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A Unified DNN Weight Compression Framework Using Reweighted Optimization Methods
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