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It is known that training deep neural networks, in particular, deep convolutional networks, with aggressively reduced numerical precision is challenging.
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G.E · 2012
Earlier work this paper cites.
New types of deep neural network learning for speech recognition and related applications: an overview
Deng, L., G.E., Hinton, and Kingsbury, B · 2013
Earlier work this paper cites.
Low precision arithmetic for deep learning
Courbariaux, M., Bengio, Y., and David, J · 2014
Earlier work this paper cites.
Deep learning with limited numerical precision
Gupta, S., Agrawal, A., Gopalakrishnan, K., and Narayanan, P · 2015
Cited alongside, same era.
A deep neural network compression pipeline: Pruning, quantization, Huffman encoding
Han, S., Mao, H., and Dally, W. J · 2015
Cited alongside, same era.
Neural networks with few multiplications
Lin, Z., Courbariaux, M., Memisevic, R., and Bengio, Y · 2015
Cited alongside, same era.
Hardware-oriented approximation of convolutional neural networks
Gysel, P., Motamedi, M., and Ghiasi, S · 2016
Closest in time.
Fixed point quantization of deep convolutional networks
Lin, D. D., Talathi, S. S., and Annapureddy, V. S · 2016
Closest in time.
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