Fast and Low-Memory Deep Neural Networks Using Binary Matrix Factorization
This work addresses resource efficiency for deploying deep neural networks, but it appears incremental as it builds on existing matrix factorization methods.
The paper tackles the problem of deep neural networks being computationally and memory intensive by applying binary matrix factorization, which significantly reduces resource requirements and enables practical implementation.
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.