A Unified DNN Weight Pruning Framework Using Reweighted Optimization Methods
Hosted in Virtual Platform
Approximate Computing for AI/ML
DescriptionTo address the large model size and intensive computation requirement of deep neural networks (DNNs), weight pruning techniques have been proposed and generally fall into two categories, i.e., static regularization-based pruning and dynamic regularization-based pruning. However, the former method currently suffers either complex workloads or accuracy degradation, while the latter one takes a long time to tune the parameters. In this paper, we propose a unified DNN weight pruning framework with dynamically updated regularization terms. Our proposed method increases the compression rate, reduces the training time and reduces the number of hyper-parameters compared with state-of-the-art ADMM-based hard constraint method.