What is new in
NanoEdge AI Studio V3?

What is new in
NanoEdge AI Studio V3?

Additional families of Machine Learning algorithm

Regression algorithms to extrapolate data and Outliers algorithms to anticipate wear and tear phenomena or to better deal with equipment obsolescence.

Additional Machine Learning algorithm families

Regression algorithms to extrapolate data and Outliers algorithms to anticipate wear and tear phenomena or to better deal with equipment obsolescence.

Easier data management​

New high-speed data acquisition and management on the STWIN development board making all industrial-grade sensors easily manageable without having to write a single line of code.​

Redesigned user interface

Completely redesigned user interface to make it even easier for non-experts to develop state-of-the-art machine learning libraries.​

With support of all STM32

Native support of all STM32 development board​

With support of all STM32

Native support of all STM32 development board​

Discover NanoEdge AI Studio V3, the market leader for EDGE AI

A search engine desktop software for AI libraries, designed for embedded developers.

With Nanoedge AI Studio, find the best AI library for your embedded project, and start incorporating machine learning capabilities into the C code in your MCU.

4 minutes overview of the Studio

Discover NanoEdge AI Studio V3, the market leader for EDGE AI

A search engine desktop software for AI libraries, designed for embedded developers.

With Nanoedge AI Studio, find the best AI library for your embedded project, and start incorporating machine learning capabilities into the C code in your MCU.

4 minutes overview of the Studio

4 Families of librairies available

Anomaly Detection

Anomaly Detection

Untrained anomaly detection library that will learn incrementally, directly on the target microcontroller.

1 Class

1 Class

Pre-trained outlier detection library that will infer directly on the target microcontroller. Useful to anticipate wear and tear phenomena or to better deal with equipment obsolescence.

Classification

Classification

Pre-trained classification library that will infer directly on the target microcontroller.

Extrapolation

Extrapolation

Pre-trained regression library that will infer directly on the target microcontroller. Particularly useful for predictive maintenance or to forecast remaining life of equipment.

Anomaly Detection

Anomaly Detection

Untrained anomaly detection library that will learn incrementally, directly on the target microcontroller.

1 Class

1 Class

Pre-trained outlier detection library that will infer directly on the target microcontroller. Useful to anticipate wear and tear phenomena or to better deal with equipment obsolescence.

Classification

Classification

Pre-trained classification library that will infer directly on the target microcontroller.

Extrapolation

Extrapolation

Pre-trained regression library that will infer directly on the target microcontroller. Particularly useful for predictive maintenance or to forecast remaining life of equipment.

From idea to datalogging, in a matter of minutes

From idea to datalogging, in a matter of minutes

Capturing data is essential to start your project, but that task might be complex and time-consuming.

That is why we embedded within the Studio a data logger function to streamline this process and make it easy to acquire and manage high-speed data on the STWIN development board

It makes all industrial-grade sensors easily manageable without having to write a single line of code. 

Just plug your board to your serial port, start NanoEdge AI Studio and you are good to go! 

NanoEdge AI Studio features

Windows 10 and Ubuntu versions

Your production data is never sent to a cloud

Dedicated to embedded developers

Libraries can run on every STM32 Arm© Cortex-M microcontroller

Automatic data quality check

Automates the search for the best AI models for your project

Collect and import data in real time via serial port. Optimized for STWIN boards

Emulator to test a library before flashing the code on your MCU

C libraries that are easy to deploy

NanoEdge AI Studio simplifies Machine Learning and Signal Processing 

Input

Project parameters : max ram, max flash, microcontroller type

Output

Precompiled static library (.a) to link to your main code

NanoEdge AI Library

Our machine learning models are developed in-house, from the ground up, starting with the algebra. Several models are built from scratch, while others are based on traditional AI/ML models (e.g. kNN, SVM, neural networks…).

Each Nanoedge AI Library is optimised thanks to several user-imported signal examples. It contains the best ML model for your project, combined with adequate signal pre-processing and optimal hyperparametrization. This library provides simple and intuitive functions (learn, detect, classify and extrapolate) that bring powerful ML features to any STM32 Cortex-M C code.

AI library

AI library

Our models are optimized for all STM32 microcontrollers.

Optimized to run on MCUs (any STM32 ARM Cortex M0-M7)

Very memory efficient (1-20KB RAM/Flash)

Ultra fast (1-20ms inference on M4 80MHz)

Can be trained and used directly within the MCU

Can be integrated into existing code / hardware

Consume very little energy

Preserve your stack (static allocation, no dynamic allocation)

Do not rely
on the cloud

Do not require any ML expertise for creation and deployment