X-LINUX-AI OpenSTLinux Expansion Package - v3.0.0

Applicable for

STM32MP13x lines, STM32MP15x lines

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X-LINUX-AI is an STM32 MPU OpenSTLinux Expansion Package that targets artificial intelligence for STM32MP1 Series devices. It contains Linux AI frameworks, as well as application examples to get started with some basic use cases such as computer vision (CV). It is composed of an OpenEmbedded meta layer, named meta-st-stm32mpu-ai, to be added on top of the STM32MP1 Distribution Package. It brings a complete and coherent easy-to-build / install environment to take advantage of AI on STM32MP1 Series devices.

1. X-LINUX-AI v3.0.0

Information

This version is compatible with Yocto Project® build system Kirkstone and has been validated against the OpenSTLinux ecosystem release v4.1.0 and validated on STM32MP1X boards.

1.1. Contents

• TensorFlow™ Lite 2.11.0
• ONNX Runtime 1.11.0
• 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
• OpenCV 4.5.x
• Python™ 3.10.x (enabling Pillow module)
• Application samples
  • Python image classification using ONNX™ Runtime based on MobileNet v1 quantized model
  • C++ / Python image classification using TensorFlow™ Lite based on MobileNet v1 quantized model
  • C++ / Python object detection using TensorFlow™ Lite based on COCO SSD MobileNet v1 quantized model
  • C++ / Python image classification using Coral Edge TPU™ based on MobileNet v1 quantized model and compiled for the Coral Edge TPU™
  • C++ / Python object detection using Coral Edge TPU™ based on COCO SSD MobileNet v1 quantized model and compiled for the Coral Edge TPU™
  • C++ face recognition using TensorFlow™ Lite models capable of recognizing the face of a known (enrolled) user (available on demand)

Warning

The face recognition binary is available on demand. Please contact the local STMicroelectronics support for more information about this application or send a request to edge.ai@st.com

• Application support for the 720p, 480p, and 272p display configurations
• Python application camera pipeline update to use GtkWaylandsink and achieve better performance
• 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

1.2. Validated hardware

As any software expansion package, the X-LINUX-AI is supported on all STM32MP1 Series and it has been validated on the following boards:

• STM32MP157F-DK2
• STM32MP157F-EV1

1.3. Software structure

2. Starter package: install from the OpenSTLinux AI package repository

Information

The STMicroelectronics packages repository service is provided for evaluation purposes only, its content may be updated at any time without notice and is therefore not approved for use in production.

All the generated X-LINUX-AI packages are available from the OpenSTLinux AI package repository service hosted at the non-browsable URL http://extra.packages.openstlinux.st.com/AI.

This repository contains AI packages that can be simply installed using apt-* utilities, which the same as those used on a Debian system:

• the main group contains the selection of AI packages whose installation is automatically tested by STMicroelectronics
• the updates group is reserved for future uses such as package revision update.

You can install them individually or by package group.

2.1. Prerequisites

• Flash the Starter Package on your SDCard

For OpenSTLinux ecosystem release v4.1.0  :

STM32MP157x-DKx Starter Package procedure

or

STM32MP157x-EV1 Starter Package procedure

• Your board has an internet connection either through the network cable or through a WiFi connection.

Information

If your internet access depends on a proxy server, you should define the http_proxy environment variable with the following command before any apt-* commands: export http_proxy='http://<proxy url>:<proxy port>/'

2.2. Configure the AI OpenSTLinux package repository

Once the board is booted, execute the following command in the console in order to configure the AI OpenSTLinux package repository:

For ecosystem release   v4.1.0  :
 wget http://extra.packages.openstlinux.st.com/AI/4.1/pool/config/a/apt-openstlinux-ai/apt-openstlinux-ai_1.0_armhf.deb
 dpkg -i apt-openstlinux-ai_1.0_armhf.deb 

Then synchronize the AI OpenSTLinux package repository.

 apt-get update

2.3. Install AI packages

Warning

The software package is provided AS IS, and by downloading it, you agree to be bound to the terms of the software license agreement (SLA). The detailed content licenses can be found here.

