Comparing Two Generations of Embedded GPUs Running a Feature Detection Algorithm

06/13/2018
by   Max Danielsson, et al.
0

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.

READ FULL TEXT
research
02/16/2016

Deep Feature-based Face Detection on Mobile Devices

We propose a deep feature-based face detector for mobile devices to dete...
research
12/14/2011

GPU-based Image Analysis on Mobile Devices

With the rapid advances in mobile technology many mobile devices are cap...
research
03/30/2020

Faster than FAST: GPU-Accelerated Frontend for High-Speed VIO

The recent introduction of powerful embedded graphics processing units (...
research
02/19/2018

PRUNE: Dynamic and Decidable Dataflow for Signal Processing on Heterogeneous Platforms

The majority of contemporary mobile devices and personal computers are b...
research
12/24/2021

Virtuoso: Video-based Intelligence for real-time tuning on SOCs

Efficient and adaptive computer vision systems have been proposed to mak...
research
10/03/2009

Hard Data on Soft Errors: A Large-Scale Assessment of Real-World Error Rates in GPGPU

Graphics processing units (GPUs) are gaining widespread use in computati...

Please sign up or login with your details

Forgot password? Click here to reset