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Assuming hardware is the major constraint for enabling real-time mobile intelligence, the industry has mainly dedicated their efforts to developing specialized hardware accelerators for machine learning and inference.
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Shmuel Winograd · 1980
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Model compression
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Automated flower classification over a large number of classes
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Caltech-ucsd birds 200
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Novel dataset for fine-grained image categorization: Stanford dogs
Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao, and Fei-Fei Li · 2011
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Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 2012
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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Do deep nets really need to be deep?
Jimmy Ba and Rich Caruana · 2014
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Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang · 2014
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Caffe: Convolutional architecture for fast feature embedding
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Performance-optimized hierarchical models predict neural responses in higher visual cortex
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Learning deep features for scene recognition using places database
Bolei Zhou, Agata Lapedriza, Jianxiong Xiao, Antonio Torralba, and Aude Oliva · 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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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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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, Alexander C. Berg, and Li Fei-Fei · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks
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Image style transfer using convolutional neural networks
Leon A Gatys, Alexander S Ecker, and Matthias Bethge · 2016
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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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Network trimming: A data-driven neuron pruning approach towards efficient deep architectures
Hengyuan Hu, Rui Peng, Yu-Wing Tai, and Chi-Keung Tang · 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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Let there be color! joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification
Ese: Efficient speech recognition engine with sparse lstm on fpga
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Channel pruning for accelerating very deep neural networks
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 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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Pruning filters for efficient convnets
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Satoshi Iizuka, Edgar Simo-Serra, and Hiroshi Ishikawa · 2016
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The unreasonable effectiveness of noisy data for fine-grained recognition
Jonathan Krause, Benjamin Sapp, Andrew Howard, Howard Zhou, Alexander Toshev, Tom Duerig, James Philbin, and Li Fei-Fei · 2016
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Fast algorithms for convolutional neural networks
Andrew Lavin and Scott Gray · 2016
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Fast convnets using group-wise brain damage
Vadim Lebedev and Victor Lempitsky · 2016
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Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2016
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Fixed point quantization of deep convolutional networks
Darryl Lin, Sachin Talathi, and Sreekanth Annapureddy · 2016
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Pruning convolutional neural networks for resource efficient transfer learning
Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz · 2016
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Thinet: A filter level pruning method for deep neural network compression
Jian-Hao Luo, Jianxin Wu, and Weiyao Lin · 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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Ntire 2017 challenge on single image super-resolution: Methods and results
Radu Timofte, Eirikur Agustsson, Luc Van Gool, Ming-Hsuan Yang, and Lei Zhang · 2017
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Multi-style generative network for real-time transfer
Hang Zhang and Kristin Dana · 2017
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Diversified visual attention networks for fine-grained object classification
Bo Zhao, Xiao Wu, Jiashi Feng, Qiang Peng, and Shuicheng Yan · 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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TVM: An automated end-to-end optimizing compiler for deep learning
Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Haichen Shen, Meghan Cowan, Leyuan Wang, Yuwei Hu, Luis Ceze, et al · 2018
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Adc: Automated deep compression and acceleration with reinforcement learning
Yihui He and Song Han · 2018
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Wide activation for efficient and accurate image super-resolution
Jiahui Yu, Yuchen Fan, Jianchao Yang, Ning Xu, Zhaowen Wang, Xinchao Wang, and Thomas Huang · 2018
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Mnn, 2019
Alibaba · 2019
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Tensorflow lite, 2019
Google · 2019
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Wootz: A compiler-based framework for fast cnn pruning via composability
Hui Guan, Xipeng Shen, and Seung-Hwan Lim · 2019
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Pconv: The missing but desirable sparsity in dnn weight pruning for real-time execution on mobile devices
Xiaolong Ma, Fu-Ming Guo, Wei Niu, Xue Lin, Jian Tang, Kaisheng Ma, Bin Ren, and Yanzhi Wang · 2020
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Patdnn: Achieving real-time dnn execution on mobile devices with pattern-based weight pruning
Wei Niu, Xiaolong Ma, Sheng Lin, Shihao Wang, Xuehai Qian, Xue Lin, Yanzhi Wang, and Bin Ren · 2020
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Priv: A privacy-preserving deep neural network model compression framework
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