Code Generation of Graph-Based Vision Processing for Multiple CUDA Cores SoC Jetson TX

Abstract

Embedded vision processing is currently ingrained into many aspects of modern life, from computer-aided surgeries to navigation of unmanned aerial vehicles. Vision processing can be described using coarse-grained data flow graphs, which were standardized by OpenVX to enable both system and kernel level optimization via separation of concerns. Notably, graph-based specification provides a gateway to a code generation engine, which can produce an optimized, hardware-specific code for deployment. Here we provide an algorithm and JAVA-MVC-based implementation of automated code generation engine for OpenVX-based vision applications, tailored to NVIDIA multiple CUDA Cores SoC Jetson TX. Our algorithm pre-processes the graph, translates it into an ordered layer-oriented data model, and produces C code, which is optimized for the Jetson TX1 and comprised of error checking and iterative execution for real time vision processing.