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Recently, the deep neural network (derived from the artificial neuralnetwork) has attracted many researchers' attention by its outstandingperformance. However, since this network requires high-performance GPUs andlarge storage, it is very hard to use it on individual devices. In order toimprove the deep neural network, many trials have been made by refining thenetwork structure or training strategy. Unlike those trials, in this paper, wefocused on the basic propagation function of the artificial neural network andproposed the binarized deep neural network. This network is a pure binarysystem, in which all the values and calculations are binarized. As a result,our network can save a lot of computational resource and storage. Therefore, itis possible to use it on various devices. Moreover, the experimental resultsproved the feasibility of the proposed network.

On Study of the Binarized Deep Neural Network for Image Classification