Kun Xie, Can Liu, Xin Wang, Xiaocan Li, Gaogang Xie, Jigang Wen, Kenli Li
Deep neural networks (DNNs) have made great breakthroughs and seen applications in many domains. However, the incomparable accuracy of DNNs is achieved with the cost of considerable memory consumption and high computational complexity, which restricts their deployment on conventional desktops and portable devices. To address this issue, low-rank factorization, which decomposes the neural network parameters into smaller sized matrices or tensors, has emerged as a promising technique for network compression. In this article, we propose leveraging the emerging tensor ring (TR) factorization to compress the neural network...
April 30, 2024: IEEE Transactions on Neural Networks and Learning Systems