X-LINUX-AI is a free of charge open-source software package dedicated to AI. It is a complete ecosystem that allow developers working with OpenSTLinux to create AI-based application very easily.

• All-in-one AI solutions for the entire STM32MPU serie
• Pre-integrated into Linux distribution based on ST environment
• Include AI frameworks to execute Neural Network models
• Include AI model benchmark application tools for MPU
• Easy application prototyping using Python language and AI frameworks Python API
• C++ API for embedded high-performance applications
• Optimized open-source solutions provided with source codes that allow extensive code reuse and time savings

X-LINUX-AI OpenEmbedded meta layer to be integrated into OpenSTLinux distribution. It contains recipes for AI frameworks, tools and application examples for STM32MPx series

The X-LINUX-AI OpenSTLinux Expansion Package v5.0.0 is compatible with the Yocto Project™ build system Mickledore. It is validated over the OpenSTLinux Distribution v5.0 on STM32MP157F-DK2 with a USB image sensor, on STM32MP157F-EV1 with its built-in camera module, and on STM32MP135F-DK with its built-in camera module

Since its release v5.0.0, the major versioning of the X-LINUX-AI OpenSTLinux Expansion Package is aligned on the major versioning of the OpenSTLinux Distribution. This prevents painful backward compatibility attempts and makes dependencies straightforward. The X-LINUX-AI generic versioning vx.y.z is built as follows:

• x: major version matching the OpenSTLinux Distribution major version. Each new major version is incompatible with previous OpenSTLinux Distribution versions.
• y: minor version, which is changed when new functionalities are added to the X-LINUX-AI OpenSTLinux Expansion Package in a backward compatible manner.
• z: patch version to introduce bug fixes. A patch version is implemented in a backward compatible manner.

X-LINUX-AI v5.0.0 expansion package:

• XNNPACK support for TensorFlow™ Lite and ONNX Runtime, with about 20% to 30% performance gain for quantized networks on a 32-bit system
• TensorFlow™ Lite 2.11.0 with XNNPACK delegate activated
• ONNX Runtime 1.14.0 with XNNPACK execution engine activated
• OpenCV 4.7.x
• Python™ 3.10.x (enabling Pillow module)
• Coral Edge TPU™ accelerator native support
  • libedgetpu 2.0.0 (Gouper) aligned with TensorFlow™ Lite 2.11.0
  • libcoral 2.0.0 (Gouper) aligned with TensorFlow™ Lite 2.11.0
  • PyCoral 2.0.0 (Gouper) aligned with TensorFlow™ Lite 2.11.0
• Support for the OpenSTLinux AI package repository allowing the installation of a prebuilt package using apt-* utilities
• Application samples
  • C++ / Python™ image classification example using TensorFlow™ Lite based on the MobileNet v1 quantized model
  • C++ / Python™ object detection example using TensorFlow™ Lite based on the COCO SSD MobileNet v1 quantized model
  • C++ / Python™ image classification example using Coral Edge TPU™ based on the MobileNet v1 quantized model and compiled for the Edge TPU™
  • C++ / Python™ object detection example using Coral Edge TPU™ based on the COCO SSD MobileNet v1 quantized model and compiled for the Edge TPU™
  • C++ face recognition application using proprietary model capable of recognizing the face of a known (enrolled) user. Contact the local STMicroelectronics support for more information about this application or send a request to edge.ai@st.com
  • Python™ image classification example using ONNX Runtime based on the MobileNet v1 quantized model
  • C++ / Python™ object detection example using ONNX Runtime based on the COCO SSD MobileNet v1 quantized model
• Application support for the 720p, 480p, and 272p display configurations
• X-LINUX-AI SDK add-on extending the OpenSTLinux SDK with AI functionality to develop and build an AI application easily. The X-LINUX-AI SDK add-on provides support for all the above frameworks. It is available from the X-LINUX-AI product page

https://wiki.st.com/stm32mpu/wiki/X-LINUX-AI_Starter_package

https://wiki.st.com/stm32mpu/wiki/X-LINUX-AI_Developer_package

https://wiki.st.com/stm32mpu/wiki/X-LINUX-AI_Distribution_package

https://wiki.st.com/stm32mpu/wiki/Category:AI_-_Application_examples