A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers
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Weight pruning methods for deep neural networks (DNNs) have been investigated recently, but prior work in this area is mainly heuristic, iterative pruning, thereby lacking guarantees on the weight reduction ratio and convergence time. To mitigate these limitations, we present a systematic weight pruning framework of DNNs using the alternating direction method of multipliers (ADMM). We first formulate the weight pruning problem of DNNs as a nonconvex optimization problem with combinatorial constraints specifying the sparsity requirements, and then adopt the ADMM framework for systematic weight pruning. By using ADMM, the original nonconvex optimization problem is decomposed into two subproblems that are solved iteratively. One of these subproblems can be solved using stochastic gradient descent, while the other can be solved analytically. The proposed ADMM weight pruning method incurs no additional suboptimality besides that resulting from the nonconvex nature of the original optimization problem. Furthermore, our approach achieves a fast convergence rate. The weight pruning results are very promising and consistently outperform prior work. On the LeNet-5 model for the MNIST data set, we achieve 40.2 times weight reduction without accuracy loss. On the AlexNet model for the ImageNet data set, we achieve 20 times weight reduction without accuracy loss. When we focus on the convolutional layer pruning for computation reductions, we can reduce the total computation by five times compared with prior work (achieving a total of 13.4 times weight reduction in convolutional layers). A significant acceleration for DNN training is observed as well, in that we can finish the whole training process on AlexNet around 80 hours. Our models are released at https://drive.google.com/drive/folders/1_O9PLIFiNHIaQIuOIJjq0AyQ7UpotlNl.
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