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 two problems: Max-Accuracy where the goal is to maximize the accuracy under some time constraints, and Max-Utility where the goal is to maximize the utility which is a weighted function of processing time and accuracy. 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.

中文翻译：

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FastVA：通过边缘处理和移动 NPU 进行深度学习视频分析

已经开发了许多移动应用程序来将深度学习应用于视频分析。虽然这些先进的深度学习模型可以为我们提供更好的结果，但它们也面临着高计算开销的问题，这意味着在移动设备上运行时会有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，它支持深度通过边缘处理和移动中的神经处理单元 (NPU) 学习视频分析。主要挑战是确定何时卸载计算以及何时使用 NPU。基于移动应用程序的处理时间和精度要求，我们研究了两个问题： Max-Accuracy 目标是在某些时间限制下最大化精度，Max-Utility 的目标是最大化效用，这是处理时间和准确性的加权函数。我们将它们表述为整数规划问题并提出基于启发式的解决方案。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。