Discover NanoEdge AI Studio the market leader for EDGE AI
NanoEdge AI Studio is 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 your MCU C code.
Documentation, tutorials, videos, FAQ, and many more...
Windows 10 and
Your production data are never sent to a cloud
Libraries can run on every
Arm© Cortex-M microcontroller
Automates the search for best AI models
Collect and import data ‘live’ via serial port
Emulator to test a library before embedding
Easy to deploy
NanoEdge AI Studio abstracts away Machine Learning and Signal Processing
Project parameters + signal examples for context
Precompiled static library (.a) to link to your main code
Slide to discover our
Software in five steps
NanoEdge AI Library
Our machine learning models are developed in-house, ground-up from the algebra. Some are built from scratch, while others take inspiration from the big classics of AI/ML (e.g. kNN, SVM, neural networks…).
Each Nanoedge AI Library is optimised thanks to a few 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.
Because we designed them specifically
for microcontrollers, our models:
Are ultra optimised to run on MCUs (every ARM Cortex-M)
Are ultra 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)
on the cloud
Don’t require any
ML expertise to be created and deployed
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 compared to that model.
Based on « in studio » training. This kind of library can classify different classes of signals inside Cortex-M MCU using ML.
Cartesiam Technology Partners