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Convolutional Neural Networks (CNNs) are computationally intensive, which limits their application on mobile devices.
Arithmetic complexity of computations , volume 33
Shmuel Winograd · 1980
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Optimal brain damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
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Second order derivatives for network pruning: Optimal brain surgeon
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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cuDNN: Efficient primitives for deep learning
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Minimizing computation in convolutional neural networks
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Tensorflow: A system for large-scale machine learning
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Cnvlutin: Ineffectual-neuron-free Deep Neural Network Computing
Jorge Albericio, Patrick Judd, Tayler Hetherington, Tor Aamodt, Natalie Enright Jerger, and Andreas Moshovos · 2016
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A multiplication reduction technique with near-zero approximation for embedded learning in IoT devices
Yuxiang Huan, Yifan Qin, Yantian You, Lirong Zheng, and Zhuo Zou · 2016
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Neural networks with few multiplications
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Karen Simonyan and Andrew Zisserman · 2015
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Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott E. Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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EIE: Efficient inference engine on compressed deep neural network
Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark A. Horowitz, and William J. Dally
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Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
Song Han, Huizi Mao, and William J. Dally
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun
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Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun
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Sheng R. Li, Jongsoo Park, and Ping Tak Peter Tang · 2017
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Efficient sparse-winograd convolutional neural networks
Xingyu Liu, Song Han, Huizi Mao, and William J. Dally · 2017
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