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