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.
|Title of host publication||Proceedings - 2018 IEEE 12th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||7|
|State||Published - 16 Nov 2018|
|Event||12th IEEE International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2018 - Hanoi, Viet Nam|
Duration: 12 Sep 2018 → 14 Sep 2018
|Name||Proceedings - 2018 IEEE 12th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2018|
|Conference||12th IEEE International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2018|
|Period||12/09/18 → 14/09/18|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT The authors would like to thank Tamara Pearlman for her insightful comments. This work was supported by JCT research grant.
© 2018 IEEE.
- Embedded computer vision
- Visual programming