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http://dx.doi.org/10.6109/jkiice.2020.24.1.141

Convolutional Neural Network Based on Accelerator-Aware Pruning for Object Detection in Single-Shot Multibox Detector  

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.24, no.1, 2020 , pp. 141-144 More about this Journal
Abstract
Convolutional neural networks (CNNs) show high performance in computer vision tasks including object detection, but a lot of weight storage and computation is required. In this paper, a pruning scheme is applied to CNNs for object detection, which can remove much amount of weights with a negligible performance degradation. Contrary to the previous ones, the pruning scheme applied in this paper considers the base accelerator architecture. With the consideration, the pruned CNNs can be efficiently performed on an ASIC or FPGA accelerator. Even with the constrained pruning, the resulting CNN shows a negligible degradation of detection performance, less-than-1% point degradation of mAP on VOD0712 test set. With the proposed scheme, CNNs can be applied to objection dtection efficiently.
Keywords
Convolutional neural networks; Pruning; Object detection; Single-shot multibox detector;
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