Accession Number:

ADA621522

Title:

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

Descriptive Note:

Conference paper preprint

Corporate Author:

NORTH CAROLINA UNIV AT CHAPEL HILL DEPT OF COMPUTER SCIENCE

Report Date:

2015-05-01

Pagination or Media Count:

14.0

Abstract:

In the automotive industry, there is currently great interest in supporting driver-assist and autonomous-control 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 GPUSyncs GPU management techniques along with the proposed graph transformations enabled computer vision workloads specified using OpenVX to be supported in a predictable way.

Subject Categories:

  • Computer Programming and Software
  • Computer Hardware
  • Computer Systems
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE