製品概要
主な利点
Automatic ML model generator
NanoEdge AI Studio selects the best ML algorithm for a given MCU (low code / no code solution).
End-to-end edge AI deployment
No expertise required. Create tiny, state-of-the-art AI models for MCUs in record time.
Available in the ST Edge AI Suite
A collection of free online tools, case studies, and resources to support engineers at every stage of their edge AI development.
概要
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 significant advantages is that NanoEdge™ AI Studio requires no advanced data science skills. Any software developer using the Studio can create optimal ML libraries from its user-friendly environment with no artificial intelligence (AI) skills.
The Studio can generate four types of libraries: anomaly detection, outlier detection, classification, and regression libraries.
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.
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特徴
- Desktop tool for design and generation of STM32-optimized libraries: anomaly and outlier detection, feature classification, and extrapolation of temporal and multivariable signals
- Anomaly detection libraries are designed using very small datasets: fine-tune the ML model directly on the STM32 microcontroller with the on-device learning capabilities, learn the normality, and detect defects in real time
- One-class classification libraries for outlier detection, designed with a very small dataset: acquisition during normal equipment operation and detection of any abnormal pattern deviation
- N-class classification libraries designed with very small, labeled dataset: classify signals in real time
- Extrapolation with small, fragmented dataset by means of regression libraries: prediction of future values based on data patterns never seen before
- Supports any type of sensor: vibration, magnetometer, current, voltage, multiaxis 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
- Native support for STM32 development boards, no configuration required
- Easy portability across the various STM32 microcontroller series
- Desktop tool for design and generation of STM32-optimized libraries: anomaly and outlier detection, feature classification, and extrapolation of temporal and multivariable signals
ソフトウェア入手
注目ビデオ
Condition Monitoring solution for Predictive maintenance is available in FP-AI-NANOEDG1 function pack for STM32Cube.
