Experience
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.
Features
NanoEdge™ AI Studio
to emulate MCU
and Linux
coding necessary
to be inserted inside
your main program
Features
NanoEdge™ AI Library
MO to M7 class MCUs
cortex M4 80Mhz
no dynamic allocation
Hardware MCU
cortex M4 80Mhz
White papers
We share with you the essence of our knowledge
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…
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
NanoEdge™ AI studio
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