Fast Algorithms for Convolutional Neural Networks

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 2016-December, Issue , 2016, Pages 4013-4021

  Lavin, Andrew ^b   Gray, Scott ^a  

^a Nervana Systems   (United States)

^b NONE

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX NETWORKS; COMPUTER VISION; CONVOLUTION; IMAGE RECOGNITION; NEURAL NETWORKS; PATTERN RECOGNITION;

CONVOLUTIONAL NEURAL NETWORK; FAST ALGORITHMS; FILTERING ALGORITHM; GPU IMPLEMENTATION; LARGE DATASETS; PEDESTRIAN DETECTION; PROCESSING RESOURCES; STATE OF THE ART;

COMPUTATIONAL COMPLEXITY;

EID: 84986325583     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.435     Document Type: Conference Paper

References (17)

1. cuDNN. https://developer.nvidia.com/cudnn. Accessed: 2015-11-01.
2. Richard E Blahut. Fast algorithms for signal processing. Cambridge University Press, 2010.
3. Jason Cong and Bingjun Xiao. Minimizing computation in convolutional neural networks. In Artificial Neural Networks and Machine Learning-ICANN 2014, pages 281-290. Springer, 2014.
4. SA Cook. On the minimum computation time for multiplication. Doctoral diss., Harvard U., Cambridge, Mass, 1966.
5. Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David. Low precision arithmetic for deep learning. CoRR, abs/1412.7024, 2014.
6. Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, and Pritish Narayanan. Deep learning with limited numerical precision. arXiv preprint arXiv:1502.02551, 2015.
7. Suyog Gupta, Wei Zhang, and Josh Milthrope. Model accuracy and runtime tradeoff in distributed deep learning. arXiv preprint arXiv:1509.04210, 2015.
8. Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015.
9. Alex Krizhevsky. One weird trick for parallelizing convolutional neural networks. arXiv preprint arXiv:1404.5997, 2014.
10. V. Madisetti. The Digital Signal Processing Handbook. Number v. 2 in Electrical engineering handbook series. CRC, 2010.
11. Michäel Mathieu, Mikael Henaff, and Yann LeCun. Fast training of convolutional networks through ffts. CoRR, abs/1312.5851, 2013.
12. Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
13. Volker Strassen. Gaussian elimination is not optimal. Numerische Mathematik, 13(4):354-356, 1969.
14. Andrei L Toom. The complexity of a scheme of functional elements realizing the multiplication of integers. In Soviet Mathematics Doklady, volume 3, pages 714-716, 1963.
15. Nicolas Vasilache, Jeff Johnson, Michäel Mathieu, Soumith Chintala, Serkan Piantino, and Yann LeCun. Fast convolutional nets with fbfft: A GPU performance evaluation. CoRR, abs/1412.7580, 2014.
16. ShmuelWinograd. Arithmetic complexity of computations, volume 33. Siam, 1980.
17. Shmuel Winograd. On multiplication of polynomials modulo a polynomial. SIAM Journal on Computing, 9(2):225-229, 1980.