linkedin kakaostory
Authors
오광일, 김성은, 배영환, 박경환, 권영수
Issue Date
2020-06
Citation
전자통신동향분석, v.35, no.3, pp.76-84
ISSN
1225-6455
Publisher
한국전자통신연구원
Language
Korean
Type
Journal Article
DOI
https://dx.doi.org/10.22648/ETRI.2020.J.350308
Abstract
Neuromorphic hardware refers to brain-inspired computers or components that model an artificial neural network comprising densely connected parallel neurons and synapses. The major element in the widespread deployment of neural networks in embedded devices are efficient architecture for neuromorphic hardware with regard to performance, power consumption, and chip area. Spiking neural networks (SiNNs) are brain-inspired in which the communication among neurons is modeled in the form of spikes. Owing to brainlike operating modes, SNNs can be power efficient. However, issues still exist with research and actual application of SNNs. In this issue, we focus on the technology development cases and market trends of two typical tracks, which are listed above, from the point of view of artificial intelligence neuromorphic circuits and subsequently describe their future development prospects.
KSP Keywords
Artificial Neural Network, Brain-inspired, Chip area, Efficient architecture, Embedded devices, Future Development, Market trends, Neuromorphic circuits, Neuromorphic hardware, Operating mode, Power Consumption
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
Type 4: