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Escoin: Efficient Sparse Convolutional Neural Network Inference on GPUs

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Escoin

Copyright 2018 Xuhao Chen, National University of Defense Technology

This is a Caffe branch for training sparse CNN on GPUs (tested with AlexNet, GoogLeNet-v1, and Resnet-50). Please let us know if you're interested in this experimental feature. More details are described in the following paper:

https://arxiv.org/pdf/1802.10280, Escort: Efficient Sparse Convolutional Neural Networks on GPUs, Xuhao Chen

For datasets, compilation and exection instructions, please got to SkimCaffe.

Contact

Xuhao Chen

Caffe

Build Status License

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.

Check out the project site for all the details like

and step-by-step examples.

Custom distributions

Community

Join the chat at https://gitter.im/BVLC/caffe

Please join the caffe-users group or gitter chat to ask questions and talk about methods and models. Framework development discussions and thorough bug reports are collected on Issues.

Happy brewing!

License and Citation

Caffe is released under the BSD 2-Clause license. The BAIR/BVLC reference models are released for unrestricted use.

Please cite Caffe in your publications if it helps your research:

@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}
}