Boosting performance of computer vision applications through embedded GPUs on the edge

TL;DR

This paper demonstrates that using embedded GPUs on edge devices significantly improves the performance of resource-intensive computer vision applications, enhancing user experience in mobile augmented reality scenarios.

Contribution

It introduces the use of embedded GPUs on edge devices to boost computer vision application performance, addressing resource limitations in mobile and edge computing environments.

Findings

GPUs provide substantial performance gains over CPUs in edge computing.

Enhanced GPU utilization leads to better user experience in AR applications.

Edge devices with embedded GPUs can handle more demanding computer vision tasks.

Abstract

Computer vision applications, especially those using augmented reality technology, are becoming quite popular in mobile devices. However, this type of application is known as presenting significant demands regarding resources. In order to enable its utilization in devices with more modest resources, edge computing can be used to offload certain high intensive tasks. Still, edge computing is usually composed of devices with limited capacity, which may impact in users quality of experience when using computer vision applications. This work proposes the use of embedded devices with graphics processing units (GPUs) to overcome such limitation. Experiments performed shown that GPUs can attain a performance gain when compared to using only CPUs, which guarantee a better experience to users using such kind of application.