A parser generator using the Grammar Flow Graph

Nakwijit, Pakawat; Ratanaworabhan, Paruj

doi:10.1109/icsec.2015.7401403

Cited by 1 publication

(1 citation statement)

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“…The NNEF parser was designed as an open source. We used LEX to support lexical analysis and YACC to automate parser generation. Moreover, we defined a token analysis rule that can recognize symbols defined in the NNEF document using LEX and YACC.…”

Section: Methodsmentioning

confidence: 99%

Interworking technology of neural network and data among deep learning frameworks

et al. 2019

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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.

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Section: Methodsmentioning

confidence: 99%