Abstract
Realtime image detection and recognition on low-power edge devices are key to implementing many social applications such as automated traffic guard systems. This paper proposes mobile-oriented convolutional neural network implementation for object detection, focusing on computational reduction techniques such as depthwise separable convolution and grouped convolution on an edge GPU device. Through empirical evaluation and analysis, it is shown that the use of grouped convolution and half-precision floating point arithmetic is effective for the calculation of \(3 \times 3\) convolution, and the grouped convolution with 8 groups for YOLOv3-tiny achieves 16 FPS with 86% detection accuracy in a 5 W low power mode on Jetson Nano. We also discuss how the processing time and recognition accuracy are affected by the floating arithmetic types and power consumption modes that the GPU device offers.
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Araki, Y., Kawazu, T., Manabe, T., Ishizuka, Y., Shibata, Y. (2023). A Mobile-Oriented GPU Implementation of a Convolutional Neural Network for Object Detection. In: Barolli, L. (eds) Complex, Intelligent and Software Intensive Systems. CISIS 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 176. Springer, Cham. https://doi.org/10.1007/978-3-031-35734-3_15
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DOI: https://doi.org/10.1007/978-3-031-35734-3_15
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