论文标题
使用二进制矩阵分解的快速和低内存深神经网络
Fast and Low-Memory Deep Neural Networks Using Binary Matrix Factorization
论文作者
论文摘要
尽管在不同的应用程序中，深层神经网络的表现出色，但它们在计算上仍然很广泛，需要大量记忆。这激发了更多研究减少实施此类网络所需的资源的研究。为此目的解决的有效方法是矩阵分解，该方法已被证明对不同的网络有效。在本文中，我们利用了二进制矩阵分解，并在减少深神经网络中所需的资源数量方面表现出了极大的效率。实际上，这种技术可以导致此类网络的实际实施。
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.
