227-0155-00L  Machine Learning on Microcontrollers

SemesterSpring Semester 2021
LecturersM. Magno, L. Benini
Periodicityevery semester recurring course
Language of instructionEnglish
CommentNumber of participants limited to 40.
Registration in this class requires the permission of the instructors.


227-0155-00 GMachine Learning on Microcontrollers Special students and auditors need a special permission from the lecturers.
Permission from lecturers required for all students.
3 hrs
Mon13:15-16:00ETZ K 63 »
M. Magno, L. Benini

Catalogue data

AbstractMachine 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 extracts useful near-sensor information in end-nodes of the “internet-of-things”, using low-power microcontrollers (ARM-Cortex-M; RISC-V).
ObjectiveLearn 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 detail how Machine Learning algorithms can be adapted to the performance constraints and limited resources of low-power microcontrollers becoming Tiny Machine learning algorithms.
ContentThe 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 ( Decision Trees, Random, Support Vector Machines, Artificial Neural Networks, Deep Learning, and Convolutional Networks)

- 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.
Lecture notesScript and exercise sheets. Books will be suggested during the course.
Prerequisites / NoticePrerequisites: Good experience in C language programming. Microprocessors and computer architecture. Basics of Digital Signal Processing. Some exposure to machine learning concepts is also desirable.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits6 credits
ExaminersL. Benini, M. Magno
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.
Additional information on mode of examinationFinal 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.

Learning materials

No public learning materials available.
Only public learning materials are listed.


No information on groups available.


General : Special students and auditors need a special permission from the lecturers
Permission from lecturers required for all students

Offered in

DAS in Data ScienceHardware for Machine LearningWInformation
Data Science MasterCore ElectivesWInformation
Electrical Engineering and Information Technology MasterSpecialization CoursesWInformation
Electrical Engineering and Information Technology MasterSpecialization CoursesWInformation
Electrical Engineering and Information Technology MasterRecommended SubjectsWInformation
Electrical Engineering and Information Technology MasterRecommended SubjectsWInformation