The growth in mobile vision applications, coupled with the performance limitations of mobile platforms, has led to a growing need to understand computer vision applications. Computationally intensive mobile vision applications, such as augmented reality or object recognition, place significant performance and power demands on existing embed- ded platforms, often leading to degraded application quality.
With a better understanding of this growing application space, it should be possible to more effectively optimize future embedded platforms. With that in mind, in this work, we introduce and evaluate a custom benchmark suite for mobile embedded vision applications named MEVBench.
MEVBench provides a wide range of mobile vision applications such as face de- tection, feature classification, object tracking and feature extraction. To better understand mobile vision processing characteristics at the architectural level, we analyze single and multithread implementations of many algorithms as it relates to performance, scalability, and memory characteristics. We provide insights into the major areas where architecture can improve the performance of these applications in embedded systems.
MEVBench is targeted at mobile embedded systems such as the ARM A9 and Intel Atom processors that are common in smartphones and tablets. These devices are gaining in popularity and acquiring more capable cameras and mobile processors.
Mobile embedded systems differ from typical desktop systems in that they are more concerned with size, energy and power constraints. This typically leads to lower computational power along with less memory resources.
MEVBench provides full applications, such as augmented reality, along with components of common vision algorithms such as SIFT feature extraction and SVM classification. The algorithms are built using the OpenCV framework unless otherwise noted.
For the OpenCV benchmarks, we used the framework and some of the functions OpenCV provides, but we assemple the applications together using custom code. Furthermore, we developed a custom framework for multithreading vision bench- marks based on Pthreads.
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