2.3.1. Install all X-LINUX-AI packages

Command	Description
apt-get install packagegroup-x-linux-ai	Install all the X-LINUX-AI packages (TensorFlow™ Lite, Edge TPU™, application samples and tools)

2.3.2. Install AI framework related packages

Command	Description
apt-get install packagegroup-x-linux-ai-tflite	Install X-LINUX-AI packages related to TensorFlow™ Lite framework (including application samples)
apt-get install packagegroup-x-linux-ai-tflite-edgetpu	Install X-LINUX-AI packages related to the Coral Edge TPU™ framework (including application samples)
apt-get install packagegroup-x-linux-ai-onnxruntime	Install X-LINUX-AI packages related to ONNX Runtime™ (including application samples)

2.3.3. Install individual packages

Command	Description
apt-get install libedgetpu2	Install libedgetpu for Coral Edge TPU™
apt-get install libcoral2	Install libcoral API for Coral Edge TPU™
apt-get install libtensorflow-lite-tools	Install Tensorflow™ Lite utilities
apt-get install libtensorflow-lite2	Install Tensorflow™ Lite runtime
apt-get install python3-libtensorflow-lite	Install Python TensorFlow™ Lite inference engine
apt-get install python3-pycoral	Install Python PyCoral API for Coral Edge TPU™
apt-get install tflite-cv-apps-edgetpu-image-classification-c++	Install C++ image classification example using Coral Edge TPU™ TensorFlow™ Lite API
apt-get install tflite-cv-apps-edgetpu-image-classification-python	Install Python image classification example using Coral Edge TPU™ TensorFlow™ Lite API
apt-get install tflite-cv-apps-edgetpu-object-detection-c++	Install C++ object detection example using Coral Edge TPU™ TensorFlow™ Lite API
apt-get install tflite-cv-apps-edgetpu-object-detection-python	Install Python object detection example using Coral Edge TPU™ TensorFlow™ Lite API
apt-get install tflite-cv-apps-image-classification-c++	Install C++ image classification using TensorFlow™ Lite
apt-get install tflite-cv-apps-image-classification-python	Install Python image classification example using TensorFlow™ Lite
apt-get install tflite-cv-apps-object-detection-c++	Install C++ object detection example using TensorFlow™ Lite
apt-get install tflite-cv-apps-object-detection-python	Install Python object detection example using TensorFlow™ Lite
apt-get install tflite-edgetpu-benchmark	Install benchmark application for Coral Edge TPU™ models
apt-get install tflite-models-coco-ssd-mobilenetv1	Install TensorFlow™ Lite COCO SSD Mobilenetv1 model
apt-get install tflite-models-coco-ssd-mobilenetv1-edgetpu	Install TensorFlow™ Lite COCO SSD Mobilenetv1 model for Coral Edge TPU™
apt-get install tflite-models-mobilenetv1	Install TensorFlow™ Lite Mobilenetv1 model
apt-get install tflite-models-mobilenetv1-edgetpu	Install TensorFlow™ Lite Mobilenetv1 model for Coral Edge TPU™
apt-get install onnxruntime	Install ONNX Runtime™
apt-get install onnxruntime-tools	Install ONNX Runtime™ utilities
apt-get install python3-onnxruntime	Install ONNX Runtime™ python API
apt-get install onnx-models-coco-ssd-mobilenetv1	Install ONNX Runtime™ COCO SSD Mobilenetv1 model
apt-get install onnx-models-mobilenet	Install ONNX Runtime™ Mobilenetv1 model
apt-get install onnx-cv-apps-image-classification-python	Install Python image classification example using ONNX Runtime™

Information

If you need more information about how to use apt-* utilities check the Package repository for OpenSTLinux distribution article.

3. Developer package: build AI application from the X-LINUX-AI add-on SDK

In order to easily develop and build an AI application without using the Yocto build system, X-LINUX-AI comes with a SDK add-on extending the OpenSTLinux SDK with AI functionality.

All the needed information to install and use the X-LINUX-AI SDK add-on can be found in the following article : How to install and use the X-LINUX-AI SDK add-on

4. Distribution package: re-generate X-LINUX-AI OpenSTLinux distribution

With the following procedure, you can re-generate the complete distribution enabling the X-LINUX-AI expansion package.
This procedure is mandatory if you want to update frameworks by yourself, or if you want to modify the application samples.

4.1. Download the STM32MP1 Distribution Package

For ecosystem release v4.1.0 :
Install the STM32MP1 Distribution Package v4.1.0, but do not initialize the OpenEmbedded environment (do not source the envsetup.sh).

