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Deep neural networks (DNNs) although achieving human-level performance in many domains, have very large model size that hinders their broader applications on edge computing devices.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Distributed optimization and statistical learning via the alternating direction method of multipliers
Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, and Jonathan Eckstein · 2011
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Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition
George E Dahl, Dong Yu, Li Deng, and Alex Acero · 2012
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, and Brian Kingsbury · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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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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Binaryconnect: Training deep neural networks with binary weights during propagations
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 2015
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Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Dynamic network surgery for efficient dnns
Yiwen Guo, Anbang Yao, and Yurong Chen · 2016
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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 · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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mememoji
Jostine Ho · 2016
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Binarized neural networks
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2016
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Eye tracking for everyone
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Fixed point quantization of deep convolutional networks
Darryl Lin, Sachin Talathi, and Sreekanth Annapureddy · 2016
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Channel pruning for accelerating very deep neural networks
Yihui He, Xiangyu Zhang, and Jian Sun · 2017
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Extremely low bit neural network: Squeeze the last bit out with admm
Cong Leng, Hao Li, Shenghuo Zhu, and Rong Jin · 2017
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Exploring the regularity of sparse structure in convolutional neural networks
Huizi Mao, Song Han, Jeff Pool, Wenshuo Li, Xingyu Liu, Yu Wang, and William J Dally · 2017
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Weighted-entropy-based quantization for deep neural networks
Eunhyeok Park, Junwhan Ahn, and Sungjoo Yoo · 2017
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Soft weight-sharing for neural network compression
Karen Ullrich, Edward Meeds, and Max Welling · 2017
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Xnor-net: Imagenet classification using binary convolutional neural networks
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Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2016
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Quantized convolutional neural networks for mobile devices
Jiaxiang Wu, Cong Leng, Yuhang Wang, Qinghao Hu, and Jian Cheng · 2016
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Nest: a neural network synthesis tool based on a grow-and-prune paradigm
Xiaoliang Dai, Hongxu Yin, and Niraj K Jha · 2017
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Learning to prune deep neural networks via layer-wise optimal brain surgeon
Xin Dong, Shangyu Chen, and Sinno Pan · 2017
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Learning to share: Simultaneous parameter tying and sparsification in deep learning
Dejiao Zhang, Haozhu Wang, Mario Figueiredo, and Laura Balzano
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Designing energy-efficient convolutional neural networks using energy-aware pruning
Tien-Ju Yang, Yu-Hsin Chen, and Vivienne Sze · 2017
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On compressing deep models by low rank and sparse decomposition
Xiyu Yu, Tongliang Liu, Xinchao Wang, and Dacheng Tao · 2017
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Incremental network quantization: Towards lossless cnns with low-precision weights
Aojun Zhou, Anbang Yao, Yiwen Guo, Lin Xu, and Yurong Chen · 2017
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Trained ternary quantization
Chenzhuo Zhu, Song Han, Huizi Mao, and William J Dally · 2017
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Joint training of low-precision neural network with quantization interval parameters
Sangil Jung, Changyong Son, Seohyung Lee, Jinwoo Son, Youngjun Kwak, Jae-Joon Han, and Changkyu Choi · 2018
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Loom: Exploiting weight and activation precisions to accelerate convolutional neural networks
Sayeh Sharify, Alberto Delmas Lascorz, Kevin Siu, Patrick Judd, and Andreas Moshovos · 2018
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