227-0155-00L  Machine Learning on Microcontrollers

SemesterHerbstsemester 2020
DozierendeM. Magno, L. Benini
Periodizitätjedes Semester wiederkehrende Veranstaltung
LehrspracheEnglisch
KommentarRegistration 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.



Lehrveranstaltungen

NummerTitelUmfangDozierende
227-0155-00 GMachine Learning on Microcontrollers Für Fachstudierende und Hörer/-innen ist eine Spezialbewilligung der Dozierenden notwendig.
Bewilligung der Dozierenden für alle Studierenden notwendig.
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.
3 Std.
Mo13:15-17:00ETZ K 63 »
M. Magno, L. Benini

Katalogdaten

KurzbeschreibungMachine 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)
LernzielLearn 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.
InhaltThe 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.
SkriptScript and exercise sheets. Books will be suggested during the course.
Voraussetzungen / BesonderesPrerequisites: C language programming. Basics of Digital Signal Processing. Basics of processor and computer architecture. Some exposure to machine learning concepts is also desirable

Leistungskontrolle

Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Leistungskontrolle als Semesterkurs
ECTS Kreditpunkte6 KP
PrüfendeL. Benini, M. Magno
Formbenotete Semesterleistung
PrüfungsspracheEnglisch
RepetitionRepetition nur nach erneuter Belegung der Lerneinheit möglich.
Zusatzinformation zum PrüfungsmodusFinal 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.

Lernmaterialien

Keine öffentlichen Lernmaterialien verfügbar.
Es werden nur die öffentlichen Lernmaterialien aufgeführt.

Gruppen

Keine Informationen zu Gruppen vorhanden.

Einschränkungen

Allgemein : Für Fachstudierende und Hörer/-innen ist eine Spezialbewilligung der Dozierenden notwendig
Bewilligung der Dozierenden für alle Studierenden notwendig
PlätzeMaximal 40
VorrangDie Belegung der Lerneinheit ist nur durch die primäre Zielgruppe möglich
Primäre ZielgruppeRobotics, Systems and Control MSc (159000)
Elektrotechnik und Informationstechnologie MSc (237000)
DAS ETH in Informationstechn. und Elektrotechnik (244000)
Elektrotech. und Informationstechnol. (Mobilität) (249000)
WartelisteBis 20.09.2020
BelegungsendeBelegung nur bis 27.09.2020 möglich

Angeboten in

StudiengangBereichTyp
DAS in Data ScienceHardware for Machine LearningWInformation
Elektrotechnik und Informationstechnologie MasterEmpfohlene FächerWInformation
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Elektrotechnik und Informationstechnologie MasterVertiefungsfächerWInformation
Elektrotechnik und Informationstechnologie MasterEmpfohlene FächerWInformation