  To 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 to achieve the desired pruning rate
without accuracy loss. In this paper, we propose a unified DNN weight pruning
framework with dynamically updated regularization terms bounded by the
designated constraint, which can generate both non-structured sparsity and
different kinds of structured sparsity. We also extend our method to an
integrated framework for the combination of different DNN compression tasks.
