Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations

@article{Hubara2017QuantizedNN,
  title={Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations},
  author={Itay Hubara and Matthieu Courbariaux and Daniel Soudry and Ran El-Yaniv and Yoshua Bengio},
  journal={Journal of Machine Learning Research},
  year={2017},
  volume={18},
  pages={187:1-187:30}
}
We introduce a method to train Quantized Neural Networks (QNNs) — neural networks with extremely low precision (e.g., 1-bit) weights and activations, at run-time. At traintime the quantized weights and activations are used for computing the parameter gradients. During the forward pass, QNNs drastically reduce memory size and accesses, and replace most arithmetic operations with bit-wise operations. As a result, power consumption is expected to be drastically reduced. We trained QNNs over the… CONTINUE READING
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Binarized neural networks for language modeling

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  • 2016
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