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Training of large-scale deep neural networks is often constrained by the available computational resources.
Why systolic architectures?
Kung, H.T · 1982
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An artificial neural network accelerator using general purpose 24 bit floating point digital signal processors
Iwata, Akira, Yoshida, Yukio, Matsuda, Satoshi, Sato, Yukimasa, and Suzumura, Nobuo · 1989
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A vlsi architecture for high-performance, low-cost, on-chip learning
Hammerstrom, Dan · 1990
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Probabilistic rounding in neural network learning with limited precision
Höhfeld, Markus and Fahlman, Scott E · 1992
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Finite precision error analysis of neural network hardware implementations
Holt, JL and Hwang, Jenq-Neng · 1993
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Enhanced mlp performance and fault tolerance resulting from synaptic weight noise during training
Murray, Alan F and Edwards, Peter J · 1994
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Training with noise is equivalent to tikhonov regularization
Bishop, Chris M · 1995
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Gradient-based learning applied to document recognition
LeCun, Yann, Bottou, Léon, Bengio, Yoshua, and Haffner, Patrick · 1998
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The tradeoffs of large scale learning
Bottou, Léon and Bousquet, Olivier · 2007
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Accelerating scientific computations with mixed precision algorithms
Baboulin, Marc, Buttari, Alfredo, Dongarra, Jack, Kurzak, Jakub, Langou, Julie, Langou, Julien, Luszczek, Piotr, and Tomov, Stanimire · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, Alex and Hinton, Geoffrey · 2009
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Neuflow: A runtime reconfigurable dataflow processor for vision
Farabet, Clément, Martini, Berin, Corda, Benoit, Akselrod, Polina, Culurciello, Eugenio, and LeCun, Yann · 2011
Cited alongside, same era.
Hogwild: A lock-free approach to parallelizing stochastic gradient descent
Recht, Benjamin, Re, Christopher, Wright, Stephen, and Niu, Feng · 2011
Cited alongside, same era.
Improving the speed of neural networks on cpus
Vanhoucke, Vincent, Senior, Andrew, and Mao, Mark Z · 2011
Cited alongside, same era.
Large scale distributed deep networks
Dean, Jeffrey, Corrado, Greg, Monga, Rajat, Chen, Kai, Devin, Matthieu, Mao, Mark, Senior, Andrew, Tucker, Paul, Yang, Ke, Le, Quoc V, et al · 2012
Deep learning with cots hpc systems
Coates, Adam, Huval, Brody, Wang, Tao, Wu, David, Catanzaro, Bryan, and Andrew, Ng · 2013
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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 · 2014
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Project adam: Building an efficient and scalable deep learning training system
Chilimbi, Trishul, Suzue, Yutaka, Apacible, Johnson, and Kalyanaraman, Karthik · 2014
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Low precision arithmetic for deep learning
Courbariaux, Matthieu, Bengio, Yoshua, and David, Jean-Pierre · 2014
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A 240 g-ops/s mobile coprocessor for deep neural networks
Gokhale, Vinayak, Jin, Jonghoon, Dundar, Aysegul, Martini, Berin, and Culurciello, Eugenio · 2014
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Improving neural networks by preventing co-adaptation of feature detectors
Hinton, Geoffrey E, Srivastava, Nitish, Krizhevsky, Alex, Sutskever, Ilya, and Salakhutdinov, Ruslan R · 2012
Cited alongside, same era.
Noise benefits in backpropagation and deep bidirectional pre-training
Audhkhasi, Kartik, Osoba, Osonde, and Kosko, Bart · 2013
Cited alongside, same era.
The MNIST database of handwritten digits
Lecun, Yann and Cortes, Corinna
Cited in the paper.
X1000 real-time phoneme recognition vlsi using feed-forward deep neural networks
Kim, Jonghong, Hwang, Kyuyeon, and Sung, Wonyong · 2014
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A million spiking-neuron integrated circuit with a scalable communication network and interface
Merolla, Paul A, Arthur, John V, Alvarez-Icaza, Rodrigo, Cassidy, Andrew S, Sawada, Jun, Akopyan, Filipp, Jackson, Bryan L, Imam, Nabil, Guo, Chen, Nakamura, Yutaka, et al · 2014
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Deep image: Scaling up image recognition
Wu, Ren, Yan, Shengen, Shan, Yi, Dang, Qingqing, and Sun, Gang · 2015
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