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We propose a novel fine-grained quantization (FGQ) method to ternarize pre-trained full precision models, while also constraining activations to 8 and 4-bits.
Dynamically scaled fixed point arithmetic
Darrell Williamson · 1991
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Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Binarized neural networks
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2016
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Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2016
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Convolutional neural networks using logarithmic data representation
Daisuke Miyashita, Edward H Lee, and Boris Murmann · 2016
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Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 2016
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Marcel Simon, Erik Rodner, and Joachim Denzler · 2016
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Finn: A framework for fast, scalable binarized neural network inference
Yaman Umuroglu, Nicholas J Fraser, Giulio Gambardella, Michaela Blott, Philip Leong, Magnus Jahre, and Kees Vissers · 2016
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Accelerating deep convolutional networks using low-precision and sparsity
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Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
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Chenzhuo Zhu, Song Han, Huizi Mao, and William J Dally · 2016
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Incremental network quantization: Towards lossless cnns with low-precision weights
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