On Study of the Binarized Deep Neural Network for Image Classification
This addresses the challenge of deploying deep neural networks on individual devices by reducing resource requirements, though it appears incremental as it builds on existing network concepts.
The paper tackles the problem of deep neural networks requiring high-performance GPUs and large storage by proposing a binarized deep neural network, where all values and calculations are binary, resulting in significant savings in computational resources and storage, enabling use on various devices.
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.