227-0155-00L Machine Learning on Microcontrollers
|Dozierende||M. Magno, L. Benini|
|Periodizität||jedes Semester wiederkehrende Veranstaltung|
|Kommentar||Number of participants limited to 40.|
Registration in this class requires the permission of the instructors.
|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 extracts useful near-sensor information in end-nodes of the “internet-of-things”, using low-power microcontrollers (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 detail how Machine Learning algorithms can be adapted to the performance constraints and limited resources of low-power microcontrollers becoming Tiny Machine learning algorithms.|
|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 ( 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.
|Skript||Script and exercise sheets. Books will be suggested during the course.|
|Voraussetzungen / Besonderes||Prerequisites: Good experience in C language programming. Microprocessors and computer architecture. Basics of Digital Signal Processing. Some exposure to machine learning concepts is also desirable.|