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Deep convolutional neural networks (CNNs) are deployed in various applications but demand immense computational requirements.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Fast training of convolutional networks through ffts
Michael Mathieu, Mikael Henaff, and Yann LeCun · 2013
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Code for kaggle-cifar10 competition. 5th place
Nagadomi · 2014
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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Dynamic network surgery for efficient dnns
Yiwen Guo, Anbang Yao, and Yurong Chen · 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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Fast algorithms for convolutional neural networks
Andrew Lavin and Scott Gray · 2016
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Pruning of winograd and fft based convolution algorithm
Xingyu Liu and Yatish Turakhia · 2016
Cited alongside, same era.
Song Han, Huizi Mao, and William J Dally
Cited in the paper.
Learning structured sparsity in deep neural networks
Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2016
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Enabling sparse winograd convolution by native pruning
Sheng Li, Jongsoo Park, and Ping Tak Peter Tang · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Scalpel: Customizing dnn pruning to the underlying hardware parallelism
Jiecao Yu, Andrew Lukefahr, David Palframan, Ganesh Dasika, Reetuparna Das, and Scott Mahlke · 2017
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Efficient sparse-winograd convolutional neural networks
Xingyu Liu, Jeff Pool, Song Han, and William J Dally · 2018
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Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally
Cited in the paper.
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