NanoEdge™ AI Studio

Our new software NanoEdge™ AI Studio will easily let you create a machine learning static library to embed in your main program running on any ARM© Cortex© M microcontroller.

NanoEdge™ AI Studio
in four easy steps

  • STEP 1
  • STEP 2
  • STEP 3
  • STEP 4

Describe your hardware environment

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
and Linux
Intuitive user experience
One-click UI, no
coding necessary
NEW 2020 Live data logging
Directly through serial/USB port
Easy deployment
Static library ready
to be inserted inside
your main program
Embedded developers first
No mathematics or data science skills required.
Emulators available
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
Low latency
16ms inference,
cortex M4 80Mhz
Static allocation
Stack preserved and
no dynamic allocation
Floating Point Unit
Compatible with FPU
Hardware MCU
Learning at the Edge
Iterative learning in 30ms
cortex M4 80Mhz

White papers


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 ( and we have carried out comparative studies with reference results to assess the perfor- mance of NanoEdge AI…

Download the paper

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.

Download the paper

NanoEdge™ AI studio