Comparing Two Generations of Embedded GPUs Running a Feature Detection Algorithm

TL;DR

This paper compares two generations of embedded GPUs in mobile devices, analyzing their performance, temperature, and architectural differences while running a feature detection algorithm, demonstrating potential for real-time applications.

Contribution

It provides a comparative analysis of embedded GPU architectures on mobile devices for computer vision tasks, highlighting performance and design considerations.

Findings

Performance is approaching real-time levels.

GPUs do not exhibit temperature issues.

Support for large work-groups is crucial.

Abstract

Graphics processing units (GPUs) in embedded mobile platforms are reaching performance levels where they may be useful for computer vision applications. We compare two generations of embedded GPUs for mobile devices when running a state-of-the-art feature detection algorithm, i.e., Harris-Hessian/FREAK. We compare architectural differences, execution time, temperature, and frequency on Sony Xperia Z3 and Sony Xperia XZ mobile devices. Our results indicate that the performance soon is sufficient for real-time feature detection, the GPUs have no temperature problems, and support for large work-groups is important.