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http://dx.doi.org/10.6109/jkiice.2019.23.1.55

Conv-XP Pruning of CNN Suitable for Accelerator  

Woo, Yonggeun (School of Computer Science and Engineering, Korea University of Technology and Education)
Kang, Hyeong-Ju (School of Computer Science and Engineering, Korea University of Technology and Education)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.23, no.1, 2019 , pp. 55-62 More about this Journal
Abstract
Convolutional neural networks (CNNs) show high performance in the computer vision, but they require an enormous amount of operations, making them unsuitable for some resource- or energy-starving environments like the embedded environments. To overcome this problem, there have been much research on accelerators or pruning of CNNs. The previous pruning schemes have not considered the architecture of CNN accelerators, so the accelerators for the pruned CNNs have some inefficiency. This paper proposes a new pruning scheme, Conv-XP, which considers the architecture of CNN accelerators. In Conv-XP, the pruning is performed following the 'X' or '+' shape. The Conv-XP scheme induces a simple architecture of the CNN accelerators. The experimental results show that the Conv-XP scheme does not degrade the accuracy of CNNs, and that the accelerator area can be reduced by 12.8%.
Keywords
Neural networks; Convolutional neural networks; Accelerator; Pruning;
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Times Cited By KSCI : 1  (Citation Analysis)
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