OpenCL Interoperability with OpenVX Graphs
Ben Ashbaugh, Ariel Bernal · 2017
OpenVX is a computer vision framework that enables embedded and real-time applications to optimize computer vision processing for performance and power. OpenVX addresses system-level optimizations by making use of a graph-based computational API. Although this gives a clear advantage over other traditional computer vision libraries such as OpenCV, which mainly addresses kernel-level optimizations, OpenVX still relies on vendor implementations to optimize individual built-in kernels. OpenVX implements several computer vision kernels but in order to increase adoption and user flexibility, OpenVX added support for C based user-kernels, which by default are single-threaded and there is no particular way to accelerate kernels or offload the computation to an accelerator such us a GPU. The user has to do the heavy lifting of supporting a multi-threaded implementation. We propose two different OpenVX API extensions to allow developers deploy accelerated user-kernels using OpenCL.