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AEasy, Fast and Energy Efficient Object Detection on Heterogeneous
BibTeX
@MISC{Architectures_aeasy,fast,
author = {On-chip Architectures and Ehsan Totoni},
title = {AEasy, Fast and Energy Efficient Object Detection on Heterogeneous},
year = {}
}
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Abstract
We optimize a visual object detection application (that uses Vision Video Library kernels) and show that OpenCL is a unified programming paradigm that can provide high performance when running on the Ivy Bridge heterogeneous on-chip architecture. We evaluate different mapping techniques and show that run-ning each kernel where it fits the best and using software pipelining can provide 1.91 times higher perfor-mance, and 42 % better energy efficiency. We also show how to trade accuracy for energy at runtime. Overall, our application can perform accurate object detection at 40 frames per second (fps) rate, in an energy effi-cient manner. 1.
