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Recent research has shown that one can train a neural network with binary weights and activations at train time by augmenting the weights with a high-precision continuous latent variable that accumulates small changes from stochastic gradient descent.
Hyperdimensional computing: An introduction to computing in distributed representation with high-dimensional random vectors
Pentti Kanerva · 2009
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Estimating or propagating gradients through stochastic neurons for conditional computation
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Analyzing the performance of multilayer neural networks for object recognition
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Training deep neural networks with low precision multiplications
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 2014
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Matthew D Zeiler and Rob Fergus · 2014
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Binaryconnect: Training deep neural networks with binary weights during propagations
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 2015
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Deep learning with limited numerical precision
Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, and Pritish Narayanan · 2015
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Reduced-precision strategies for bounded memory in deep neural nets
Patrick Judd, Jorge Albericio, Tayler Hetherington, Tor Aamodt, Natalie Enright Jerger, Raquel Urtasun, and Andreas Moshovos · 2015
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Compression of deep convolutional neural networks for fast and low power mobile applications
Yong-Deok Kim, Eunhyeok Park, Sungjoo Yoo, Taelim Choi, Lu Yang, and Dongjun Shin · 2015
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Neural networks with few multiplications
Zhouhan Lin, Matthieu Courbariaux, Roland Memisevic, and Yoshua Bengio · 2015
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Ternary neural networks for resource-efficient ai applications
Hande Alemdar, Nicholas Caldwell, Vincent Leroy, Adrien Prost-Boucle, and Frédéric Pétrot · 2016
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Convolutional networks for fast, energy-efficient neuromorphic computing
Steven K Esser, Paul A Merolla, John V Arthur, Andrew S Cassidy, Rathinakumar Appuswamy, Alexander Andreopoulos, David J Berg, Jeffrey L McKinstry, Timothy Melano, Davis R Barch, et al · 2016
Fixed point quantization of deep convolutional networks
Darryl Lin, Sachin Talathi, and Sreekanth Annapureddy · 2016
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Deep neural networks are robust to weight binarization and other non-linear distortions
Paul Merolla, Rathinakumar Appuswamy, John Arthur, Steve K Esser, and Dharmendra Modha · 2016
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Neuromorphic deep learning machines
Emre Neftci, Charles Augustine, Somnath Paul, and Georgios Detorakis · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 2016
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Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
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Hardware-oriented approximation of convolutional neural networks
Philipp Gysel, Mohammad Motamedi, and Soheil Ghiasi · 2016
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Quantized neural networks: Training neural networks with low precision weights and activations
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2016
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Minje Kim and Paris Smaragdis · 2016
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Fengfu Li, Bo Zhang, and Bin Liu · 2016
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Song Han, Huizi Mao, and William J Dally
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Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally
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Shuchang Zhou, Yuxin Wu, Zekun Ni, Xinyu Zhou, He Wen, and Yuheng Zou · 2016
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Chenzhuo Zhu, Song Han, Huizi Mao, and William J Dally · 2016
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Yodann: An architecture for ultra-low power binary-weight cnn acceleration
Renzo Andri, Lukas Cavigelli, Davide Rossi, and Luca Benini · 2017
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Deep convolutional neural network inference with floating-point weights and fixed-point activations
Liangzhen Lai, Naveen Suda, and Vikas Chandra · 2017
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Incremental network quantization: Towards lossless cnns with low-precision weights
Aojun Zhou, Anbang Yao, Yiwen Guo, Lin Xu, and Yurong Chen · 2017
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