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admm-pruning

Prune DNN using Alternating Direction Method of Multipliers (ADMM)

Our paper

“A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers” (https://arxiv.org/abs/1804.03294)

Citation

If you use these models in your research, please cite:

@article{zhang2018systematic,
  title={A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers},
  author={Zhang, Tianyun and Ye, Shaokai and Zhang, Kaiqi and Tang, Jian and Wen, Wujie and Fardad, Makan and Wang, Yanzhi},
  journal={arXiv preprint arXiv:1804.03294},
  year={2018}
}

Models

  1. lenet-5
  • see tensorflow-mnist-model in this repository
  1. bvlc_alexnet (focus on weight reduction)
  1. bvlc_alexnet (focus on conv reduction)

Results

  1. lenet-5 (top1 accuracy: 99.2%)
Layer Weights Weights after prune Weights after prune %
conv1 0.5K 0.1K 20%
conv2 25K 2K 8%
fc1 400K 3.6K 0.9%
fc2 5K 0.35K 7%
Total 430.5K 6.05K 1.4%
  1. bvlc_alexnet (top5 accuracy: 80.2%, 40 iterations of ADMM)
Layer Weights Weights after prune Weights after prune %
conv1 34.8K 28.19K 81%
conv2 307.2K 61.44K 20%
conv3 884.7K 168.09K 19%
conv4 663.5K 132.7K 20%
conv5 442.4K 88.48K 20%
fc1 37.7M 1.06M 2.8%
fc2 16.8M 0.99M 5.9%
fc3 4.1M 0.38M 9.3%
Total 60.9M 2.9M 4.76%

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Prune DNN using Alternating Direction Method of Multipliers (ADMM)

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