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2018 | Vol. 66, nr 6 | 799--811
Tytuł artykułu

Speeding-up convolutional neural networks: A survey

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Convolutional neural networks (CNN) have become ubiquitous in computer vision as well as several other domains, but the sheer size of the modern CNNs means that for the majority of practical applications, a significant speed up and compression are often required. Speeding-up CNNs therefore have become a very active area of research with multiple diverse research directions pursued by many groups in academia and industry. In this short survey, we cover several research directions for speeding up CNNs that have become popular recently. Specifically, we cover approaches based on tensor decompositions, weight quantization, weight pruning, and teacher-student approaches. We also review CNN architectures designed for optimal speed and briefly consider automatic architecture search.
Wydawca

Rocznik
Strony
799--811
Opis fizyczny
Bibliogr. 85 poz., rys., wykr., tab.
Twórcy
autor
  • Yandex, Moscow, Russia
Bibliografia
  • [1] T. Young, D. Hazarika, S. Poria, and E. Cambria. Recent trends in deep learning based natural language processing. arXiv preprint arXiv:1708.02709, 2017.
  • [2] Seonwoo Min, Byunghan Lee, and Sungroh Yoon. Deep learning in bioinformatics. Briefings in bioinformatics, 2017.
  • [3] Yan Duan, Xi Chen, Rein Houthooft, John Schulman, and Pieter Abbeel. Benchmarking deep reinforcement learning for continuous control. In ICML, 2016.
  • [4] Kunihiko Fukushima and Sei Miyake. Neocognitron: A selforganizing neural network model for a mechanism of visual pattern recognition. Competition and cooperation in neural nets, 1982.
  • [5] Yann LeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel. Backpropagation applied to handwritten zip code recognition. Neural computation, 1989.
  • [6] Rajat Raina, Anand Madhavan, and Andrew Y Ng. Largescale deep unsupervised learning using graphics processors. In ICML, 2009.
  • [7] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012.
  • [8] Jian Cheng, PeisongWang, Gang Li, Qinghao Hu, and Hanqing Lu. Recent advances in efficient computation of deep convolutional neural networks. arXiv preprint arXiv:1802.00939, 2018.
  • [9] Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, and Joel S Emer. Efficient processing of deep neural networks: A tutorial and survey. Proceedings of the IEEE, 2017.
  • [10] Rupesh Kumar Srivastava, Klaus Greff, and Jürgen Schmidhuber. Highway networks. arXiv preprint arXiv:1505.00387, 2015.
  • [11] Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Q Weinberger. Deep networks with stochastic depth. In ECCV, 2016.
  • [12] Michael Figurnov, Maxwell D Collins, Yukun Zhu, Li Zhang, Jonathan Huang, Dmitry Vetrov, and Ruslan Salakhutdinov. Spatially adaptive computation time for residual networks. arXiv preprint, 2017.
  • [13] Tolga Bolukbasi, Joseph Wang, Ofer Dekel, and Venkatesh Saligrama. Adaptive neural networks for efficient inference. In ICML, 2017.
  • [14] Roberto Rigamonti, Amos Sironi, Vincent Lepetit, and Pascal Fua. Learning separable filters. In CVPR, 2013.
  • [15] Max Jaderberg, Andrea Vedaldi, and Andrew Zisserman. Speeding up convolutional neural networks with low rank expansions. In BMVC, 2014.
  • [16] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In CVPR, 2016.
  • [17] Emily Denton, Wojciech Zaremba, Joan Bruna, Yann Le-Cun, and Rob Fergus. Exploiting linear structure within convolutional networks for efficient evaluation. arXiv preprint arXiv:1404.0736, 2014.
  • [18] Vadim Lebedev, Yaroslav Ganin, Maksim Rakhuba, Ivan V. Oseledets, and Victor S. Lempitsky. Speeding-up convolutional neural networks using fine-tuned cp-decomposition. ICLR, 2015.
  • [19] M. Astrid and Seung-Ik Lee. Cp-decomposition with tensor power method for convolutional neural networks compression. In Big Data and Smart Computing, 2017.
  • [20] T. G. Kolda and B. W. Bader. Tensor decompositions and applications. SIAM Rev., 2009.
  • [21] Genevera Allen. Sparse higher-order principal components analysis. In AISTATS, 2012.
  • [22] Jonghoon Jin, Aysegul Dundar, and Eugenio Culurciello. Flattened convolutional neural networks for feedforward acceleration. arXiv preprint arXiv:1412.5474, 2014.
  • [23] Xiangyu Zhang, Jianhua Zou, Kaiming He, and Jian Sun. Accelerating very deep convolutional networks for classification and detection. TPAMI, 2016.
