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

Fast and Low-Memory Deep Neural Networks Using Binary Matrix Factorization