Learning both Weights and Connections for Efficient Neural Networks

@inproceedings{Han2015LearningBW,
  title={Learning both Weights and Connections for Efficient Neural Networks},
  author={Song Han and Jeff Pool and John Tran and William J. Dally},
  booktitle={NIPS},
  year={2015}
}
Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems. Also, conventional networks fix the architecture before training starts; as a result, training cannot improve the architecture. To address these limitations, we describe a method to reduce the storage and computation required by neural networks by an order of magnitude without affecting their accuracy by learning only the important connections. Our method prunes… CONTINUE READING

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