There are many frameworks for building, training and executing neural networks. Each of them offers their own format for storing network architecture. There are two frameworks considered in this paper: Caffe and Torch. They offer Google Protocol Buffer (protobuf) and the built-in Torch format for storing the architecture of neural networks. The existence of different formats leads to the difficulties of porting neural networks to finite devices of different manufacturers. It leads to difficulties in porting neural networks to end-point devices of different vendors. To resolve these issues the Khronos Group proposed universal NNEF format which will be mediator between frameworks and proprietary low-level libraries. The NNEF format allows storing a description of a neural network using a computational graph. In this paper the two main approaches of development of import (parsing) library for neural networks stored in NNEF: online and offline parsing. For each approach an advantages and disadvantages were noticed which will help developers to choose correct way of NNEF parser implementation. The main advantage of an offline parser is simplicity for debugging, and the online parser is a low computational complexity.
Low computational complexity algorithm for recognition highly corrupted QR codes based on Hamming-Lippmann neural network
