Boosting performance of computer vision applications through embedded GPUs on the edge
This work addresses performance limitations for users of computer vision applications on edge devices, but it is incremental as it applies an existing method (GPU acceleration) to a new context.
The paper tackles the challenge of running resource-intensive computer vision applications on edge devices by proposing the use of embedded GPUs, resulting in a performance gain compared to CPUs that improves user experience.
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.