Extending OpenVX for model-based design of embedded vision applications

Developing computer vision applications for low-power heterogeneous systems is increasingly gaining interest in the embedded systems community. Even more interesting is the tuning of such embedded software for the target architecture when this is driven by multiple constraints (e.g., performance, peak power, energy consumption). Indeed, developers frequently run into system-level inefficiencies and bottlenecks that can not be quickly addressed by traditional methods. In this context OpenVX has been proposed as the standard platform to develop portable, optimized and power-efficient applications for vision algorithms targeting embedded systems. Nevertheless, adopting OpenVX for rapid prototyping, early algorithm parametrization and validation of complex embedded applications is a very challenging task. This paper presents a methodology to integrate a model-based design environment to OpenVX. The methodology allows applying Matlab/Simulink for the model-based design, parametrization, and validation of computer vision applications. Then, it allows for the automatic synthesis of the application model into an OpenVX description for the hardware and constraints-aware application tuning. Experimental results have been conducted with an application for digital image stabilization developed through Simulink and, then, automatically synthesized into OpenVX-VisionWorks code for an NVIDIA Jetson TX1 board.