1 Description
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.
A demonstration version is available for three months for free experiments. A professional version provides the
yearly Solo or Team licenses for embedded developers.
To help users to bootstrap their projects, STMicroelectronics proposes the Edge AI Sprint Package to limit risks
and investments while increasing the chances of success. This is a bundle that includes training sessions, a
NanoEdge
™
AI Studio license, and technical support.
Check the ordering information section from the data brief for more details and contact STMicroelectronics sales
office or authorized business partners to proceed with an order.
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 four types of libraries: anomaly detection, outlier detection, classification, and regression
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.
Outlier detection can be used to detect any abnormality with the one-class classification method. No example
of abnormal behavior is needed. Import normal signals into the Studio and easily create an optimized outlier
detection ML library.
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.
A regression algorithm can be used to extrapolate data and predict future data patterns. Import signals and
targeted values in the desktop tool and generate in a few steps a smart library to, for example, improve energy
management or forecast the remaining lifetime of an equipment.
These libraries can be combined and chained: anomaly or outlier detection to detect a problem on the equipment,
classification to identify the source of the problem, and regression to extrapolate information and 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.
NanoEdgeAIStudio
Description
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