STMicroelectronics has created a hotspot on the GitHub open source repository to host trusted code developed in-house for its STM32 microcontrollers.
The STM32 Hotspot organization on GitHub hosts professionally developed embedded software projects for STM32 microcontrollers. The non-productized code was created by ST’s in-house engineers, originally written for purposes such as exhibition demonstrations and proof-of-concept models.
Code examples like these would typically not be shared outside ST. The projects in STM32 Hotspot can help accelerate development of commonly needed functions and the code is ready to download free of charge and distributed under ST’s usual licensing terms for direct integration in customers’ own applications.
STM32 Hotspot coexists with ST’s main GitHub organization, which provides access to all official STM32 open-source software including the STM32Cube platform and STM32MPU embedded-software distributions. Together, the two communities extend support for users of the over 1200 microcontroller variants in the STM32 family based on ARM Cortex-M embedded cores and the Cortex-A7 MPU core.
Code in the hotspot include the STM32WB-BLE-AI-MotionSense, which combines AI and RF by running a machine learning application on an STM32WB. The system can distinguish between walking, running, or staying put. It then sends the result over Bluetooth to the ST BLE Sensor application on iOS or Android.
The project comes with predefined models that run on the STM32WB5MM-DK board, which uses the ISM330DHCX, an inertial sensor with a machine learning core. The code thus shows how AI at the edge can run powerful models on a device like an STM32WB, thanks to the processing capabilities available on the sensor.
ST’s official GitHub page contains more than 450 repositories, from standard STM32Cube MCU packages for all current STM32 microcontroller series to expansion packages for specific applications.
For instance, ST published four machine learning programs last year on the official GitHub channel. It ensured developers could reduce their time to market when using our sensors and our code for movement recognition.
Similarly, ST released its X-CUBE-AZRTOS on GitHub last April to hasten developments on Azure RTOS. The code serves as implementation examples for features like FileX, ThreadX, or USBX, among others.
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