TensorQuant: A Simulation Toolbox for Deep Neural Network Quantization

@inproceedings{Loroch2017TensorQuantAS,
  title={TensorQuant: A Simulation Toolbox for Deep Neural Network Quantization},
  author={Dominik Marek Loroch and Franz-Josef Pfreundt and Norbert Wehn and Janis Keuper},
  booktitle={MLHPC@SC},
  year={2017}
}
Recent research implies that training and inference of deep neural networks (DNN) can be computed with low precision numerical representations of the training/test data, weights and gradients without a general loss in accuracy. The benefit of such compact representations is twofold: they allow a significant reduction of the communication bottleneck in distributed DNN training and faster neural network implementations on hardware accelerators like FPGAs. Several quantization methods have been… CONTINUE READING
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