ETRI-Knowledge Sharing Plaform

ENGLISH

성과물

논문 검색
구분 SCI
연도 ~ 키워드

상세정보

학술지 CitiusSynapse: A Deep Learning Framework for Embedded Systems
Cited 0 time in scopus Download 15 time Share share facebook twitter linkedin kakaostory
저자
홍승태, 조현우, 김정시
발행일
202112
출처
Applied Sciences, v.11 no.23, pp.1-22
ISSN
2076-3417
출판사
MDPI
DOI
https://dx.doi.org/10.3390/app112311570
협약과제
21HS1900, 스마트기기를 위한 온디바이스 지능형 정보처리 가속화 SW플랫폼 기술 개발, 김정시
초록
As embedded systems, such as smartphones with limited resources, have become increas-ingly popular, active research has recently been conducted on performing on-device deep learning in such systems. Therefore, in this study, we propose a deep learning framework that is specialized for embedded systems with limited resources, the operation processing structure of which differs from that of standard PCs. The proposed framework supports an OpenCL-based accelerator engine for accelerator deep learning operations in various embedded systems. Moreover, the parallel processing performance of OpenCL is maximized through an OpenCL kernel that is optimized for embedded GPUs, and the structural characteristics of embedded systems, such as unified memory. Furthermore, an on-device optimizer for optimizing the performance in on-device environments, and model con-verters for compatibility with conventional frameworks, are provided. The results of a performance evaluation show that the proposed on-device framework outperformed conventional methods.
키워드
Deep learning framework, Embedded systems, On-device, OpenCL acceleration
KSP 제안 키워드
Conventional methods, Deep learning framework, Embedded system, Limited resources, Parallel Processing, Performance evaluation, Unified memory, deep learning(DL), device framework, processing performance, structural characteristics
본 저작물은 크리에이티브 커먼즈 저작자 표시 (CC BY) 조건에 따라 이용할 수 있습니다.
저작자 표시 (CC BY)