# FastVA: Deep Learning Video Analytics Through Edge Processing and NPU in Mobile

arXiv: https://arxiv.org/abs/2001.04049 · HTML: https://mlsanity.com/paper/2001.04049/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2020.

## Built on by (most cited first, 6 of 6)

- [On Fast Simulation of Dynamical System with Neural Vector Enhanced Numerical Solver](https://mlsanity.com/paper/2208.03680/) (2022)
- [Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques](https://mlsanity.com/paper/2211.15751/) (2022)
- [A3D: Adaptive, Accurate, and Autonomous Navigation for Edge-Assisted Drones](https://mlsanity.com/paper/2307.09880/) (2023)
- [NAWQ-SR: A Hybrid-Precision NPU Engine for Efficient On-Device Super-Resolution](https://mlsanity.com/paper/2212.09501/) (2022)
- [GuideMe: A Mobile Application based on Global Positioning System and Object Recognition Towards a Smart Tourist Guide](https://mlsanity.com/paper/2105.13426/) (2021)
- [CANS: Communication Limited Camera Network Self-Configuration for Intelligent Industrial Surveillance](https://mlsanity.com/paper/2109.05665/) (2021)

## Abstract (arXiv)

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.