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With ever-increasing computational demand for deep learning, it is critical to investigate the implications of the numeric representation and precision of DNN model weights and activations on computational efficiency.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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Large scale distributed deep networks
Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Andrew Senior, Paul Tucker, Ke Yang, Quoc V Le, et al · 2012
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Imagenet classification with deep convolutional neural networks
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Learning hierarchical features for scene labeling
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Dadiannao: A machine-learning supercomputer
Yunji Chen, Tao Luo, Shaoli Liu, Shijin Zhang, Liqiang He, Jia Wang, Ling Li, Tianshi Chen, Zhiwei Xu, Ninghui Sun, et al · 2014
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Low precision arithmetic for deep learning
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 2014
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Efficient implementation of stdp rules on spinnaker neuromorphic hardware
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Zidong Du, Krishna Palem, Avinash Lingamneni, Olivier Temam, Yunji Chen, and Chengyong Wu · 2014
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Awni Hannun, Carl Case, Jared Casper, Bryan Catanzaro, Greg Diamos, Erich Elsen, Ryan Prenger, Sanjeev Satheesh, Shubho Sengupta, Adam Coates, et al · 2014
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Caffe: Convolutional architecture for fast feature embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell · 2014
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Very deep convolutional networks for large-scale image recognition
Origami: A convolutional network accelerator
Lukas Cavigelli, David Gschwend, Christoph Mayer, Samuel Willi, Beat Muheim, and Luca Benini · 2015
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Compressing neural networks with the hashing trick
Wenlin Chen, James Wilson, Stephen Tyree, Kilian Q Weinberger, and Yixin Chen · 2015
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A ultra-low-energy convolution engine for fast brain-inspired vision in multicore clusters
Francesco Conti and Luca Benini · 2015
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PerforatedCNNs: Acceleration through elimination of redundant convolutions
Michael Figurnov, Dmitry Vetrov, and Pushmeet Kohli · 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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Karen Simonyan and Andrew Zisserman · 2014
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Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc VV Le · 2014
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Deepface: Closing the gap to human-level performance in face verification
Yaniv Taigman, Ming Yang, Marc’Aurelio Ranzato, and Lars Wolf · 2014
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Lorenz K Muller and Giacomo Indiveri · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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