车载边缘计算的嵌入式深度学习

2018 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2018-10-01 DOI:10.1109/SEC.2018.00038

J. Hochstetler, Rahul Padidela, Qi Chen, Qing Yang, Song Fu

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引用次数: 51

摘要

由于深度学习的快速发展，物体识别的准确性得到了很大的提高，但深度学习一般需要大量的训练数据，训练过程非常缓慢和复杂。在这项工作中，使用英特尔Movidius“神经计算棒”和树莓派3模型B来分析实时图像和视频中的物体，用于车辆边缘计算。这项研究的结果告诉我们操纵杆在不同操作系统和处理能力下的表现。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Embedded Deep Learning for Vehicular Edge Computing

The accuracy of object recognition has been greatly improved due to the rapid development of deep learning, but the deep learning generally requires a lot of training data and the training process is very slow and complex. In this work, an Intel Movidius" Neural Compute Stick along with Raspberry Pi 3 Model B is used to analyze the objects in the real time images and videos for vehicular edge computing. The results shown in this study tells how the stick performs in conjunction with different operating systems and processing power.

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2018 IEEE/ACM Symposium on Edge Computing (SEC)

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