ST Edge AI Suite

Dive into the edge AI world

Find out more about the tools that enable edge AI in your embedded project.

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Explore our edge AI lab

Discover inspiring application examples leveraging the power of edge AI and STM32 microcontrollers and microprocessors.

Generate optimized ML libraries

Find and configure the best edge AI library in a few steps, based on minimal amount of data.

Program and evaluate MEMS sensors

Analyze data, develop embedded AI features, evaluate embedded libraries, and design algorithms without any coding.

Benchmark AI neural network models

Use online AI benchmarking services. Test your models on ST devices remotely.

Demo: classify motor faults

Learn how we added machine learning features to an industrial machine based on vibration data.

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Webinar

Oct 29, 2024 - 10:00 AM CEST

Deliver everyday practicality to your IoT market with edge AI

Learn how to run tiny neural networks directly on an IMU sensor with Intelligent Sensor Processing Unit (ISPU) for unmatched performance and efficiency.

Denise Sanfilippo

Application Development Engineer

Starts in

3d:12h:7min

All the tools available in the ST Edge AI Suite

X-LINUX-AI

HAND POSTURE TOF

HIGH SPEED DATALOG

MEMS STUDIO

NANOEDGE AI STUDIO

ST EDGE AI CORE

ST EDGE AI DEVELOPER CLOUD

MODEL ZOO

STELLAR STUDIO

STM32CUBE.AI

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All frameworks compatible with ST edge AI solutions


Architecture	Core Dual Arm^® Cortex^®-A35	Operating frequency 1.5 GHz	Coprocessor Arm^® Cortex^®-M33	Coprocessor frequency 400 MHz	DRAM support DDR3(L)/ DDR4/ LPDDR4 16/32-bit
Edge AI and multimedia	NPU 1.35 TOPS	3D GPU 1080p60 with RGB, LVDS, DSI outputs	Video encoding H.264 encode/decode	PCIe Gen2	Ethernet 3 ports with switch (TSN support)	USB 3	Display interface LVDS/DSI
Security	Target certifications SESIP Level 3 PSA Certified Level 1	TrustZone^® YES	Resource isolation framework YES	Secure provisioning ecosystem YES

Go to STM32MP2 product page

Discover all AI-enabled products

STM32 microcontrollers

STM32 microprocessors

Smart sensors

FAQ

Quickly find clear, concise answers to the most common questions.

Frequently asked questions on how to deploy edge AI in embedded projects.

The ST Edge AI Suite is a set of tools for integrating AI features in embedded systems. It supports STM32 microcontrollers and microprocessors, Stellar automotive microcontrollers, and MEMS smart sensors, and includes resources for data handling and AI model optimization and deployment. Users will also find educational insights and real-world case studies to simplify their design journey.

The ST Edge AI Suite is compatible with a wide range of sensors as shown in the breakdown:

Time series sensors: accelerometers, gyroscopes, magnetometers, temperature sensors, ToF ranging sensors and other sensors that output data over time.
Audio sensors: microphones are the primary sensors for capturing audio data.
Vision sensors: cameras (RGB, B&W, IR), Time of Light sensors, radar, lidar, and more.

The ST Edge AI Suite is optimized for ST sensors, including MEMS devices with an MLC and the ISPU, but it is also supports any sensor as long as the data provided is compatible with the tool requirements.

The tools in the ST Edge AI Suite can support different types of data:

High Speed Datalog:
- Time Series Data

NanoEdge AI Studio:
- Time Series Data

STM32Cube.AI (X-CUBE-AI):
- Time Series Data
- Audio Data
- Vision Data

MEMS Studio:
- Time Series Data (from the MEMS sensor)

StellarStudioAI:
- Time Series Data
- Audio Data

AI for OpenSTLinux (X-LINUX-AI):
- Time Series Data
- Audio Data
- Vision Data

For all ST devices: you can use the ST Edge AI Core CLI version and ST Edge AI Developer Cloud to optimize and evaluate your AI model performance on any ST hardware.
For STM32 MCUs: you can use the STM32Cube.AI (X-CUBE-AI) for neural network optimization and the NanoEdge AI Studio for AutoML.
For STM32 MPUs: AI for OpenSTLinux (X-LINUX-AI) and the STM32MP2 offline compiler for Linux AI frameworks.
For Stellar MCUs: StellarStudioAI software for neural network optimization and deployment.
For MEMS with a machine learning core and the ISPU: the MEMS Studio for data analysis, algorithm design and model optimization and the model zoo for pre-optimized models.

The ST Edge AI Suite facilitates the deployment of AI models by allowing users to easily find the right tool for their project:

Data logging: capturing the sensor data necessary for AI model training.
Auto ML: automatically generating optimized machine learning algorithms.
Model optimization: optimizing AI models and generating associated code for target devices.
Validation and testing: ensuring model performance meets deployment criteria.
Online benchmarking: testing model performance on ST hardware using the cloud.

Embedded developers can also benefit from:

The model zoo: simplifying the deployment of AI models on supported devices.
Documentation for more guidance through the deployment process.

Visit the edge AI Community

Your stepping stone to enabling edge AI on MCUs, MPUs, and smart sensors.