SD-VBS: The San Diego Vision Benchmark Suite

Authors:
Sravanthi Kota Venkata;Ikkjin Ahn;Donghwan Jeon;Anshuman Gupta;Christopher Louie;Saturnino Garcia;Serge Belongie;Michael Bedford Taylor
Affiliations:
Department of Computer Science and Engineering, University of California, San Diego, USA;Department of Computer Science and Engineering, University of California, San Diego, USA;Department of Computer Science and Engineering, University of California, San Diego, USA;Department of Computer Science and Engineering, University of California, San Diego, USA;Department of Computer Science and Engineering, University of California, San Diego, USA;Department of Computer Science and Engineering, University of California, San Diego, USA;Department of Computer Science and Engineering, University of California, San Diego, USA;Department of Computer Science and Engineering, University of California, San Diego, USA
Venue:
IISWC '09 Proceedings of the 2009 IEEE International Symposium on Workload Characterization (IISWC)
Year:
2009

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Abstract

In the era of multi-core, computer vision has emerged as an exciting application area which promises to continue to drive the demand for both more powerful and more energy efficient processors. Although there is still a long way to go, vision has matured significantly over the last few decades, and the list of applications that are useful to end users continues to grow. The parallelism inherent in vision applications makes them a promising workload for multi-core and many-core processors.