NanoEdgeAIStudio Automated Machine Learning (ML) tool for STM32 microcontrollers
●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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STM32 、 STNEAIEVALLIC 、 STNEAISOLOLIC 、 STNEAITEAMLIC 、 STNEAISPR1 、 STNEAISPR2 、 STNEAISPR3 、 STNEAISPR4 |
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Datasheet |
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Please see the document for details |
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English Chinese Chinese and English Japanese |
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15-Sep-2021 |
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Rev 1 |
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DB4564 |
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1.1 MB |
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