SDAA422 June   2026 TDA4VE-Q1

 

  1.   1
  2.   Abstract
  3.   Trademarks
  4. 1Introduction
  5. 2Understanding OpenVX Kernel Architecture
    1. 2.1 OpenVX Node Execution Flow
    2. 2.2 Host and Target Kernels
    3. 2.3 A72 Target Execution Model
  6. 3Creating a Custom Kernel Package
    1. 3.1 Package Structure
    2. 3.2 Defining the Kernel Interface
  7. 4Developing a Custom OpenVX Node for the A72 Core: A Threshold Example
    1. 4.1 Implementing the Host Kernel
    2. 4.2 Implementing the A72 Target Kernel
    3. 4.3 Accessing Image Buffers on the A72 Core
    4. 4.4 Implementing the Threshold Algorithm
    5. 4.5 Registering the Kernel Package
  8. 5Integrating the Custom Node into vision_apps
    1. 5.1 Implementing the app_custom_threshold Example
    2. 5.2 Example Execution Log
  9. 6Summary
  10. 7References

Abstract

Texas Instruments Processor SDK RTOS provides the TIOVX and vision_apps frameworks for developing heterogeneous image processing and AI pipelines on TDA4x devices. While the SDK includes a comprehensive set of standard OpenVX kernels, many applications require custom processing functions that are not supported by the built-in kernel library. This application note describes the complete workflow for developing and integrating a custom OpenVX node that executes on an Arm Cortex-A72 core, including kernel interface definition, Host and Target Kernel implementation, kernel registration, build integration, and graph execution within the vision_apps framework. A grayscale threshold operation is used as a reference implementation to illustrate parameter validation, shared-memory buffer access, and runtime processing. Although a simple threshold algorithm is used for demonstration purposes, the methodology presented in this document can be readily applied to application-specific image processing, sensor processing, and AI pre-processing or post-processing functions.