Automated Machine Learning (ML) tool for STM32 developers

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Product overview


NanoEdge™ AI Studio (NanoEdgeAIStudio) is a new Machine Learning (ML) technology to bring true innovation easily to the end-users. In just a few steps, developers can create an optimal ML library for their project, based on a minimal amount of data. NanoEdge™ AI Studio, also called the Studio, is a PC-based push-button development studio for developers, which runs on Windows® or Linux® Ubuntu®.
One of its big advantages is that NanoEdge™ AI Studio requires no specific data science skills. Any software developer using the Studio can create optimal ML libraries from its user-friendly environment with absolutely no Artificial Intelligence (AI) skills.
The Studio can generate two types of libraries: anomaly detection libraries and classification libraries.
An anomaly detection library is generated from a minimal amount of data examples showing normal and abnormal behaviors. Once created, load the library into the microcontroller to train and infer directly on the device. The library learns the equipment behavior from data acquired locally and adapts to each equipment behavior. Once trained, the library inference compares data coming from equipment over time against the locally created models to identify and report anomalies.
A classification library can be used to classify a collection of data, representing different types of equipment defects (such as bearing problems, cavitation problems or others) or different types of events in equipment environment. Import the signals into the Studio and, in just a few steps, create a classification ML library that gathers all this knowledge into a single library. When running on the microcontroller, the classifier analyzes the live data and indicates the percentage of similarity against this static knowledge.
Both types of libraries can be combined and chained, anomaly detection to detect a problem on the equipment and classification to identify the source of the problem to provide real insight to the maintenance team.
The input signals can range from vibration to pressure, sound, magnetic, time of flight just to name a few, or even a combination of several signals. Multiple sensors can be combined, either in a single library, or using multiple libraries concurrently.
Both learning and inference are done directly inside the microcontroller by means of the NanoEdge™ AI self-learning library, which streamlines the AI process and significantly reduces development effort, cost and therefore time to market.
  • All features

    • Desktop tool for design and generation of an STM32-optimized library for anomaly detection and feature classification of temporal and multi-variable signals
    • Anomaly detection libraries are designed using very small datasets. They can learn normality directly on the STM32 microcontroller and detect defects in real time
    • Classification libraries are designed with very small, labeled dataset. They classify signals in real time
    • Supports any type of sensor: vibration, magnetometer, current, voltage, multi-axis accelerometer, temperature, acoustic and more
    • Explore millions of possible algorithms to find the optimal library in terms of accuracy, confidence, inference time and memory footprint
    • Generate very small footprint libraries running down to the smallest Arm® Cortex®-M0 microcontrollers
    • Embedded emulator to test library performance live with an attached STM32 board or from test data files
    • Easy portability across the various STM32 microcontroller series

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