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Status: Unchecked ×Keyword: computation reduction ×Clear all

3 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   A Systematic DNN Weight Pruning Framework Using Alternating Direction Method of Multipliers

   Zhang, Ye, Zhang et al. · Lecture notes in computer science · 2018

   The paper presents a pruning framework for deep neural networks based on the alternating direction method of multipliers (ADMM), and reports large weight reductions on LeNet-5 and AlexNet without accuracy loss.

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   1. UncheckedOn LeNet-5 trained on MNIST, the authors report cutting the number of weights 71.2-fold with no loss of accuracy.“On the LeNet-5 model for the MNIST data set, we achieve 71.2 times weight reduction without accuracy loss.”
   2. UncheckedOn AlexNet trained for ImageNet, the authors' pruning method cuts the number of weights 21-fold without losing accuracy.“On the AlexNet model for the ImageNet data set, we achieve 21 times weight reduction without accuracy loss.”
   3. UncheckedPruning only convolutional layers, the authors report cutting total computation five-fold against prior work, with 13.4 times fewer weights in those layers.“When we focus on the convolutional layer pruning for computation reductions, we can reduce the total computation by five times compared with the prior work (achieving a total of 13.4 times weight reduction in convolutional layers).”