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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. J. Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems
Original
Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang · 2015
Earlier work this paper cites.
Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding
Original
Song Han, Huizi Mao, and William J. Dally · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Original
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Original
Geoffrey Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Original
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
An analysis of deep neural network models for practical applications
Original
Alfredo Canziani, Adam Paszke, and Eugenio Culurciello · 2016
Earlier work this paper cites.
Binarynet: Training deep neural networks with weights and activations constrained to +1 or -1
Original
Matthieu Courbariaux and Yoshua Bengio · 2016
Earlier work this paper cites.

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In-datacenter performance analysis of a tensor processing unit

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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
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Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc V. Le, Geoffrey E. Hinton, and Jeff Dean · 2017
Later among the works it cites.
Blockdrop: Dynamic inference paths in residual networks
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Zuxuan Wu, Tushar Nagarajan, Abhishek Kumar, Steven Rennie, Larry S. Davis, Kristen Grauman, and Rogério Schmidt Feris · 2017
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Shufflenet: An extremely efficient convolutional neural network for mobile devices
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Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun · 2017
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Dynamic Channel Pruning: Feature Boosting and Suppression
X. Gao, Y. Zhao, L. Dudziak, R. Mullins, and C.-z. Xu · 2018
Closest in time.
Soft filter pruning for accelerating deep convolutional neural networks
Yang He, Guoliang Kang, Xuanyi Dong, Yanwei Fu, and Yi Yang · 2018

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2018

Channel Gating Neural Networks

Hua, Weizhe, Zhou, Yuan, De Sa, Christopher et al.

Understand

This paper introduces channel gating, a dynamic, fine-grained, and hardware-efficient pruning scheme to reduce the computation cost for convolutional neural networks (CNNs).

Channel gating identifies regions in the features that contribute less to the classification result, and skips the computation on a subset of the input channels for these ineffective regions.
Unlike static network pruning, channel gating optimizes CNN inference at run-time by exploiting input-specific characteristics, which allows substantially reducing the compute cost with almost no accuracy loss.
We experimentally show that applying channel gating in state-of-the-art networks achieves 2.7-8.0$\times$ reduction in floating-point operations (FLOPs) and 2.0-4.4$\times$ reduction in off-chip memory accesses with a minimal accuracy loss on CIFAR-10.

Built on

Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. J. Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems
Original
Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang · 2015
Earlier work this paper cites.
Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding
Original
Song Han, Huizi Mao, and William J. Dally · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Original
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Original
Geoffrey Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Original
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
An analysis of deep neural network models for practical applications
Original
Alfredo Canziani, Adam Paszke, and Eugenio Culurciello · 2016
Earlier work this paper cites.
Binarynet: Training deep neural networks with weights and activations constrained to +1 or -1
Original
Matthieu Courbariaux and Yoshua Bengio · 2016
Earlier work this paper cites.

Similar

Xnor-net: Imagenet classification using binary convolutional neural networks
Original
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 2016
Cited alongside, same era.
More is less: A more complicated network with less inference complexity
Original
Xuanyi Dong, Junshi Huang, Yi Yang, and Shuicheng Yan · 2017
Cited alongside, same era.
Channel pruning for accelerating very deep neural networks
Original
Yihui He, Xiangyu Zhang, and Jian Sun · 2017
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Original
Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
Cited alongside, same era.
In-datacenter performance analysis of a tensor processing unit

Then

Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Original
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc V. Le, Geoffrey E. Hinton, and Jeff Dean · 2017
Later among the works it cites.
Blockdrop: Dynamic inference paths in residual networks
Original
Zuxuan Wu, Tushar Nagarajan, Abhishek Kumar, Steven Rennie, Larry S. Davis, Kristen Grauman, and Rogério Schmidt Feris · 2017
Later among the works it cites.
Shufflenet: An extremely efficient convolutional neural network for mobile devices
Original
Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun · 2017
Later among the works it cites.
Dynamic Channel Pruning: Feature Boosting and Suppression
X. Gao, Y. Zhao, L. Dudziak, R. Mullins, and C.-z. Xu · 2018
Closest in time.
Soft filter pruning for accelerating deep convolutional neural networks
Yang He, Guoliang Kang, Xuanyi Dong, Yanwei Fu, and Yi Yang · 2018

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Original

Norman P. Jouppi, Cliff Young, Nishant Patil, David A. Patterson, Gaurav Agrawal, Raminder Bajwa, Sarah Bates, Suresh Bhatia, Nan Boden, Al Borchers, Rick Boyle, Pierre-luc Cantin, Clifford Chao, Chris Clark, Jeremy Coriell, Mike Daley, Matt Dau, Jeffrey Dean, Ben Gelb, Tara Vazir Ghaemmaghami, Rajendra Gottipati, William Gulland, Robert Hagmann, Richard C. Ho, Doug Hogberg, John Hu, Robert Hundt, Dan Hurt, Julian Ibarz, Aaron Jaffey, Alek Jaworski, Alexander Kaplan, Harshit Khaitan, Andy Koch, Naveen Kumar, Steve Lacy, James Laudon, James Law, Diemthu Le, Chris Leary, Zhuyuan Liu, Kyle Lucke, Alan Lundin, Gordon MacKean, Adriana Maggiore, Maire Mahony, Kieran Miller, Rahul Nagarajan, Ravi Narayanaswami, Ray Ni, Kathy Nix, Thomas Norrie, Mark Omernick, Narayana Penukonda, Andy Phelps, Jonathan Ross, Amir Salek, Emad Samadiani, Chris Severn, Gregory Sizikov, Matthew Snelham, Jed Souter, Dan Steinberg, Andy Swing, Mercedes Tan, Gregory Thorson, Bo Tian, Horia Toma, Erick Tuttle, Vijay Vasudevan, Richard Walter, Walter Wang, Eric Wilcox, and Doe Hyun Yoon · 2017

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Learning efficient convolutional networks through network slimming

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Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, and Changshui Zhang · 2017

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Modeling the resource requirements of convolutional neural networks on mobile devices

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Zongqing Lu, Swati Rallapalli, Kevin S. Chan, and Thomas F. La Porta · 2017

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Deciding how to decide: Dynamic routing in artificial neural networks

Mason McGill and Pietro Perona · 2017

Cited alongside, same era.