  • [24] Yong-Deok Kim, Eunhyeok Park, Sungjoo Yoo, Taelim Choi, Lu Yang, and Dongjun Shin. Compression of deep convolutional neural networks for fast and low power mobile applications. arXiv preprint arXiv:1511.06530, 2015.
  • [25] PeisongWang and Jian Cheng. Accelerating convolutional neural networks for mobile applications. In ACM Multimedia, 2016.
  • [26] Alexander Novikov, Dmitry Podoprikhin, Anton Osokin, and Dmitry Vetrov. Tensorizing neural networks. In NIPS, 2015.
  • [27] 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. Imagenet large scale visual recognition challenge. IJCV, 2015.
  • [28] Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. Automatic differentiation in pytorch. ICLR, 2017.
  • [29] Min Lin, Qiang Chen, and Shuicheng Yan. Network in network. ICLR, 2014.
  • [30] Forrest N. Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, William J. Dally, and Kurt Keutzer. Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <0.5mb model size. arXiv preprint arXiv:1602.07360, 2016.
  • [31] Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, 2017.
  • [32] Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He. Aggregated residual transformations for deep neural networks. In CVPR, 2017.
  • [33] Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun. Shufflenet: An extremely efficient convolutional neural network for mobile devices. arXiv preprint arXiv:1707.01083, 2017.
  • [34] I. Freeman, L. Roese-Koerner, and A. Kummert. EffNet: An Efficient Structure for Convolutional Neural Networks. arXiv preprint arXiv:1801.06434, 2018.
  • [35] Bichen Wu, Alvin Wan, Xiangyu Yue, Peter Jin, Sicheng Zhao, Noah Golmant, Amir Gholaminejad, Joseph Gonzalez, and Kurt Keutzer. Shift: A zero flop, zero parameter alternative to spatial convolutions. arXiv preprint arXiv:1711.08141, 2017.
  • [36] Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, et al. Going deeper with convolutions. In CVPR, 2015.
  • [37] Christian Szegedy, Sergey Ioffe, and Vincent Vanhoucke. Inception- v4, inception-resnet and the impact of residual connections. AAAI, 2017.
  • [38] François Chollet. Xception: Deep learning with depthwise separable convolutions. CVPR, 2017.
  • [39] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. CVPR, 2016.
  • [40] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In CVPR, 2016.
  • [41] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In CVPR, 2012.
  • [42] Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P Adams, and Nando De Freitas. Taking the human out of the loop: A review of bayesian optimization. Proceedings of the IEEE, 2016.
  • [43] Barret Zoph and Quoc V. Le. Neural architecture search with reinforcement learning. ICLR, 2017.
  • [44] Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V. Le. Learning transferable architectures for scalable image recognition. arXiv preprint arXiv:1707.07012, 2017.
  • [45] H. Pham, M. Y. Guan, B. Zoph, Q. V. Le, and J. Dean. Efficient neural architecture search via parameter sharing. ArXiv, 2018.
  • [46] Han Cai, Tianyao Chen, Weinan Zhang, Yong Yu, and Jun Wang. Reinforcement learning for architecture search by network transformation. arXiv preprint arXiv:1707.04873, 2017.
  • [47] Bowen Baker, Otkrist Gupta, Ramesh Raskar, and Nikhil Naik. Practical neural network performance prediction for early stopping. arXiv preprint arXiv:1705.10823, 2017.
  • [48] Ariel Gordon, Elad Eban, Ofir Nachum, Bo Chen, Tien-Ju Yang, and Edward Choi. Morphnet: Fast & simple resourceconstrained structure learning of deep networks. arXiv preprint arXiv:1711.06798, 2017.
  • [49] Vincent Vanhoucke, Andrew Senior, and Mark Z. Mao. Improving the speed of neural networks on cpus. In Deep Learning and Unsupervised Feature Learning Workshop, NIPS 2011, 2011.
  • [50] Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R. Salakhutdinov. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580, 2012.
  • [51] Yunchao Gong, Liu Liu, Ming Yang, and Lubomir Bourdev. Compressing deep convolutional networks using vector quantization. arXiv preprint arXiv:1412.6115, 2014.
  • [52] Herve Jegou, Matthijs Douze, and Cordelia Schmid. Product quantization for nearest neighbor search. TPAMI, 2011.
  • [53] Sajid Anwar, Kyuyeon Hwang, andWonyong Sung. Fixed point optimization of deep convolutional neural networks for object recognition. In ICASSP, 2015.
  • [54] Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David. Binaryconnect: Training deep neural networks with binary weights during propagations. NIPS, 2015.
  • [55] Zhouhan Lin, Matthieu Courbariaux, Roland Memisevic, and Yoshua Bengio. Neural networks with few multiplications. ICLR, 2016.
