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김성은,박형일,임인기,오광일,강태욱,박미정,강성원,Kim, S.E.,Park, H.I.,Lim, I.G.,Oh, K.I.,Kang, T.W.,Park, M.J.,Kang, S.W. 한국전자통신연구원 2016 전자통신동향분석 Vol.31 No.6
인체에 근접한 다양한 휴대 정보 단말기 간의 통신망을 무선으로 구축하는 Wireless Body Area Network(WBAN) 분야에 관한 연구 결과가 국내외에서 지속적으로 발표되고 있다. 이 가운데 인체통신 기술은 인체를 신호의 전송경로로 활용하여 단말기들 간의 연결을 위한 케이블이 필요하지 않으며, 저전력 고속 데이터 전송이 가능하여 WBAN에 가장 적합한 통신 기술로 손꼽힌다. 더불어 인체통신 기술은 사용자의 간단한 접촉을 기반으로 인체 네트워크를 구성하므로 웨어러블 디바이스/센서/단말 및 임플란트 디바이스 분야에 반드시 필요한 핵심 통신 기술로 주목받고 있다. 본고에서는 WBAN에서 최근 인체통신 기술의 개발 동향과 활용 분야 및 표준화 동향에 관하여 살펴보고자 한다.
오광일,김성은,배영환,박성모,이재진,강성원,Oh, K.I.,Kim, S.E.,Bae, Y.H.,Park, S.M.,Lee, J.J.,Kang, S.W. 한국전자통신연구원 2018 전자통신동향분석 Vol.33 No.6
In the age of IoT, in which everything is connected to a network, there have been increases in the amount of data traffic, latency, and the risk of personal privacy breaches that conventional cloud computing technology cannot cope with. The idea of edge computing has emerged as a solution to these issues, and furthermore, the concept of ultra-low power edge intelligent semiconductors in which the IoT device itself performs intelligent decisions and processes data has been established. The key elements of this function are an intelligent semiconductor based on artificial intelligence, connectivity for the efficient connection of neurons and synapses, and a large-scale spiking neural network simulation framework for the performance prediction of a neural network. This paper covers the current trends in ultra-low power edge intelligent semiconductors including issues regarding their technology and application.
오광일,김성은,배영환,박경환,권영수,Oh, K.I.,Kim, S.E.,Bae, Y.H.,Park, K.H.,Kwon, Y.S. 한국전자통신연구원 2020 전자통신동향분석 Vol.35 No.3
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.