Supporting Real-Time Computer Vision Workloads Using OpenVX on Multicore+GPU Platforms
Supporting Real-Time Computer Vision Workloads Using OpenVX on Multicore+GPU Platforms
复制标题
在多核 GPU 平台上使用 OpenVX 支持实时计算机视觉工作负载
DOI：
10.1109/rtss.2015.33
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
James H. Anderson
中科院分区：
文献类型：
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作者：
Glenn A. Elliott;Kecheng Yang;James H. Anderson
在汽车行业中，目前有很大的兴趣支持驾驶员辅助和自主控制功能，这些功能通过摄像头利用基于视觉的传感。图形处理单元(GPU)的使用可能使这些特征能够在可接受的大小、重量和功率范围内以经济高效的方式得到支持。OpenVX是支持计算机视觉工作负载的新兴标准。OpenVX使用基于图形的软件体系结构，旨在实现在不同平台上的高效计算，包括那些使用GPU等加速器的平台。遗憾的是，在存在实时约束的环境中，OpenVX的使用会带来一定的挑战。例如，流水线很难支持，处理图可能会有循环。本文提出了一种图变换技术，使这些问题得以规避。此外，还给出了一个应用这些技术的OpenVX实现的案例研究评估。此OpenVX实现运行在先前开发的名为GPUSync的GPU管理框架之上。在此案例研究中，GPUSync的GPU管理技术的使用以及建议的图形转换使使用OpenVX指定的计算机视觉工作负载能够以可预测的方式得到支持。
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.