===== Workshop: Rapid Development of ML-Based Anomaly Detection on STM32 Microcontrollers =====

**When:** Wed **26.02.2025** 12:00 - 17:00 \\
**Where:** KYPO - S108, Fakulta informatiky, Botanická 68a \\
**Registration:** [[https://forms.office.com/e/gJRsMFBgk1]] \\ 
open for everybody \\
**Questions**: [[mailto:jan.kral@fi.muni.cz]]

=== Part 1: STM32 MCUs & Portfolio ===
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__Duration:__ approx. 45 min
  * STM32 MCUs Portfolio Update & ST MEMS Portfolio Update

=== Part 2: Rapid Development of Anomaly Detection Machine Learning Algorithms ===
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Join us for a hands-on training session focused on rapidly developing and integrating machine learning (ML) models within embedded applications using resource-constrained microcontrollers (MCUs), such as the Cortex-M0+ with 32 Kbytes RAM and 256 Kbytes flash. This training is designed to accelerate model development through automated tools, minimizing the need for extensive data science expertise. Join us to experience the practicality and potential of ML in embedded systems!

**Key components of the class:**
__Duration:__ approx. 3 hours

  * **Anomaly detection use case:** Explore predictive maintenance for mechanical parts, specifically linear motors. Due to practical constraints, attendees will emulate motor movements by hand.

  * **Relevant components:** Utilize MEMS sensors like accelerometers and gyroscopes for movement trajectory prediction.

  * **Comprehensive development pipeline:** \\
    * **Data collection:** Gather input datasets. \\
    * **ML model selection and benchmarking:** Choose and evaluate the best models. \\
    * **Model emulation:** Simulate ML model behavior using real-time data streams. \\
    * **Model library generation:** Create and integrate ML model libraries within embedded applications.

** Expected outcomes **

Develop ML model libraries with a binary size of a few Kbytes of RAM and flash, capable of learning on the target MCU during runtime. Our goal is to ignite a passion for efficient algorithm coding and demonstrate that ML is feasible on limited-resource MCUs, including runtime learning on the target MCU. 

=== Part 3: Question and Answer Session ===
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General discussion about ST MCUs and Sensors.

=== Course Materials ===
Presentation and templates: \\
[[https://owncloud.cesnet.cz/index.php/s/8FEN8GD6CwN8337]]

Teams Link: \\
[[https://teams.microsoft.com/meet/332156999227?p=aoxBJGb7a5QaF5H2tl 
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