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Accelerating Convolutional Neural Networks for Mobile Applications
2016
Proceedings of the 2016 ACM on Multimedia Conference - MM '16
Convolutional neural networks (CNNs) have achieved remarkable performance in a wide range of computer vision tasks, typically at the cost of massive computational complexity. The low speed of these networks may hinder realtime applications especially when computational resources are limited. In this paper, an efficient and effective approach is proposed to accelerate the test-phase computation of CNNs based on low-rank and group sparse tensor decomposition. Specifically, for each convolutional
doi:10.1145/2964284.2967280
dblp:conf/mm/WangC16
fatcat:pgox4okm6ndrtaidwjgsvisb4q