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Large number of weights in deep neural networks makes the models difficult to be deployed in low memory environments such as, mobile phones, IOT edge devices as well as "inferencing as a service" environments on cloud.
Pruning algorithms-a survey
Russell Reed · 1993
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann Lecun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Framewise phoneme classification with bidirectional LSTM and other neural network architectures
Alex Graves and Jürgen Schmidhuber · 2005
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Natural language processing (almost) from scratch
Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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Deep learning with COTS HPC systems
Adam Coates, Brody Huval, Tao Wang, David J. Wu, Bryan Catanzaro, and Andrew Y. Ng · 2013
Earlier work this paper cites.
Decaf: A deep convolutional activation feature for generic visual recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell · 2014
Earlier work this paper cites.
Compressing deep convolutional networks using vector quantization
Yunchao Gong, Liu Liu, Ming Yang, and Lubomir D. Bourdev · 2014
Earlier work this paper cites.
Multi-scale orderless pooling of deep convolutional activation features
Yunchao Gong, Liwei Wang, Ruiqi Guo, and Svetlana Lazebnik · 2014
Earlier work this paper cites.
Caffe: Convolutional architecture for fast feature embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross B. Girshick, Sergio Guadarrama, and Trevor Darrell · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Deepface: Closing the gap to human-level performance in face verification
Yaniv Taigman, Ming Yang, Marc’Aurelio Ranzato, and Lior Wolf · 2014
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Matthew D. Zeiler and Rob Fergus · 2014
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https://petewarden.com/2015/04/20/why-gemm-is-at-the-heart-of-deep-learning/
Why gemm is at the heart of deep learning · 2015
Cited alongside, same era.
https://www.nvidia.com/content/tegra/embedded-systems/pdf/jetson_tx1_whitepaper.pdf , 2015
Gpu-based deep learning inference: A performance and power analysis · 2015
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Binarynet: Training deep neural networks with weights and activations constrained to +1 or -1
Matthieu Courbariaux and Yoshua Bengio · 2016
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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 · 2016
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Quantized neural networks: Training neural networks with low precision weights and activations
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2016
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Fengfu Li and Bin Liu · 2016
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Pruning convolutional neural networks for resource efficient transfer learning
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Dario Amodei, Rishita Anubhai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Jingdong Chen, Mike Chrzanowski, Adam Coates, Greg Diamos, Erich Elsen, Jesse Engel, Linxi Fan, Christopher Fougner, Tony Han, Awni Y. Hannun, Billy Jun, Patrick LeGresley, Libby Lin, Sharan Narang, Andrew Y. Ng, Sherjil Ozair, Ryan Prenger, Jonathan Raiman, Sanjeev Satheesh, David Seetapun, Shubho Sengupta, Yi Wang, Zhiqian Wang, Chong Wang, Bo Xiao, Dani Yogatama, Jun Zhan, and Zhenyao Zhu · 2015
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Wenlin Chen, James T. Wilson, Stephen Tyree, Kilian Q. Weinberger, and Yixin Chen · 2015
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Song Han, Huizi Mao, and William J. Dally · 2015
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Learning both weights and connections for efficient neural networks
Song Han, Jeff Pool, John Tran, and William J. Dally · 2015
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Neural networks with few multiplications
Zhouhan Lin, Matthieu Courbariaux, Roland Memisevic, and Yoshua Bengio · 2015
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https://www.tensorflow.org/performance/quantization
How to quantize neural networks with tensorflow
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https://software.intel.com/en-us/mkl
Intel math kernel library
Cited in the paper.
Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 2016
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Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
Shuchang Zhou, Zekun Ni, Xinyu Zhou, He Wen, Yuxin Wu, and Yuheng Zou · 2016
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Chenzhuo Zhu, Song Han, Huizi Mao, and William J. Dally · 2016
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Structured pruning of deep convolutional neural networks
Sajid Anwar, Kyuyeon Hwang, and Wonyong Sung · 2017
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Deepmon: Mobile gpu-based deep learning framework for continuous vision applications
Loc N. Huynh, Youngki Lee, and Rajesh Krishna Balan · 2017
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