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Low precision operations can provide scalability, memory savings, portability, and energy efficiency.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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Non-asymptotic analysis of stochastic approximation algorithms for machine learning
Moulines, E. and Bach, F. R · 2011
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Accelerating stochastic gradient descent using predictive variance reduction
Johnson, R. and Zhang, T · 2013
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Training deep neural networks with low precision multiplications
Courbariaux, M., Bengio, Y., and David, J.-P · 2014
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M. S., Berg, A. C., and Li, F · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Binaryconnect: Training deep neural networks with binary weights during propagations
Courbariaux, M., Bengio, Y., and David, J.-P · 2015
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Deep learning with limited numerical precision
Gupta, S., Agrawal, A., Gopalakrishnan, K., and Narayanan, P · 2015
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Han, S., Mao, H., and Dally, W. J · 2015
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Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Binarized neural networks
Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., and Bengio, Y · 2016
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On large-batch training for deep learning: Generalization gap and sharp minima
Keskar, N. S., Mudigere, D., Nocedal, J., Smelyanskiy, M., and Tang, P. T. P · 2016
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Convolutional neural networks using logarithmic data representation
Miyashita, D., Lee, E. H., and Murmann, B · 2016
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XNOR-Net: Imagenet classification using binary convolutional neural networks
Rastegari, M., Ordonez, V., Redmon, J., and Farhadi, A · 2016
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DoReFa-Net: Training low bitwidth convolutional neural networks with low bitwidth gradients
Zhou, S., Wu, Y., Ni, Z., Zhou, X., Wen, H., and Zou, Y · 2016
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Mixed low-precision deep learning inference using dynamic fixed point
Mellempudi, N., Kundu, A., Das, D., Mudigere, D., and Kaul, B · 2017
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Automatic differentiation in PyTorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
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Song, Z., Liu, Z., Wang, C., and Wang, D · 2017
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ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning
Zhang, H., Li, J., Kara, K., Alistarh, D., Liu, J., and Zhang, C · 2017
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Scalable methods for 8-bit training of neural networks
Banner, R., Hubara, I., Hoffer, E., and Soudry, D · 2018
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Zhu, C., Han, S., Mao, H., and Dally, W. J · 2016
Cited alongside, same era.
QSGD: Communication-efficient SGD via gradient quantization and encoding
Alistarh, D., Grubic, D., Li, J., Tomioka, R., and Vojnovic, M · 2017
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Incremental network quantization: Towards lossless cnns with low-precision weights
Aojun Zhou, Anbang Yao, Y. G. L. X. Y. C · 2017
Cited alongside, same era.
In-datacenter performance analysis of a tensor processing unit
Jouppi, N. P., Young, C., Patil, N., Patterson, D., Agrawal, G., Bajwa, R., Bates, S., Bhatia, S., Boden, N., Borchers, A., et al · 2017
Cited alongside, same era.
Flexpoint: An adaptive numerical format for efficient training of deep neural networks
Köster, U., Webb, T., Wang, X., Nassar, M., Bansal, A. K., Constable, W., Elibol, O., Gray, S., Hall, S., Hornof, L., et al · 2017
Cited alongside, same era.
Training quantized nets: A deeper understanding
Li, H., De, S., Xu, Z., Studer, C., Samet, H., and Goldstein, T · 2017
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., and Sun, J
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Microsoft unveils Project Brainwave for real-time AI
Burger, D · 2018
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Mixed precision training of convolutional neural networks using integer operations
Das, D., Mellempudi, N., Mudigere, D., Kalamkar, D. D., Avancha, S., Banerjee, K., Sridharan, S., Vaidyanathan, K., Kaul, B., Georganas, E., Heinecke, A., Dubey, P., Corbal, J., Shustrov, N., Dubtsov, R., Fomenko, E., and Pirogov, V. O · 2018
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High-accuracy low-precision training
De Sa, C., Leszczynski, M., Zhang, J., Marzoev, A., Aberger, C. R., Olukotun, K., and Ré, C · 2018
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Training deep neural networks with 8-bit floating point numbers
Wang, N., Choi, J., Brand, D., Chen, C.-Y., and Gopalakrishnan, K · 2018
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Training and inference with integers in deep neural networks
Wu, S., Li, G., Chen, F., and Shi, L · 2018
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