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The 2023.2 Tria Vitis Platforms project adds an external Hailo-8 accelerator to selected PCIe-enabled ZUBoard 1CG and UltraZed-EV designs, then integrates its driver, firmware, runtime and TAPPAS into a PetaLinux build. The tutorial demonstrates PCIe detection, software recognition and a TAPPAS camera pipeline; it is a board-specific integration example, not proof of compatibility with every Tria board or a general performance benchmark.

What the project adds

In this installment of Mario Bergeron’s Tria Vitis Platforms series, an external Hailo-8 accelerator complements the programmable-logic DPU covered in earlier parts. The aim is to enable custom AI applications on Tria development boards. The project uses the 2023.2 repository branch and describes three stages: confirming PCIe enumeration, checking that the Hailo software stack recognizes the device, and running an application through TAPPAS.

The series includes ZUBoard, Ultra96-V2 and UltraZed-7EV, but the Hailo PCIe examples highlighted in this tutorial are for ZUBoard 1CG and UltraZed-EV. The article page displays a publication date of November 18, 2024; its revision history separately lists November 18 and 24, 2023. Those dates are distinct page metadata, not a basis for treating the software instructions as current guidance.

Choose the hardware path that matches the board

The two demonstrated configurations use different M.2 modules and PCIe arrangements. Their components should not be assumed interchangeable.

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UltraZed-EV tria-uz7ev-nvme Opsero M.2 Stack FMC with a Hailo-8 M-Key module

These are the combinations described by the tutorial. Check the board design, connector, module keying and carrier requirements for the specific hardware before attempting the setup; a shared accelerator family name does not establish physical fit.

Validate PCIe before integrating software

Begin with the appropriate PCIe-enabled platform design for the board, then inspect the target’s PCIe devices with lspci. The tutorial’s cited output identifies a Hailo-8 coprocessor. This first check establishes that the host can enumerate the device; it does not by itself confirm that the Hailo driver and runtime are installed or that an AI application can run.

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Integrate the Hailo software stack into PetaLinux

The project adds recipes for the Hailo driver, firmware and runtime to the PetaLinux build, then layers in TAPPAS for application examples. Its recipe and layer adjustments are tied to a historical combination of Yocto releases, so they are useful as project-specific integration details rather than current vendor-support recommendations.

Driver, firmware and runtime

The author incorporates recipe content into the project using symlinks and adjusts the layer configuration’s compatibility declarations to include Yocto Langdale. These are build-integration changes made for this project; a different layer revision or build environment may require different handling.

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TAPPAS recipes and target adjustment

The cited Mickledore branch did not contain the needed TAPPAS recipes, so the author uses recipes from Kirkstone and likewise extends the layer compatibility declaration for Langdale. A recipe that selects between Hailo-8 and Hailo-15 based on an IMX8 target also needed modification for the boards in this project. The element14 republication notes that examples in the apps directory require modification for Zynq UltraScale+ targets. Taken together, these caveats make this an adaptation project, not a drop-in setup.

Check device recognition and run a TAPPAS example

After building and booting the image, the tutorial shows two software-level checks. In Python, importing hailo_platform prints version 4.19.0. The author also runs blaze_app_python, which exercises Hailo-8-accelerated MediaPipe models through TAPPAS.

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In the shown camera-to-display pipeline log, the author reports an average of 30.74 frames per second and a current rate of 30.61 fps at that point. These are outputs from the demonstrated setup, not a guarantee for other boards, cameras, models or builds. The tutorial does not provide independent reproducibility evidence or a comparison method that would support generalizing the rates.

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What the performance figures do—and do not—show

The tutorial attributes 26 TOPS peak performance to the Hailo-8 acceleration module. That is the module’s stated peak figure in the tutorial, not a measured result from the camera pipeline. The reported 30.74 average fps is an application log result; it should not be treated as a direct conversion of TOPS or as a comparison between the ZUBoard and UltraZed-EV paths. No performance comparison between those hardware configurations is established.

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Who should use this example

This project is most useful to developers working with the named PCIe-capable board configurations who want a reference for connecting an external Hailo-8 and adapting a Yocto/PetaLinux build around it. It demonstrates a route from PCIe enumeration to runtime detection and an application-level TAPPAS run, while also showing where board-specific recipe and example changes arise. It does not establish support for Hailo on every board in the series, current compatibility of the historical layers, or performance outside the author’s reported setup.

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