Proceedings of the 49th Annual Design Automation Conference
Don't burn your mobile!: safe computational re-sprinting via model predictive control
Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
Accelerating SURF detector on mobile devices
Proceedings of the 20th ACM international conference on Multimedia
Computational sprinting on a hardware/software testbed
Proceedings of the eighteenth international conference on Architectural support for programming languages and operating systems
EVA: an efficient vision architecture for mobile systems
Proceedings of the 2013 International Conference on Compilers, Architectures and Synthesis for Embedded Systems
BCIBench: a benchmarking suite for EEG-based brain computer interface
Proceedings of the 11th Workshop on Optimizations for DSP and Embedded Systems
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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 embedded platforms, often leading to degraded application quality. With a better understanding of this growing application space, it will be possible to more effectively optimize future embedded platforms. 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 detection, 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 to evaluate performance, scalability, and memory characteristics. We provide insights into the major areas where architecture can improve the performance of these applications in embedded systems.