부산대학교 도서관

Due to deep neural networks (DNNs) a large number of parameters, DNNs increase the demand for computing and storage during training, reasoning and deployment, especially when DNNs stack deeper and wider. Tensor decomposition can not only compress DNN models but also reduce parameters and storage requirements while maintaining high accuracy and performance. About tensor ring (TR) decomposition of tensor decomposition, there are two problems: (1) The practice of setting the TR rank to be equal in TR decomposition results in an unreasonable rank configuration. (2) The training time of selecting rank through iterative processes is time-consuming. To address the two problems, a TR network compression method by Variational Bayesian (TR-VB) is proposed based on the Global Analytic Solution of Empirical Variational Bayesian Matrix Factorization (GAS of EVBMF). The method consists of three steps: (1) rank selection, (2) TR decomposition, and (3) fine-tuning to recover accumulated loss of accuracy. Experimental results show that, for a given network, TR-VB gives the best results in terms of Top-1 accuracy, parameters, and training time under different compression levels. Furthermore, TR-VB validated on CIFAR-10/100 public benchmarks achieves state-of-the-art performance.