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This series of articles teach you step-by-step, how to deploy an AI model on the embedded platform, and here we take opensource RT-Thread IoTOS as an example.
The development process is based on the STM32H743ZI-Nucleo board and the STM32CubeMX.AI tool is used, as it supports automatically generating various embedded projects (including but not limited to MDK, STM32CubeIDE, etc.) based on a trained AI Model (Keras/TF-Lite only). The tool is easy to get started and is suitable for embedded AI start-up development.
The operating system I use is Ubuntu 18.04. This tutorial also applies to Windows environments, with exactly the same experimental steps. The following development tools are used in this experiment:
The STM32Cube may cause the following errors in the ubuntu environment:
After installation, When running the executable files under the ‘bin’ folder in the terminal, an error is reported: the main class “com.st.app.Main” cannot be found or loaded, so you need to change Ubuntu’s default Open-JDK into Oracle JDK, as shown below:
1# sudo tar zxvf jdk-8u172-linux-x64.tar.gz -C /usr/lib/jvm
2# sudo update-alternatives — install /usr/bin/java java /usr/lib/jvm/jdk1.8.0_172/bin/java 300
3# sudo update-alternatives — config java
4# java -version
Start by cloning the following open source repositories to the local:
In this experiment, I chose the simplest Linear Regression Tensor Flow2 Demo as an example, and the model-related source file specifications are as follows:
contains three different ways to construct the network
tf2_Linear Regressions 'Extended.ipynb
contains different ways to train models
Note that there are 3 ways to build the model:
When you import the AI model into CubeMx, you will encounter the following errors if you use the network model generated in the latter two ways:
1INVALID MODEL: Couldn't load Keras model /home/lebhoryi/RT-Thread/Edge_AI/Project1/keras_model.h5,
2error: Unknown layer: Functional
, and the trained AI Model is saved as Keras format, with a suffix of .h5, such as keras_model.h5.
The sample model I have saved, you can directly download the model for experiments, Here’s the download link
The neural network model structure trained in this example is as follows:
Select the STM32H743ZI Nucleo development board in CubeMX, where there are no restrictions on board models.
in the menu bar, select
, and then select the latest version of the
Embedded Software Packages Manager
plug-in in the
column, and click
in the lower right corner after installation.
plug-in in your project:
The following interface appears:
Next, select the serial port for communication, and here we select serial 3 because this serial port is used as the virtual serial port of the ST link.
Before burning the AI model to the development board, you need to analyze the Model to see if it can be converted to the embedded project properly, and the model we use in this experiment is simple and can be analyzed more quickly, with the results as follows:
Next, we’ll validate the converted embedded project on the board, in which the CubeMX AI tool automatically generates the embedded project based on the AI model you imported. It burns the compiled executable file into the board and verifies the running results through STlink’s virtual serial port. Since Ubuntu does not support MDK, I have to choose to automatically generate the STM32CubeIDE project here.
The validated result is shown as follows:
We just verified the project results in the previous step, now we are going to generate project source code, as shown in the following image:
The generated Project folder tree looks like this:
1(base) #( 07/03/20@10:51 am )( lebhoryi@RT-AI ):~/RT-Thread/Edge_AI@master✗✗✗
2 tree -L 2 ./Project1
4├── DNN # CubeMX Generate Project Path
5│ ├── DNN.ioc # CubeMX file
6│ ├── Drivers
7│ ├── Inc
8│ ├── Middlewares
9│ ├── network_generate_report.txt
10│ ├── Src
11│ ├── Startup
12│ ├── STM32CubeIDE
13│ ├── STM32H743ZITX_FLASH.ld
14│ └── STM32H743ZITX_RAM.ld
15├── image # Related picture save folder
16│ ├── mymodel1.png # model
17│ └── STM32H743.jpg # H743
18├── model # model Save path
19│ └── keras_model.h5
22└── tf2_ Regressions_Extended.ipynb
To get to know STM32CubeIDE, please refer to this link here.
Select the File option →import:
Select the path of the previous export project:
The interface for a successful import looks like this:
We can then use the STM32Cube IDE to debug the generated project.
The corresponding bin file is automatically generated during compilation, which can be burned to the board later by the stm32cubeProgramer tool.
in the upper right corner, then select
and select the
file we want to open.
The dialogue for successful burning:
In the ubuntu system, we can use the serial tool
to view the running results of the final program, as follows:
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