Edge AI tools enable the deployment of AI / ML models directly on STMicroelectronics edge devices, such as microcontrollers (MCUs), Microprocessors (MPU) and MEMS smart sensors. These tools play a crucial role in bringing intelligence to the edge, allowing devices to process data locally without relying on cloud services. Hence improving the energy and cost effectiveness of tinyML edge devices. Edge AI tools can be either local or cloud based and they are essential for creating efficient and optimized models that can run directly at the edge, on edge optimized devices. Whether you’re a product leader, AI practitioner, or embedded engineer, these tools empower you to build intelligent solutions that enhance products and services.
AI tools enable the deployment of AI / ML models directly on STMicroelectronics edge devices, such as STM32 microcontrollers (MCUs) and microprocessors (MPU). These tools play a crucial role in bringing intelligence to the edge, allowing devices to process data locally without relying on cloud services, thereby improving the energy and cost efficiency of tinyML edge devices.
Whether you opt for bring your own data (BYOD) or bring your own model (BYOM) strategies, we offer solutions to explore the possibilities offered by edge AI on STM32, as well as solutions for deploying AI models in a production environment.
Software and libraries for STM32 MCUs
Effortlessly create ML libraries for embedded devices with access to a vast library of pre-built models (AutoML), enabling self-training without the need for extensive data collection.
Software and libraries for STM32 MPUs
Integrate AI models seamlessly on your STM32 MPU with a comprehensive framework tailored for developers using OpenSTLinux.