Deep Learning Video Analytics Through Edge Computing and Neural Processing Units on Mobile Devices
Deep Learning Video Analytics Through Edge Computing and Neural Processing Units on Mobile Devices
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DOI：
10.1109/tmc.2021.3105953
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发表时间：
2021-08
影响因子：
7.9
通讯作者：
Tianxiang Tan;G. Cao
中科院分区：
文献类型：
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作者：
Tianxiang Tan;G. Cao
已经开发了许多移动应用程序将深度学习应用于视频分析。尽管这些先进的深度学习模型可以为我们提供更好的结果，但它们也遭受了高度计算的开销，这意味着在移动设备上运行时更长的延迟和更多的能耗。为了解决这个问题，我们提出了一个名为FASTVA的框架，该框架通过移动设备的边缘处理和神经处理单元（NPU）支持深度学习视频分析。主要的挑战是确定何时卸载计算以及何时使用NPU。根据移动应用程序的处理时间和准确性要求，我们研究了三个问题：最大准确性，目的是在某些时间限制下最大化准确性，最大限制是在其中目标是最大化效用，该实用程序是加权功能处理时间和准确性以及最小能量的目标是在一定时间和准确性约束下最小化能量。我们将它们作为整数编程问题提出，并提出基于启发式方法的解决方案。我们已经在智能手机上实施了FASTVA，并通过广泛的评估证明了其有效性。
Many mobile applications have been developed to apply deep learning for video analytics. Although these advanced deep learning models can provide us with better results, they also suffer from the high computational overhead which means longer delay and more energy consumption when running on mobile devices. To address this issue, we propose a framework called FastVA, which supports deep learning video analytics through edge processing and Neural Processing Unit (NPU) in mobile. The major challenge is to determine when to offload the computation and when to use NPU. Based on the processing time and accuracy requirement of the mobile application, we study three problems: Max-Accuracy where the goal is to maximize the accuracy under some time constraints, Max-Utility where the goal is to maximize the utility which is a weighted function of processing time and accuracy, and Min-Energy where the goal is to minimize the energy under some time and accuracy constraints. We formulate them as integer programming problems and propose heuristics based solutions. We have implemented FastVA on smartphones and demonstrated its effectiveness through extensive evaluations.