  Despite the outstanding performance of deep neural networks in different
applications, they are still computationally extensive and require a great
number of memories. This motivates more research on reducing the resources
required for implementing such networks. An efficient approach addressed for
this purpose is matrix factorization, which has been shown to be effective on
different networks. In this paper, we utilize binary matrix factorization and
show its great efficiency in reducing the required number of resources in deep
neural networks. In effect, this technique can lead to the practical
implementation of such networks.
