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Journal Article 임베디드 시스템용 딥러닝 추론 엔진 기술 동향
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Authors
유승목, 이경희, 박재복, 윤석진, 조창식, 정영준, 조일연
Issue Date
2019-08
Citation
전자통신동향분석, v.34, no.4, pp.23-31
ISSN
1225-6455
Publisher
한국전자통신연구원
Language
Korean
Type
Journal Article
DOI
https://dx.doi.org/10.22648/ETRI.2019.J.340403
Abstract
Deep learning is a hot topic in both academic and industrial fields. Deep learning applications can be categorized into two areas. The first category involves applications such as Google Alpha Go using interfaces with human operators to run complicated inference engines in high-performance servers. The second category includes embedded applications for mobile Internet-of-Things devices, automotive vehicles, etc. Owing to the characteristics of the deployment environment, applications in the second category should be bounded by certain H/W and S/W restrictions depending on their running environment. For example, image recognition in an autonomous vehicle requires low latency, while that on a mobile device requires low power consumption. In this paper, we describe issues faced by embedded applications and review popular inference engines. We also introduce a project that is being development to satisfy the H/W and S/W requirements.
KSP Keywords
Alpha Go, Autonomous vehicle, Embedded applications, High performance, Hot topics, Inference Engine, Internet of thing(IoT), Low latency, Mobile devices, deep learning(DL), human operator
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
Type 4: