Acceleration performance study of Convolutional Neural Network based on Split-radix-2/(2a) FFT algorithms

Abstract : Convolution Neural Networks (CNN) make breakthrough progress in many areas recently, such as speech recognition and image recognition. A limiting factor for use of CNN in large-scale application is, until recently, their computational expense, especially the calculation of linear convolution in spatial domain. Convolution theorem provides a very effective way to implement a linear convolution in spatial domain by multiplication in frequency domain. This paper proposes an unified one-dimensional FFT algorithm based on decimation-in-time split-radix-2/(2a), in which a is an arbitrary natural number. The acceleration performance of convolutional neural network is studied by using the proposed FFT algorithm on CPU environment. Experimental results on the MNIST database and Cifar-10 database show great improvement when compared to the direct linear convolution based CNN with no loss in accuracy, and the radix-2/4 FFT gets the best time savings of 38.56% and 72.01% respectively. Therefore, it is a very effective way to realize linear convolution operation in frequency domain. © 2017, Science Press. All right reserved.
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Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology, 2017, 39 (2), pp.285-292. 〈10.11999/JEIT160357〉
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https://hal-univ-rennes1.archives-ouvertes.fr/hal-01618935
Contributeur : Laurent Jonchère <>
Soumis le : mercredi 18 octobre 2017 - 17:15:32
Dernière modification le : mercredi 16 mai 2018 - 11:23:42

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J. Wu, Z. Da, L. Wei, L. Senhadji, H. Shu. Acceleration performance study of Convolutional Neural Network based on Split-radix-2/(2a) FFT algorithms. Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology, 2017, 39 (2), pp.285-292. 〈10.11999/JEIT160357〉. 〈hal-01618935〉

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