Choose your microcontroller type
(ARM® Cortex® M0 to M7)
Choose the maximum amount of RAM that you wish to allocate to your library
Choose the sensor type used to collect data (accelerometer, current, HAL effect, ECG, etc…)
Provide your contextual data
Even if the learning happens inside the microcontroller, regular and abnormal signals are needed to give your library search engine some context.
NanoEdge Ai Studio uses both regular and abnormal datasets to test several machine learning model’s performance against your data, along with a multitude of different (hyper) parameters and signal processing algorithms.
Test before you compile
With NanoEdge AI Emulator, you can emulate the behavior of your custom NanoEdge AI Library directly on your PC, as if the library was running on your microcontroller.
Compile and Download
Choose your compilation flags and download your final static library ready to learn and infer in your microcontroller.
To make your life easier, you also receive a Hello World Linux and Windows emulators and documentation.
NanoEdge™ AI Studio
Thought for developers
Command line engine
to emulate MCU
Runs on Windows 10
Intuitive user experience
One-click UI, no
NEW 2020 Live data logging
Directly through serial/USB port
Static library ready
to be inserted inside
your main program
Embedded developers first
No mathematics or data science skills required.
Test your NanoEdge AI library on your PC (Windows and Linux) before your final compile
NEW 2020 Enhanced version of Cartesiam’s
Automatic data compliance and quality verification tool
NanoEdge™ AI Library
Optimized for microcontrollers
Run on every Arm Cortex
MO to M7 class MCUs
Low RAM footprint
As low as 4Kb
cortex M4 80Mhz
Stack preserved and
no dynamic allocation
Floating Point Unit
Compatible with FPU
Learning at the Edge
Iterative learning in 30ms
cortex M4 80Mhz
We share with you the essence of our knowledge
NanoEdge™ AI Studio for Embedded Applications Performance for Vibration Applications
In this paper we survey machine learning algorithms of NanoEdge AI library for anomaly detection. We focus on realistic vibration applications. For this purpose we have considered datasets from the NASA Acoustics and Vibration Database (www.data-acoustics.com) and we have carried out comparative studies with reference results to assess the perfor- mance of NanoEdge AI…
NanoEdge™ AI vs Threshold Approach for Anomaly Detection
In this paper we survey machine learning algorithms of NanoEdge AI library and a threshold approach for anomaly detection by a comparative evaluation of algorithms. The general context of
this study is embedded applications.
We are mathematicians, engineers, developers, data scientists, marketers, salespeople, artificial intelligence consultants, connected objects specialists and above all, entrepreneurs who know the capacities and constraints of companies.