A Computing Platform for Video Crowdprocessing Using Deep Learning
A Computing Platform for Video Crowdprocessing Using Deep Learning
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DOI：
10.1109/infocom.2018.8486406
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发表时间：
2018-04
期刊：
影响因子：
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通讯作者：
Zongqing Lu;Kevin S. Chan;T. L. Porta
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文献类型：
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作者：
Zongqing Lu;Kevin S. Chan;T. L. Porta
诸如智能电话的移动的设备使得用户能够以越来越高的速率生成和共享视频。在某些情况下，这些视频可能包含有价值的信息，可以用于各种目的。然而，我们考虑众处理视频，而不是集中收集和处理视频用于信息检索，其中每个移动终端本地处理存储的视频。虽然移动的设备的计算能力不断提高，但使用深度学习来处理视频，即，卷积神经网络对于移动的设备来说仍然是一项艰巨的任务。为此，我们设计并构建了CrowdVision，这是一个计算平台，可使移动的设备能够以分布式和节能的方式利用云卸载，使用深度学习对视频进行众包处理。CrowdVision可以在各种设置和不同网络连接下快速有效地处理具有卸载的视频，并且大大优于现有的计算卸载框架（例如，2倍加速）。在这样做的过程中，CrowdVision解决了几个挑战：（i）如何利用深度学习的计算特性进行视频处理;（ii）如何并行处理和卸载加速;以及（iii）如何通过确定正确的卸载时刻来优化运行时的时间和能量。
Mobile devices such as smartphones are enabling users to generate and share videos with increasing rates. In some cases, these videos may contain valuable information, which can be exploited for a variety of purposes. However, instead of centrally collecting and processing videos for information retrieval, we consider crowdprocessing videos, where each mobile device locally processes stored videos. While the computational capability of mobile devices continues to improve, processing videos using deep learning, i.e., convolutional neural networks, is still a demanding task for mobile devices. To this end, we design and build CrowdVision, a computing platform that enables mobile devices to crowdprocess videos using deep learning in a distributed and energy-efficient manner leveraging cloud offload. CrowdVision can quickly and efficiently process videos with offload under various settings and different network connections and greatly outperform the existing computation offload framework (e.g., with a 2× speed-up). In doing so CrowdVision tackles several challenges: (i) how to exploit the characteristics of the computing of deep learning for video processing; (ii) how to parallelize processing and offloading for acceleration; and (iii) how to optimize both time and energy at runtime by just determining the right moments to offload.