4.2. Install X-LINUX-AI environment

• Clone the meta-st-stm32mpu-ai git repositories

Warning

The software package is provided AS IS, and by downloading it, you agree to be bound to the terms of the software license agreement (SLA). The detailed content licenses can be found here.

For X-LINUX-AI v3.0.0:
 cd <Distribution Package installation directory>/layers/meta-st
 git clone https://github.com/STMicroelectronics/meta-st-stm32mpu-ai.git -b v3.0.0

• For a new environment

Source the build environment with the correct board and layer:

 cd ../..
 DISTRO=openstlinux-weston MACHINE=stm32mp1 BSP_DEPENDENCY='layers/meta-st/meta-st-stm32mpu-ai' source layers/meta-st/scripts/envsetup.sh

• For an already installed environment

Add the meta-st-stm32mpu-ai to Yocto environment:

 cd ../..
 source layers/meta-st/scripts/envsetup.sh
 bitbake-layers add-layer ../layers/meta-st/meta-st-stm32mpu-ai/

4.3. Build the image

 bitbake st-image-ai

Information

Note that building the image could take long time depending on the host computer performance.

4.4. Flash the built image

Follow this link to see how to flash the built image.

5. How to use the X-LINUX-AI Expansion Package

5.1. Material needed

To use the X-LINUX-AI OpenSTLinux Expansion Package, choose one of the following materials:

• STM32MP157F-DK2 + an UVC USB WebCam
• STM32MP157F-EV1 with the built in OV5640 parallel camera

Optional:

• Coral USB Edge TPU™ accelerator

5.2. Boot the OpenSTlinux Starter Package

At the end of the boot sequence, the demo launcher application appears on the screen.

5.3. Install the X-LINUX-AI

Warning

The software package is provided AS IS, and by downloading it, you agree to be bound to the terms of the software license agreement (SLA). The detailed content licenses can be found here.

After having configured the AI OpenSTLinux package you can install the X-LINUX-AI components.

 apt-get install packagegroup-x-linux-ai

And restart the demo launcher:

 systemctl restart weston-launch

Check that X-LINUX-AI is properly installed:

 x-linux-ai -v
X-LINUX-AI version: v3.0.0

Information

With the x-linux-ai binary it is possible to check the X-LINUX-AI version and the supported frameworks/applications. Use x-linux-ai --help for more information.

5.4. Launch an AI application sample

Once the demo launcher is restarted, notice that it is slightly different because new AI application samples have been installed.
The demo launcher has the following appearance, and you can navigate into the different screens by using the NEXT or BACK buttons.

The demo launcher now contain AI application samples that are described within dedicated article available in the X-LINUX-AI application samples zoo page.

5.5. Enjoy running your own NN models

The above examples provide application samples to demonstrate how to execute models easily on the STM32MP1.

You are free to update the C/C++ application or Python scripts for your own purposes, using your own NN models.

Source code locations are provided in application sample pages.

Artificial intelligence

Microprocessor unit

Linux^® is a registered trademark of Linus Torvalds.

Computer vision

The Linux Foundation® and Yocto Project® are registered trademarks of the Linux Foundation. Linux® is a registered trademark of Linux Torvalds

Software development kit (A programming package that enables a programmer to develop applications for a specific platform.)

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Application programming interface

Board support package

USB vdeo class

Neural network

Artificial intelligence

Microprocessor unit

Linux^® is a registered trademark of Linus Torvalds.

Computer vision

The Linux Foundation® and Yocto Project® are registered trademarks of the Linux Foundation. Linux® is a registered trademark of Linux Torvalds

Software development kit (A programming package that enables a programmer to develop applications for a specific platform.)

Flash memory shortened to gain space in titles, tables and block diagrams

Application programming interface

Board support package

USB vdeo class

Neural network

Flash memories combine high density and cost effectiveness of EPROMs with the electrical erasability of EEPROMs. For this reason, the Flash memory market is one of the most exciting areas of the semiconductor industry today and new applications requiring in system reprogramming, such as cellular telephones, automotive engine management systems, hard disk drives, PC BIOS software for Plug & Play, digital TV, set top boxes, fax and other modems, PC cards and multimedia CD-ROMs, offer the prospect of very high volume demand.