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Algorithms 2018, 11(10), 159; https://doi.org/10.3390/a11100159

A Faster Algorithm for Reducing the Computational Complexity of Convolutional Neural Networks

1,2,3,* , 1,2
,
1,2
and
1,2,3
1
Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
2
Key Laboratory of Information Technology for Autonomous Underwater Vehicles, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Received: 10 September 2018 / Revised: 4 October 2018 / Accepted: 16 October 2018 / Published: 18 October 2018
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Abstract

Convolutional neural networks have achieved remarkable improvements in image and video recognition but incur a heavy computational burden. To reduce the computational complexity of a convolutional neural network, this paper proposes an algorithm based on the Winograd minimal filtering algorithm and Strassen algorithm. Theoretical assessments of the proposed algorithm show that it can dramatically reduce computational complexity. Furthermore, the Visual Geometry Group (VGG) network is employed to evaluate the algorithm in practice. The results show that the proposed algorithm can provide the optimal performance by combining the savings of these two algorithms. It saves 75% of the runtime compared with the conventional algorithm. View Full-Text
Keywords: convolutional neural network; Winograd; minimal filtering; Strassen; fast; complexity convolutional neural network; Winograd; minimal filtering; Strassen; fast; complexity
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Zhao, Y.; Wang, D.; Wang, L.; Liu, P. A Faster Algorithm for Reducing the Computational Complexity of Convolutional Neural Networks. Algorithms 2018, 11, 159.

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