Artificial intelligence ecosystem for STM32
Be unique. Give your product an Edge
Embedded machine learning can improve many applications in a simple, fast, and cost-effective way.
Predictive maintenance, IoT products, smart buildings, asset tracking, people counting... So many applications that could become smarter thanks to the integration of artificial intelligence!
Our comprehensive AI solutions are geared to enable you to embed machine learning capabilities into your products today!
An extensive offer to meet the needs of any project
ST brings you two AI solutions for STM32, so you can find the right fit for your project, regardless of your level of expertise in machine learning.
With NanoEdge AI Studio, you can easily generate libraries for your embedded devices, with millions of prebuilt models available. You do not need to collect and document large and complex data sets. Your model is self-trained on your equipment.
If you already have knowledge on AI, STM32Cube.AI helps you port and optimize artificial neural network models on STM32 microcontrollers.
NanoEdge AI Studio, your machine learning wizard
NanoEdge AI Studio allows you to easily generate libraries for your embedded devices, with millions of prebuilt models available.
Even if you are new to AI, you can create a complete product in just a few days! For example, this tool lets you easily develop predictive maintenance applications thanks to its anomaly detection, classification, or regression algorithms.
|NanoEdgeAIStudio||Automated Machine Learning (ML) tool for STM32 developers|
STM32Cube.AI, your software tool to port and optimize your own artificial neural networks
STM32Cube.AI is part of the STM32Cube ecosystem and supports models from the main AI training frameworks.
If you already have AI design skills and have created your own ANN models, STM32Cube.AI comes with many example applications. You will find, debug tools, and preintegrated libraries to get you started quickly.
Accelerate your development with STM32 function packs
These software packages help you save precious time, allowing you to focus on your artificial neural network models and what makes your application unique.
DOWNLOAD THE FUNCTION PACK YOU NEED:
Combine AI and Linux with our MPU offer
DOWNLOAD LINUX RESOURCES FOR STM32 MPU:
|X-LINUX-AI||STM32 MPU OpenSTLinux Expansion Pack for AI computer vision application|
STM32 platforms to start your development
Our STM32 hardware tools address a wide range of application requirements and will help you develop your next application!
Our embedded machine learning solutions are fully integrated in the STM32 ecosystem. You will benefit from many features, such as advanced graphics, connectivity, sensing and many other capabilities provided by our wide range of STM32 MCUs and MPUs.
FIND THE MOST SUITED STM32 PLATFORM:
|B-L475E-IOT01A||STM32L4 Discovery kit IoT node, low-power wireless, BLE, NFC, SubGHz, Wi-Fi|
|STEVAL-STLKT01V1||SensorTile development kit|
|STEVAL-STWINKT1B||STWIN SensorTile Wireless Industrial Node development kit and reference design for industrial IoT applications|
|STM32L562E-DK||Discovery kit with STM32L562QE MCU|
|STM32H747I-DISCO||Discovery kit with STM32H747XI MCU|
|STM32MP157C-DK2||Discovery kit with STM32MP157C MPU|
|STM32MP157F-DK2||Discovery kit with STM32MP157F MPU|
|Avenger96||STM32MP157A-based Avenger96 board from 96Boards|
|B-CAMS-OMV||Camera module bundle for STM32 boards|
FP-AI-VISION1: Computer Vision application (12:02)
FP-AI-VISION1 is composed of software components generated by the X-CUBE-AI Expansion Package complemented with application software components dedicated to the AI-based computer vision application
AI on STM32 - Person Presence Detection with FP-AI-VISION1 (6:23)
Running on STM32H7 and STM32L4 this demo shows you how wide is the panel of Artificial Intelligence applications you can develop on STM32 thanks to STM32Cube.AI
AI on STM32 : Multiple Object Detection with X-LINUX-AI
Running on STM32MP1 this demo shows you how wide is the panel of Artificial Intelligence applications you can develop on STM32MP1 either on C++ API or Python™ runtime.
Predictive maintenance (07:34)
Condition Monitoring solution for Predictive maintenance is available in FP-AI-NANOEDG1 function pack for STM32Cube. Running on Ultra Low Power STM32 MCUs this code example package allows to quickly create and fine tune condition monitoring application for user equipment.