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One of the main barriers for deploying neural networks on embedded systems has been large memory and power consumption of existing neural networks.
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M. G. Hluchyj and M. J. Karol · 1991
Earlier work this paper cites.
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C. Farabet, C. Poulet, J. Y. Han, and Y. LeCun · 2009
Earlier work this paper cites.
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S. Williams, A. Waterman, and D. Patterson · 2009
Earlier work this paper cites.
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Earlier work this paper cites.
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A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
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V. Gokhale, J. Jin, A. Dundar, B. Martini, and E. Culurciello · 2014
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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S. Ioffe and C. Szegedy · 2015
Earlier work this paper cites.
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M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi · 2016
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Later among the works it cites.
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Later among the works it cites.
Densely connected convolutional networks
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Later among the works it cites.
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, et al · 2017
Later among the works it cites.
Weighted-entropy-based quantization for deep neural networks
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Later among the works it cites.
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B. Wu, F. Iandola, P. H. Jin, and K. Keutzer · 2016
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C. Zhu, S. Han, H. Mao, and W. J. Dally · 2016
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Darknet: Open source neural networks in c
J. Redmon
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B. Wu, A. Wan, X. Yue, and K. Keutzer · 2017
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Designing energy-efficient convolutional neural networks using energy-aware pruning
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F. Yu, D. Wang, and T. Darrell · 2017
Later among the works it cites.
Integrated model, batch and domain parallelism in training neural networks
A. Gholami, A. Azad, P. Jin, K. Keutzer, and A. Buluc · 2018
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