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
