Artificial intelligence ecosystem for STM32

Artificial intelligence ecosystem for STM32

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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.

NanoEdgeAI Logo NanoEdgeAI

STM32Cube.AI, your software tool to port and optimize your own artificial neural networks

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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

To simplify application development, we offer code examples around important use cases, such as computer vision, sensing, and condition monitoring. Our function packs are a complete integration of an artificial neural network coupled with pre/post-processing functions and connected to microcontroller peripherals.
These software packages help you save precious time, allowing you to focus on your artificial neural network models and what makes your application unique.


Combine AI and Linux with our MPU offer

If you are looking to combine machine learning functions with the flexibility of Linux on our MPU platforms, you can use our dedicated software library.


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.



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

Getting Started with STM32Cube.AI (13:57)

X-CUBE-AI is an STM32Cube Expansion Package extending STM32CubeMX capabilities with automatic conversion of pre-trained Neural Network and integration of generated optimized library into the user's project.

AI on STM32 : Multiple Object Detection with X-LINUX-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.

AI on STM32 : Face recognition with FP-AI-FACEREC (3:55)

Face recognition software is now available in the new FP-AI-FACEREC1 function pack. Running on STM32H7 MCU and on STM32MP1 MPU