Michele Magno: Katalogdaten im Herbstsemester 2020 |
| Name | Herr PD Dr. Michele Magno |
| Lehrgebiet | Embedded Systems and Tiny Machine Learning |
| Adresse | Zentr. f. projektbasiertes Lernen ETH Zürich, ETF F 109 Sternwartstrasse 7 8092 Zürich SWITZERLAND |
| Telefon | +41 44 632 66 86 |
| michele.magno@pbl.ee.ethz.ch | |
| Departement | Informationstechnologie und Elektrotechnik |
| Beziehung | Privatdozent |
| Nummer | Titel | ECTS | Umfang | Dozierende | |
|---|---|---|---|---|---|
| 227-0155-00L | Machine Learning on Microcontrollers  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. | 6 KP | 3G | M. Magno, L. Benini | |
| Kurzbeschreibung | 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) | ||||
| Lernziel | 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. | ||||
| Inhalt | 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. | ||||
| Skript | Script and exercise sheets. Books will be suggested during the course. | ||||
| Voraussetzungen / Besonderes | Prerequisites: C language programming. Basics of Digital Signal Processing. Basics of processor and computer architecture. Some exposure to machine learning concepts is also desirable | ||||