Resources
Access our free resources to learn about machine learning.
Find links to useful content for each of our solutions: NanoEdge AI Studio, STM32Cube.AI and X-LINUX-AI.
Find all the function packs that integrate concrete examples to easily start a project.
Introduction to STM32 AI solutions
Overview of Artificial Intelligence solutions for STM32 (NanoEdge AI Studio, STM32Cube.AI)
Add valuable new features to your product with AI on STM32
Intelligence at the Edge [e-book]
A great source of technology, market, and product information for casual learner as well as data scientist
Get started with NanoEdge AI Studio
Discover how to create your data collection and create your first machine learning algorithm in a few clicks
Presentations
[Marketing presentation] NanoEdgeAI solution overview
[Databrief] Automated Machine Learning (ML) tool for STM32 microcontrollers
[Flyer] NanoEdge AI Studio Machine-Learning Software Development for Connected devices and Industrial Equipment Advertisement
Online training
Anyone can build self-learning edge AI devices for STM32
How to easily integrate anomaly detection or predictive maintenance capabilities into your system with NanoEdge AI Studio
NanoEdge AI from datalogging to integration
Blog articles
STEVAL-STWINKT1B: New Components and Application Examples Help Developers Working on Condition Monitoring Applications with AI at the Edge
The Next Automation Age, How New Cyber-Physical Systems Are Making a Positive Difference
NanoEdge AI Studio: 2 New Algorithm Families in 1 Comprehensive AI Solution
License agreement
NanoEdge AI Studio software license agreement
Get started with STM32Cube.AI
Discover how to optimize your AI Neural Network and create processing libraries for your STM32 project
STM32Cube.AI Developer Cloud
Discover how to take advantage of this online platform to optimize, benchmark and create AI processing libraries for your STM32 project.
Presentations
[Marketing presentation] Optimize Neural Networks on STM32 with STM32Cube.AI
[Databrief] Artificial intelligence (AI) software expansion for STM32Cube
Wiki
How to measure machine learning model power consumption with STM32Cube.AI generated application
Blog articles
ST joins MLCommons, why 1 benchmark can help teams adopt machine learning at the edge
STM32Cube.AI v7.3 empowers users to find the perfect balance between inference times and RAM
STM32Cube.AI v7.2, Now With Support for Deeply Quantized Neural Network and Why It Matters
License agreement
STM32Cube.AI software license agreement – SLA0048
Get started with X-LINUX-AI for OpenSTLinux
Learn how to easily integrate Kera and TensorFlow trained AI models into your OpenSTLinux environment
Presentations
[Databrief] X-LINUX-AI: AI Expansion Package for STM32 MPU OpenSTLinux
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.
FP-AI-MONITOR1
STM32Cube function pack for ultra-low power STM32 with artificial intelligence (AI) monitoring application based on a wide range of sensors
#STM32Cube.AI #NanoEdge AI Studio
FP-AI-SENSING1
STM32Cube function pack for ultra-low power IoT node with artificial intelligence (AI) application based on audio and motion sensing
#STM32Cube.AI
FP-AI-VISION1
STM32Cube function pack for ultra-low power IoT node with artificial intelligence (AI) application based on audio and motion sensing
#STM32Cube.AI
FP-AI-FACEREC
Artificial Intelligence (AI) face recognition function pack for STM32Cube
#STM32Cube.AI
FP-AI-CTXAWARE1
STM32Cube function pack for ultra-low power context awareness with distributed artificial intelligence (AI): acoustic analysis with NN on MCU and motion analysis with ML on IMU
#STM32Cube.AI
Wiki – Computer vision applications
Wiki – Sensing applications
