OpenVX 1.3 Adds Feature Sets for Optimized AI & Vision Processing

October 22, 2019 Brandon Lewis

The Khronos Group has ratified the OpenVX 1.3 specification, a royalty-free open standard for accelerating machine learning inferencing and computer vision workloads. As part of the announcement, Khronos has also released an open source implementation of OpenVX 1.3 for the Raspberry Pi 3 Model B, code samples, and conformance test suite.

OpenVX 1.3 defines multiple feature sets for common use cases, including:

  • Graph Infrastructure (baseline for other feature sets)
  • Default Vision
  • Enhanced Vision
  • Neural Network Inferencing (including tensor objects)
  • NNEF Kernel import (including tensor objects)
  • Binary Images,
  • Safety Critical (reduced features to enable easier safety certification)

The new specification can be found on the OpenVX registry.

The OpenVX 1.3 implementation for the Raspberry Pi 3 Model B was ported to the Raspbian operating system by MulticoreWare, and uses tiling and chaining techniques to provide memory optimizations. This allows the kernel to leverage the aforementioned instruction sets, merge threads into higher-order operations, and automatically parallelize them across multicore CPUs and GPUs.

Sample OpenVX 1.3 implementations are available on GitHub, while core API specifications, headers, extensions, and documentation can be found at

About the Author

Brandon Lewis

Brandon Lewis, Editor-in-Chief of Embedded Computing Design, is responsible for guiding the property's content strategy, editorial direction, and engineering community engagement, which includes IoT Design, Automotive Embedded Systems, the Power Page, Industrial AI & Machine Learning, and other publications. As an experienced technical journalist, editor, and reporter with an aptitude for identifying key technologies, products, and market trends in the embedded technology sector, he enjoys covering topics that range from development kits and tools to cyber security and technology business models. Brandon received a BA in English Literature from Arizona State University, where he graduated cum laude. He can be reached by email at

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