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Dynamic network surgery

Dynamic network surgery is a very effective method for DNN pruning. To better use it with python and matlab, you may also need a classic version of the Caffe framework. For the convolutional and fully-connected layers to be pruned, change their layer types to "CConvolution" and "CInnerProduct" respectively. Then, pass "cconvolution_param" and "cinner_product_param" messages to these modified layers for better pruning performance.

Example for usage

Below is an example for pruning the "ip1" layer in LeNet5:

layer {
  name: "ip1"
  type: "CInnerProduct"
  bottom: "pool2"
  top: "ip1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 500
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
  cinner_product_param {
    gamma: 0.0001
    power: 1
    c_rate: 4
    iter_stop: 14000  
    weight_mask_filler {
      type: "constant"
      value: 1
    }
    bias_mask_filler {
      type: "constant"
      value: 1
    }        
  }   
}

Citation

Please cite our work in your publications if it helps your research:

@inproceedings{guo2016dynamic,		
  title = {Dynamic Network Surgery for Efficient DNNs},
  author = {Guo, Yiwen and Yao, Anbang and Chen, Yurong},
  booktitle = {Advances in neural information processing systems (NIPS)},
  year = {2016}
} 

and do not forget about Caffe:

@article{jia2014caffe,
  Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
  Journal = {arXiv preprint arXiv:1408.5093},
  Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
  Year = {2014}
}

Enjoy your own surgeries!

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Caffe implementation for dynamic network surgery.

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