  • [56] Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio. Binarized neural networks. NIPS, 2016.
  • [57] Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015.
  • [58] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. ICLR, 2014.
  • [59] Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi. Xnor-net: Imagenet classification using binary convolutional neural networks. In ECCV, 2016.
  • [60] Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio. Quantized neural networks: Training neural networks with low precision weights and activations. arXiv preprint arXiv:1609.07061, 2016.
  • [61] Shuchang Zhou, Yuxin Wu, Zekun Ni, Xinyu Zhou, He Wen, and Yuheng Zou. Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients. arXiv preprint arXiv:1606.06160, 2016.
  • [62] Asit K. Mishra, Eriko Nurvitadhi, Jeffrey J. Cook, and Debbie Marr. WRPN: wide reduced-precision networks. arXiv preprint arXiv:1709.01134, 2017.
  • [63] Hessam Bagherinezhad, Mohammad Rastegari, and Ali Farhadi. Lcnn: Lookup-based convolutional neural network. CVPR, 2017.
  • [64] Yann LeCun, John S. Denker, and Sara A. Solla. Optimal brain damage. In NIPS, 1990.
  • [65] Kumar Chellapilla, Sidd Puri, and Patrice Simard. High performance convolutional neural networks for document processing. In Tenth International Workshop on Frontiers in Handwriting Recognition, 2006.
  • [66] Vadim Lebedev and Victor Lempitsky. Fast convnets using group-wise brain damage. In CVPR, 2016.
  • [67] Baoyuan Liu, Min Wang, Hassan Foroosh, Marshall Tappen, and Marianna Pensky. Sparse convolutional neural networks. In CVPR, 2015.
  • [68] Mikhail Figurnov, Aizhan Ibraimova, Dmitry P Vetrov, and Pushmeet Kohli. Perforatedcnns: Acceleration through elimination of redundant convolutions. In NIPS, 2016.
  • [69] Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li. Learning structured sparsity in deep neural networks. In NIPS, 2016.
  • [70] Richard Shin, Charles Packer, and Dawn Song. Differentiable neural network architecture search. 2018.
  • [71] Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz. Pruning convolutional neural networks for resource efficient transfer learning. arXiv preprint arXiv:1611.06440, 2016.
  • [72] Song Han, Jeff Pool, John Tran, and William Dally. Learning both weights and connections for efficient neural network. In NIPS, 2015.
  • [73] Hengyuan Hu, Rui Peng, Yu-Wing Tai, and Chi-Keung Tang. Network trimming: A data-driven neuron pruning approach towards efficient deep architectures. arXiv preprint arXiv:1607.03250, 2016.
  • [74] Jian-Hao Luo, Jianxin Wu, and Weiyao Lin. Thinet: A filter level pruning method for deep neural network compression. CVPR, 2017.
  • [75] Hanxiao Liu, Karen Simonyan, Oriol Vinyals, Chrisantha Fernando, and Koray Kavukcuoglu. Hierarchical representations for efficient architecture search. arXiv preprint arXiv:1711.00436, 2017.
  • [76] Mark Craven and Jude W Shavlik. Extracting tree-structured representations of trained networks. In NIPS, 1996.
  • [77] Sebastian Thrun. Extracting rules from artificial neural networks with distributed representations. In Advances in neural information processing systems, 1995.
  • [78] Cristian Bucila, Rich Caruana, and Alexandru Niculescu-Mizil. Model compression. In Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006.
  • [79] Xinchuan Zeng and Tony R Martinez. Using a neural network to approximate an ensemble of classifiers. Neural Processing Letters, 2000.
  • [80] Geoffrey E Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in a neural network. NIPS 2014 Deep Learning Workshop, 2014.
  • [81] Jinyu Li, Rui Zhao, Jui-Ting Huang, and Yifan Gong. Learning small-size dnn with output-distribution-based criteria. In Interspeech, 2014.
  • [82] Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio. Fitnets: Hints for thin deep nets. ICLR, 2015.
  • [83] Antonio Polino, Razvan Pascanu, and Dan Alistarh. Model compression via distillation and quantization. ICLR, 2018.
  • [84] Hande Alemdar, Vincent Leroy, Adrien Prost-Boucle, and Frédéric Pétrot. Ternary neural networks for resource-efficient ai applications. In IJCNN, 2017.
  • [85] Nicholas Frosst and Geoffrey Hinton. Distilling a neural network into a soft decision tree. arXiv preprint arXiv:1711.09784, 2017.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
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Identyfikator YADDA
bwmeta1.element.baztech-3221666b-2abf-4665-a3a8-65b2cc86a375
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