Fetching the paper…
Reading the bibliography…
For computer vision applications, prior works have shown the efficacy of reducing numeric precision of model parameters (network weights) in deep neural networks.
Improving the speed of neural networks on cpus
V. Vanhoucke, A. Senior, and M. Z. Mao · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Estimating or propagating gradients through stochastic neurons for conditional computation
Y. Bengio, N. Léonard, and A. C. Courville · 2013
Earlier work this paper cites.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. E. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2014
Earlier work this paper cites.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng · 2015
Earlier work this paper cites.
Binaryconnect: Training deep neural networks with binary weights during propagations
M. Courbariaux, Y. Bengio, and J. David · 2015
Earlier work this paper cites.
Deep learning with limited numerical precision
S. Gupta, A. Agrawal, K. Gopalakrishnan, and P. Narayanan · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Earlier work this paper cites.
Neural networks with few multiplications
Z. Lin, M. Courbariaux, R. Memisevic, and Y. Bengio · 2015
Cited alongside, same era.
Resiliency of deep neural networks under quantization
W. Sung, S. Shin, and K. Hwang · 2015
Cited alongside, same era.
Binarynet: Training deep neural networks with weights and activations constrained to +1 or -1
M. Courbariaux and Y. Bengio · 2016
Cited alongside, same era.
F. Li and B. Liu · 2016
Cited alongside, same era.
Convolutional neural networks using logarithmic data representation
S. Zagoruyko and N. Komodakis · 2016
Later among the works it cites.
Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
S. Zhou, Z. Ni, X. Zhou, H. Wen, Y. Wu, and Y. Zou · 2016
Later among the works it cites.
C. Zhu, S. Han, H. Mao, and W. J. Dally · 2016
Later among the works it cites.
Low-precision batch-normalized activations
B. Graham · 2017
Closest in time.
In-Datacenter Performance Analysis of a Tensor Processing Unit
N. P. Jouppi, C. Young, N. Patil, D. Patterson, G. Agrawal, R. Bajwa, S. Bates, S. Bhatia, N. Boden, A. Borchers, R. Boyle, P.-l. Cantin, C. Chao, C. Clark, J. Coriell, M. Daley, M. Dau, J. Dean, B. Gelb, T. Vazir Ghaemmaghami, R. Gottipati, W. Gulland, R. Hagmann, C. R. Ho, D. Hogberg, J. Hu, R. Hundt, D. Hurt, J. Ibarz, A. Jaffey, A. Jaworski, A. Kaplan, H. Khaitan, A. Koch, N. Kumar, S. Lacy, J. Laudon, J. Law, D. Le, C. Leary, Z. Liu, K. Lucke, A. Lundin, G. MacKean, A. Maggiore, M. Mahony, K. Miller, R. Nagarajan, R. Narayanaswami, R. Ni, K. Nix, T. Norrie, M. Omernick, N. Penukonda, A. Phelps, J. Ross, M. Ross, A. Salek, E. Samadiani, C. Severn, G. Sizikov, M. Snelham, J. Souter, D. Steinberg, A. Swing, M. Tan, G. Thorson, B. Tian, H. Toma, E. Tuttle, V. Vasudevan, R. Walter, W. Wang, E. Wilcox, and D. H. Yoon · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
D. Miyashita, E. H. Lee, and B. Murmann · 2016
Cited alongside, same era.
Xnor-net: Imagenet classification using binary convolutional neural networks
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi · 2016
Cited alongside, same era.
Inception-v4, inception-resnet and the impact of residual connections on learning
C. Szegedy, S. Ioffe, and V. Vanhoucke · 2016
Cited alongside, same era.
FINN: A framework for fast, scalable binarized neural network inference
Y. Umuroglu, N. J. Fraser, G. Gambardella, M. Blott, P. H. W. Leong, M. Jahre, and K. A. Vissers · 2016
Cited alongside, same era.
Accelerating deep convolutional networks using low-precision and sparsity
G. Venkatesh, E. Nurvitadhi, and D. Marr · 2016
Cited alongside, same era.
https://github.com/ppwwyyxx/tensorpack
Cited in the paper.
Closest in time.
Ternary Neural Networks with Fine-Grained Quantization
N. Mellempudi, A. Kundu, D. Mudigere, D. Das, B. Kaul, and P. Dubey · 2017
Closest in time.
Can fpgas beat gpus in accelerating next-generation deep neural networks?
E. Nurvitadhi, G. Venkatesh, J. Sim, D. Marr, R. Huang, J. Ong Gee Hock, Y. T. Liew, K. Srivatsan, D. Moss, S. Subhaschandra, and G. Boudoukh · 2017
Closest in time.
Incremental network quantization: Towards lossless cnns with low-precision weights
A. Zhou, A. Yao, Y. Guo, L. Xu, and Y. Chen · 2017
Closest in time.