ST EDGE AI SUITE

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

Be inspired.

50+

Case studies.

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10+

Free software and tools.

Find support.

20+

Resources and documents.

Dive into the edge AI world

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

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.

Webinar

Uncover the potential of edge AI by exploring a real-world application

Gain insights into edge AI and its transformative business impact with a firsthand customer testimonial.

Pierre Guiglion

Technical Marketing Engineer

All the tools available in the ST Edge AI Suite

X-LINUX-AI

HAND POSTURE TOF

HIGH SPEED DATALOG

MEMS STUDIO

NANO EDGE AI STUDIO

ST EDGE AI CORE

EDGE AI DEVELOPER CLOUD

MODEL ZOO

STELLAR STUDIO

STM32 CUBE AI

By bringing solutions to engineers and data scientists at every stage of their development, the ST Edge AI Suite accelerates edge AI adoption.

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.

The tools featured in the ST Edge AI Suite are free of charge, including for commercial use, which contributes to reducing the costs of running AI on embedded devices.

Discover the future of edge computing at the tinyML EMEA Innovation Forum.

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