Supporting Real-Time Computer Vision Workloads Using OpenVX on Multicore+GPU Platforms

In the automotive industry, there is currently great interest in supporting driver-assist and autonomouscontrol features that utilize vision-based sensing through cameras. The usage of graphics processing units (GPUs) can potentially enable such features to be supported in a cost-effective way, with...

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Bibliographic Details
Main Authors: Elliott, Glenn A., Kecheng Yang, Anderson, James H.
Format: Conference Proceeding
Language:English
Subjects:
Automotive engineering
Computer vision
Engines
Graphics processing units
Graphs
Kernel
Libraries
Platforms
Real time
Real-time systems
Transformations
Workload
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Summary:In the automotive industry, there is currently great interest in supporting driver-assist and autonomouscontrol features that utilize vision-based sensing through cameras. The usage of graphics processing units (GPUs) can potentially enable such features to be supported in a cost-effective way, within an acceptable size, weight, and power envelope. OpenVX is an emerging standard for supporting computer vision workloads. OpenVX uses a graph-based software architecture designed to enable efficient computation on heterogeneous platforms, including those that use accelerators like GPUs. Unfortunately, in settings where real-time constraints exist, the usage of OpenVX poses certain challenges. For example, pipelining is difficult to support and processing graphs may have cycles. In this paper, graph transformation techniques are presented that enable these issues to be circumvented. Additionally, a case-study evaluation is presented involving an OpenVX implementation in which these techniques are applied. This OpenVX implementation runs atop a previously developed GPU-management framework called GPUSync. In this case study, the usage of GPUSync's GPU management techniques along with the proposed graph transformations enabled computer vision workloads specified using OpenVX to be supported in a predictable way.
ISSN:1052-8725
2576-3172
DOI:10.1109/RTSS.2015.33