Computation-Performance Optimization of Convolutional Neural Networks With Redundant Filter Removal
| Autor: | Shao-Yi Chien, Tung-Wei Lin, Yu Tsao, Chih-Ting Liu, Yi-Heng Wu, Yu-Sheng Lin, Heng Lee |
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| Rok vydání: | 2019 |
| Předmět: |
Computational complexity theory
Contextual image classification Computer science Computation 020208 electrical & electronic engineering ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Convolutional neural network Convolution Hardware and Architecture Convolutional code 0202 electrical engineering electronic engineering information engineering Computer vision algorithms Electrical and Electronic Engineering Algorithm Coding (social sciences) |
| Zdroj: | IEEE Transactions on Circuits and Systems I: Regular Papers. 66:1908-1921 |
| ISSN: | 1558-0806 1549-8328 |
| DOI: | 10.1109/tcsi.2018.2885953 |
| Popis: | Convolutional neural networks (CNNs) are widely employed in modern computer vision algorithms, where the input image is convolved iteratively by many filters to extract the knowledge behind it. However, while the depth of convolutional layers gets deeper and deeper in recent years, the enormous computational complexity makes it difficult to be deployed on embedded systems with limited hardware resources. In this paper, inspired by rate-distortion optimization in image and video coding, we propose a computation-performance optimization (CPO) method to remove the redundant convolution filters in a CNN with performance constraints. To prove the effectiveness of the proposed method, CPO is applied to the networks for image super-resolution and image classification. Under almost the same PSNR drop and accuracy drop for performance evaluation in these two tasks, we can achieve the best parameter and computation reduction when compared with previous works. |
| Databáze: | OpenAIRE |
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