Ms. Xinlu Sun Profile

Xinlu Sun

at HIT Shenzhen Graduate School

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Proceedings Article | 15 March 2019 Paper

Pruning filters with L1-norm and standard deviation for CNN compression

Xinlu Sun, Dianle Zhou, Xiaotian Pan, Zhiwei Zhong, Fei Wang

Proceedings Volume 11041, 110412J (2019) https://doi.org/10.1117/12.2523246

KEYWORDS: Neurons, Convolution, Multilayers, Neural networks, Field programmable gate arrays, Databases, Defense technologies, Machine vision, Computer vision technology, Mobile devices

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Convolution Neural Networks (CNN) have evolved to be the state-of-art technique for machine learning tasks. However, CNNs bring a significant increase in the computation and parameter storage costs, which makes it difficult to deploy on embedded devices with limited hardware resources and a tight power budget. In recent years, people focus on reducing these overheads by compressing the CNN models, such as pruning weights and pruning filters. Compared with the method of pruning weights, the method of pruning filters does not result in sparse connectivity patterns. And it is conducive to the parallel acceleration on hardware platforms. In this paper, we proposed a new method to judge the importance of filters. In order to make the judgement more accurate, we use the standard deviation to represent the amount of information extracted by the filter. In the process of pruning, the unimportant filters can be removed directly without loss in the test accuracy. We also proposed a multilayer pruning method to avoid setting the pruning rate layer by layer. This holistic pruning method can improve the pruning efficiency. In order to verify the effectiveness of our algorithm, we do experiments with simple network VGG16 and complex networks ResNet18/34. We re-trained the pruned CNNs to compensate the accuracy loss caused by the pruning process. The results showed that our pruning method can reduce inference cost by up to 50% for VGG16 and 35% for ResNet18/34 on CIFAR10 with little accuracy loss.

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