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학술지 Interworking Technology of Neural Network and Data among Deep Learning Frameworks
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저자
박재복, 유승목, 윤석진, 이경희, 조창식
발행일
201912
출처
ETRI Journal, v.41 no.6, pp.760-770
ISSN
1225-6463
출판사
한국전자통신연구원 (ETRI)
DOI
https://dx.doi.org/10.4218/etrij.2018-0135
협약과제
18HS1400, 운전자 주행경험 모사기반 일반도로환경의 자율주행4단계(SAE)를 지원하는 주행판단엔진 개발, 최정단
초록
Based on the growing demand for neural network technologies, various neural network inference engines are being developed. However, each inference engine has its own neural network storage format. There is a growing demand for standardization to solve this problem. This study presents interworking techniques for ensuring the compatibility of neural networks and data among the various deep learning frameworks. The proposed technique standardizes the graphic expression grammar and learning data storage format using the Neural Network Exchange Format (NNEF) of Khronos. The proposed converter includes a lexical, syntax, and parser. This NNEF parser converts neural network information into a parsing tree and quantizes data. To validate the proposed system, we verified that MNIST is immediately executed by importing AlexNet's neural network and learned data. Therefore, this study contributes an efficient design technique for a converter that can execute a neural network and learned data in various frameworks regardless of the storage format of each framework.
키워드
AI, AlexNet, Caffe, CNN, deep learning, interworking, neural network, NNEF, parser, Tensorflow
KSP 제안 키워드
Deep learning framework, Design techniques, Exchange format, Graphic expression, Inference Engine, Learning data, Network Inference, Network information, Network technology, Neural networks, Parsing tree
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