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Quantized Neural Networks (QNNs) use low bit-width fixed-point numbers for representing weight parameters and activations, and are often used in real-world applications due to their saving of computation resources and reproducibility of results.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Improving the speed of neural networks on cpus
Vincent Vanhoucke, Andrew Senior, and Mark Z Mao · 2011
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Deep neural networks for acoustic modeling in speech recognition
Geoffrey Hinton, Li Deng, Dong Yu, George Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Brian Kingsbury, et al · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
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Exploiting linear structure within convolutional networks for efficient evaluation
Emily L Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, and Rob Fergus · 2014
Earlier work this paper cites.
Speeding up convolutional neural networks with low rank expansions
Max Jaderberg, Andrea Vedaldi, and Andrew Zisserman · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Binaryconnect: Training deep neural networks with binary weights during propagations
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Cited alongside, same era.
Neural networks with few multiplications
Zhouhan Lin, Matthieu Courbariaux, Roland Memisevic, and Yoshua Bengio · 2015
Cited alongside, same era.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Fixed point quantization of deep convolutional networks
Darryl Lin, Sachin Talathi, and Sreekanth Annapureddy · 2016
Cited alongside, same era.
Quantized neural networks: Training neural networks with low precision weights and activations
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2017
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Wrpn: wide reduced-precision networks
Asit Mishra, Eriko Nurvitadhi, Jeffrey J Cook, and Debbie Marr · 2017
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Balanced quantization: An effective and efficient approach to quantized neural networks
Shu-Chang Zhou, Yu-Zhi Wang, He Wen, Qin-Yao He, and Yu-Heng Zou · 2017
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Scalable methods for 8-bit training of neural networks
Ron Banner, Itay Hubara, Elad Hoffer, and Daniel Soudry · 2018
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Norm matters: efficient and accurate normalization schemes in deep networks
Elad Hoffer, Ron Banner, Itay Golan, and Daniel Soudry · 2018
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Xnor-net: Imagenet classification using binary convolutional neural networks
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 2016
Cited alongside, same era.
Instance normalization: The missing ingredient for fast stylization
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2016
Cited alongside, same era.
Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
Shuchang Zhou, Yuxin Wu, Zekun Ni, Xinyu Zhou, He Wen, and Yuheng Zou · 2016
Cited alongside, same era.
Deep learning with low precision by half-wave gaussian quantization
Zhaowei Cai, Xiaodong He, Jian Sun, and Nuno Vasconcelos · 2017
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
Cited alongside, same era.
Song Han, Huizi Mao, and William J Dally
Cited in the paper.
Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally
Cited in the paper.
Ai benchmark: Running deep neural networks on android smartphones
Andrey Ignatov, Radu Timofte, William Chou, Ke Wang, Max Wu, Tim Hartley, and Luc Van Gool · 2018
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Quantizing deep convolutional networks for efficient inference: A whitepaper
Raghuraman Krishnamoorthi · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Training and inference with integers in deep neural networks
Shuang Wu, Guoqi Li, Feng Chen, and Luping Shi · 2018
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Group normalization
Yuxin Wu and Kaiming He · 2018
Later among the works it cites.