  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.
