Fetching the paper…
Reading the bibliography…
We propose DoReFa-Net, a method to train convolutional neural networks that have low bitwidth weights and activations using low bitwidth parameter gradients.
Imagenet: A large-scale hierarchical image database
Deng, Jia, Dong, Wei, Socher, Richard, Li, Li-Jia, Li, Kai, and Fei-Fei, Li · 2009
Earlier work this paper cites.
Large-scale fpga-based convolutional networks
Farabet, Clément, LeCun, Yann, Kavukcuoglu, Koray, Culurciello, Eugenio, Martini, Berin, Akselrod, Polina, and Talay, Selcuk · 2011
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Netzer, Yuval, Wang, Tao, Coates, Adam, Bissacco, Alessandro, Wu, Bo, and Ng, Andrew Y · 2011
Earlier work this paper cites.
Improving the speed of neural networks on cpus
Vanhoucke, Vincent, Senior, Andrew, and Mao, Mark Z · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012
Earlier work this paper cites.
Neuflow: Dataflow vision processing system-on-a-chip
Pham, Phi-Hung, Jelaca, Darko, Farabet, Clement, Martini, Berin, LeCun, Yann, and Culurciello, Eugenio · 2012
Earlier work this paper cites.
Estimating or propagating gradients through stochastic neurons for conditional computation
Bengio, Yoshua, Léonard, Nicholas, and Courville, Aaron · 2013
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Bahdanau, Dzmitry, Cho, Kyunghyun, and Bengio, Yoshua · 2014
Earlier work this paper cites.
Training deep neural networks with low precision multiplications
Courbariaux, Matthieu, Bengio, Yoshua, and David, Jean-Pierre · 2014
Cited alongside, same era.
Compressing deep convolutional networks using vector quantization
Gong, Yunchao, Liu, Liu, Yang, Ming, and Bourdev, Lubomir · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, Diederik and Ba, Jimmy · 2014
Cited alongside, same era.
1-bit stochastic gradient descent and its application to data-parallel distributed training of speech dnns
Seide, Frank, Fu, Hao, Droppo, Jasha, Li, Gang, and Yu, Dong · 2014
Cited alongside, same era.
Tensorflow: Large-scale machine learning on heterogeneous systems, 2015
Abadi, Martın, Agarwal, Ashish, Barham, Paul, Brevdo, Eugene, Chen, Zhifeng, Citro, Craig, Corrado, Greg S, Davis, Andy, Dean, Jeffrey, Devin, Matthieu, et al · 2015
Neural networks with few multiplications
Lin, Zhouhan, Courbariaux, Matthieu, Memisevic, Roland, and Bengio, Yoshua · 2015
Later among the works it cites.
Quantized convolutional neural networks for mobile devices
Wu, Jiaxiang, Leng, Cong, Wang, Yuhang, Hu, Qinghao, and Cheng, Jian · 2015
Later among the works it cites.
Binarynet: Training deep neural networks with weights and activations constrained to+ 1 or-1
Courbariaux, Matthieu and Bengio, Yoshua · 2016
Closest in time.
Kim, Minje and Smaragdis, Paris · 2016
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Deep learning with limited numerical precision
Gupta, Suyog, Agrawal, Ankur, Gopalakrishnan, Kailash, and Narayanan, Pritish · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, Sergey and Szegedy, Christian · 2015
Cited alongside, same era.
Diannao: A small-footprint high-throughput accelerator for ubiquitous machine-learning
Chen, Tianshi, Du, Zidong, Sun, Ninghui, Wang, Jia, Wu, Chengyong, Chen, Yunji, and Temam, Olivier
Cited in the paper.
Dadiannao: A machine-learning supercomputer
Chen, Yunji, Luo, Tao, Liu, Shaoli, Zhang, Shijin, He, Liqiang, Wang, Jia, Li, Ling, Chen, Tianshi, Xu, Zhiwei, Sun, Ninghui, et al
Cited in the paper.
Han, Song, Mao, Huizi, and Dally, William J
Cited in the paper.
Learning both weights and connections for efficient neural network
Han, Song, Pool, Jeff, Tran, John, and Dally, William
Cited in the paper.
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Hinton, Geoffrey, Deng, Li, Yu, Dong, Dahl, George E, Mohamed, Abdel-rahman, Jaitly, Navdeep, Senior, Andrew, Vanhoucke, Vincent, Nguyen, Patrick, Sainath, Tara N, et al
Cited in the paper.
Li, Fengfu and Liu, Bin · 2016
Closest in time.
Deep neural networks are robust to weight binarization and other non-linear distortions
Merolla, Paul, Appuswamy, Rathinakumar, Arthur, John, Esser, Steve K, and Modha, Dharmendra · 2016
Closest in time.
Xnor-net: Imagenet classification using binary convolutional neural networks
Rastegari, Mohammad, Ordonez, Vicente, Redmon, Joseph, and Farhadi, Ali · 2016
Closest in time.