FastVA：通过边缘处理和移动设备中的NPU进行深度学习视频分析
FastVA: Deep Learning Video Analytics Through Edge Processing and NPU in Mobile
摘要：
已经开发了许多移动应用以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，但它们也遭受了高计算开销，这意味着更长的延迟和更多的能量消耗时，在移动设备上运行。为了解决这个问题，我们提出了一个框架称为FastVA，它支持深度学习视频分析通过边缘处理和神经处理单元（NPU）在移动。主要的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，目标是在一定的时间约束下最大化精度，和Max-Utility，目标是最大化效用，这是处理时间和精度的加权函数。我们制定他们的整数规划问题，并提出基于知识的解决方案。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。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
