상세정보
학술지
Interworking Technology of Neural Network and Data among Deep Learning Frameworks
- 발행일
- 201912
- 출처
- ETRI Journal, v.41 no.6, pp.760-770
- ISSN
- 1225-6463
- 출판사
- 한국전자통신연구원 (ETRI)
- 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.
- KSP 제안 키워드
- Deep learning framework, Design techniques, Exchange format, Graphic expression, Inference Engine, Learning data, Network Inference, Network information, Network technology, Parsing tree, Storage Format
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