Hailo’s Yocto layers allow the user to integrate Hailo’s software into an existing Yocto environment. They include recipes for:
- PCIe driver
- Hailo-8 firmware
- HailoRT GStreamer library implementing the HailoNet element
- HailoRT library
- pyHailoRT - HailoRT Python API (wraps the run-time library)
- Hailo TAPPAS - framework for optimized execution of video-processing pipelines
This layer works with poky.
Please follow the recommended setup procedures of your OE distribution.
We follow the branching naming convention described at https://wiki.yoctoproject.org/wiki/Yocto_Project_Branch_Conventions.
The branches that are currently supported are: Zeus, Dunfell, Honister, Kirkstone.
- For integrating HailoRT to your existing environment - see hailo.ai developer zone documentation (registration is required for full documentation access).
- For integrating TAPPAS to your existing environment - see the documentation in the TAPPAS GitHub.
See hailo.ai developer zone - HailoRT changelog (registration required).
Contact information and support is available at hailo.ai.
Hailo-8 is a deep learning processor for edge devices. The Hailo-8 provides groundbraking efficiency for neural network deployment. The Hailo-8 edge AI processor, featuring up to 26 tera-operations per second (TOPS), significantly outperforms all other edge processors. Hailo-8 is available in various form-factors, including the Hailo-8 M.2 Module.
The Hailo-8 AI processor is designed to fit into a multitude of smart machines and devices, for a wide variety of sectors including Automotive, Smart Cities, Industry 4.0, Retail and Smart Homes.
For more information, please visit hailo.ai.