Reweighted Alternating Direction Method of Multipliers for DNN weight pruning

As Deep Neural Networks (DNNs) continue to grow in complexity and size, leading to a substantial computational burden, weight pruning techniques have emerged as an effective solution. This paper presents a novel method for dynamic regularization-based pruning, which incorporates the Alternating Direction Method of Multipliers (ADMM). Unlike conventional methods that employ simple and abrupt threshold processing, the proposed method introduces a reweighting mechanism to assign importance to the weights in DNNs. Compared to other ADMM-based methods, the new method not only achieves higher accuracy but also saves considerable time thanks to the reduced number of necessary hyperparameters. The method is evaluated on multiple architectures, including LeNet-5, ResNet-32, ResNet-56, and ResNet-50, using the MNIST, CIFAR-10, and ImageNet datasets, respectively. Experimental results demonstrate its superior performance in terms of compression ratios and accuracy compared to state-of-the-art pruning methods. In particular, on the LeNet-5 model for the MNIST dataset, it achieves compression ratios of 355.9 × with a slight improvement in accuracy; on the ResNet-50 model trained with the ImageNet dataset, it achieves compression ratios of 4.24 × without sacrificing accuracy.