Edge AI tools enable the deployment of machine learning models directly on ST devices, such as STM32 microcontrollers (MCUs) and microprocessors (MPU) bringing intelligence directly to the edge.
These tools enable devices to process data locally instead of relying on the cloud, which reduces energy use and lowers the cost of tinyML edge devices.
- Whether you choose a bring your own data (BYOD) or bring your own model (BYOM) approach, we provide:
- User-friendly tools to explore and experiment with edge AI on STM32
- Advanced solutions to deploy AI models in a production environment
ST's software ecosystem simplifies development and helps both embedded engineers and data scientists efficiently integrate edge AI into their solutions.
Software and libraries for STM32 MCUs
NanoEdge AI Studio
Easily create ML libraries for embedded devices using a vast library of prebuilt models (AutoML). Train and adapt your models yourself, without requiring extensive data collection.
Software and libraries for STM32 MPUs
STM32 MPU OpenSTLinux expansion pack
With X-LINUX-AI, integrate AI models seamlessly with a comprehensive framework tailored for developers using OpenSTLinux.
Featured Videos
This new free tool replaces X-CUBE-AI as the desktop solution for preparing, optimizing, and validating AI models running on STM32 MCUs. It brings a unified workflow, improved performance evaluation, and a modern user experience that will benefit both embedded and ML engineers. STM32Cube AI Studio is fully compatible with the STM32Cube ecosystem, allowing a smooth transition from your existing projects.
A comprehensive collection of pre-trained machine learning models optimized for STM32 microcontrollers, complemented by numerous re-training and deployment scripts. Available on GitHub, this collection is ideal for seamlessly integrating AI into STM32-based projects.
Discover in 90 seconds how to seamlessly integrate AI in your STM32 MPU projects: Select, train, optimize and benchmark your AI model with AI tools Create and deploy your application with X-LINUX-AI See your model in action with the STM32 MPU evaluation board