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227-0155-00L 6 Credits MSC , WBZ D-ITET , D-MATH , D-INFK
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Machine Learning on Microcontrollers

Lecturers & Examiners: PD Dr. Michele Magno, Prof. Dr. Luca Benini
Registration in this class requires the permission of the instructors. Class size will be limited to 16. Preference is given to students in the MSc EEIT.
VVZ CR n/a

Last Updated: 2026-02-05 15:35:17

Abstract

Machine Learning (ML) and artificial intelligence are pervading the digital society. Today, even low power embedded systems are incorporating ML, becoming increasingly “smart”. This lecture gives an overview of ML methods and algorithms to process and extract useful near-sensor information in end-nodes of the “internet-of-things”, using low-power microcontrollers/ processors (ARM-Cortex-M; RISC-V)

Objective

Learn how to Process data from sensors and how to extract useful information with low power microprocessors using ML techniques. We will analyze data coming from real low-power sensors (accelerometers, microphones, ExG bio-signals, cameras…). The main objective is to study in details how Machine Learning algorithms can be adapted to the performance constraints and limited resources of low-power microcontrollers.

Content

The final goal of the course is a deep understanding of machine learning and its practical implementation on single- and multi-core microcontrollers, coupled with performance and energy efficiency analysis and optimization. The main topics of the course include: - Sensors and sensor data acquisition with low power embedded systems - Machine Learning: Overview of supervised and unsupervised learning and in particular supervised learning (Bayes Decision Theory, Decision Trees, Random Forests, kNN-Methods, Support Vector Machines, Convolutional Networks and Deep Learning) - Low-power embedded systems and their architecture. Low Power microcontrollers (ARM-Cortex M) and RISC-V-based Parallel Ultra Low Power (PULP) systems-on-chip. - Low power smart sensor system design: hardware-software tradeoffs, analysis, and optimization. Implementation and performance evaluation of ML in battery-operated embedded systems. The laboratory exercised will show how to address concrete design problems, like motion, gesture recognition, emotion detection, image and sound classification, using real sensors data and real MCU boards. Presentations from Ph.D. students and the visit to the Digital Circuits and Systems Group will introduce current research topics and international research projects.

Resources

Lecture Notes

Script and exercise sheets. Books will be suggested during the course.

General Information

Language
English
Levels
MSC , WBZ
Frequency
Semesterly recurring

Examination

Type
graded semester performance
Final grade will be based on a graded project work that can also be done in teams. The project topic can be chosen freely, as long as it employs content that is taught in this course and it employs machine learning on micro-controllers.The final project can be done at home, we are providing to the students all the hardware necessary. The final presentation of their work is done via zoom.

Registration & Places

Max Places
40
Signup End
27.09.2020
Priority: Registration for the course unit is only possible for the primary target group

Course Components

Type Title Time & Place Hours
lecture with exercise Machine Learning on Microcontrollers
Permission from lecturers required for all students. All the lectures will be remote by zoom. For exercises we will include a tutorial to install all the software at home. The lab will be divided in 3 groups and students need physical assistance and can come in their dedicate time in ETZ K63.
  • Mon 13:15-17:00 (ETZ K 63)
3 h weekly

Offered In