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Application Software Pack: ML-Based System State Monitor

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This Machine Learning (ML) application software pack, ML-based System State Monitor, relies on the Deep Learning (DL) subfield of ML and enables developers to develop and deploy neural networks on MCU-based systems for building smart sensing applications. This example software package, designed to show how to create a fan vibration state monitoring solution and also provides details on how to validate and evaluate the performance of a model by running it through different inference engines on an embedded sensing device.

This App SW Pack is available for the MCX N family with dedicated eIQ® Neutron neural processing unit (NPU) for machine learning applications, i.MX RT1170 family of Crossover MCUs, LPC55S6x and K66 families of general purpose MCUs.

To access this software pack, click on the download button here to visit the NXP MCUXpresso SDK github repository and follow the steps found in this App SW Pack ML State Monitor Lab Guide.

Diagram

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ML-based System State Monitor - App SW Pack

ML-based System State Monitor - App SW Pack

ML-based System State Monitor - App SW Pack - Example

ML-Based System State Monitor App SW Pack - Example

ML-based System State Monitor - App SW Pack - Collaterals

ML-Based System State Monitor App SW Pack - Collaterals

ML-based System State Monitor - App SW Pack - SW Stack Block Diagram

ML-Based System State Monitor App SW Pack - SW Stack Block Diagram

Features

  • AI edge Computing
  • eIQ® machine learning (ML) software development and deep learning at the edge on i.MX RT Crossover MCUs
  • Complete and easy ML-based applications development, validation and performance analysis
  • Multiple inference engines usage: TensorFlow™ Lite Micro, DeepViewRT™, Glow
  • Provides examples through an entire workflow of building and deploying on embedded targets with a real use-case
  • Related application spaces: system state monitoring, activity recognition, machine health (preventive maintenance, anomaly detection, failure identification)
  • Low-latency real-time system monitoring and failure identification
  • Applicable to time series real-time data
  • Can be used for other types of sensor data models that detect vibration

Supported Devices

  • i.MX-RT1170 : i.MX RT1170: 1 GHz Crossover MCU with Arm® Cortex® Cores
  • LPC55S6x : High Efficiency Arm® Cortex®-M33-Based Microcontroller Family
  • K66_180 : Kinetis® K66-180 MHz, Dual High-Speed and Full-speed USBs, 2MB Flash Microcontrollers (MCUs) based on Arm® Cortex®-M4 Core
  • FXLS8974CF : ±2g/±4g/±8g/±16g, Low-Power 12-Bit Digital IoT Accelerometer
  • MCX-N94-N54-N53-N52-N24 : MCX N94, N54, N53, N52 and N24 with Highly Integrated Low-power Dual Core Arm® Cortex®-M33 MCUs, with on-chip Accelerators and Advanced Security

Downloads

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  • Application Example Software

    Application Software Pack - ML State Monitor

    EXTERNALRev 0Jan 18, 2022 16:36:00APPLICATION-SW-PACK-ML-STATE-MONITOR

Note: For better experience, software downloads are recommended on desktop.

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Documentation

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1 documents

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Design Resources

Hardware

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5 hardware offerings

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2 hardware offerings

  • CuBox M
    Embedded Board Solutions

    CuBox M

    CuBox-M and its iMX8M Plus based SOM adds 2.3 TOPs of neural network acceleration inside. This makes it the perfect quick to market solution for your latest vision proces...
    CuBox M By SolidRun Ltd Gold Partner
  • Nitrogen93 SMARC
    Embedded Board Solutions

    Nitrogen93 SMARC

    SMARC form factor SOM powered by i.MX 93, NXP PMIC PCA9451A, our Sona NX611 Wi-Fi 6/BT 5.3 using NXP’s IW611, LPDDR4 RAM, and eMMC. Software options: Yocto Linux/Buildroo...
    Nitrogen93 SMARC By Ezurio Gold Partner

To find a complete list of our partners that support this software, please see our Partner Marketplace.

Training

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2 trainings

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