Discover NanoEdge AI Studio, 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.
Documentation, tutorials, videos, FAQ, and much more...
Windows 10 and
Your production data is never sent to a cloud
Libraries can run on every
Arm© Cortex-M microcontroller
Automates the search for the best AI models for your project
Collect and import data in real time via serial port
Emulator to test a library before embedding in a connected object
C libraries that are easy to deploy
NanoEdge AI Studio simplifies Machine Learning and Signal Processing
Project parameters + signal examples for context
Precompiled static library (.a) to link to your main code
software in 5 steps
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) that bring powerful ML features to any Cortex-M C code.
for microcontrollers, our models:
Are optimised to run on MCUs (every ARM Cortex-M)
Are very memory efficient (1-20KB RAM/Flash)
Are ultra fast (1-20ms inference on M4 80MHz)
Can be trained and used directly within the MCU
Can be integrated into
existing code / hardware
Preserve your stack (static allocation, no dynamic allocation)
Do not rely
on the cloud
Do not require any
ML expertise for creation and deployment
2 types of librairies
Anomaly detection library
Based on « in situ » training (directly inside Cortex-M MCU). This kind of ML library can be used to learn a series of nominal signals and detect anomalies based on that model.
Based on « in studio » training. This kind of library can classify different classes of signals inside a Cortex-M MCU using ML.