227-0150-00L  Systems-on-chip for Data Analytics and Machine Learning

SemesterFrühjahrssemester 2020
DozierendeL. Benini
Periodizitätjährlich wiederkehrende Veranstaltung
KommentarPreviously "Energy-Efficient Parallel Computing Systems for Data Analytics"


227-0150-00 GSystems-on-chip for Data Analytics and Machine Learning4 Std.
Di08:15-12:00ETZ E 9 »
L. Benini


KurzbeschreibungSystems-on-chip architecture and related design issues with a focus on machine learning and data analytics applications. It will cover multi-cores, many-cores, vector engines, GP-GPUs, application-specific processors and heterogeneous compute accelerators. Special emphasis given to energy-efficiency issues and hardware-software techniques for power and energy minimization.
LernzielGive in-depth understanding of the links and dependencies between architectures and their energy-efficient implementation and to get a comprehensive exposure to state-of-the-art systems-on-chip platforms for machine learning and data analytics. Practical experience will also be gained through practical exercises and mini-projects (hardware and software) assigned on specific topics.
InhaltThe course will cover advanced system-on-chip architectures, with an in-depth view on design challenges related to advanced silicon technology and state-of-the-art system integration options (nanometer silicon technology, novel storage devices, three-dimensional integration, advanced system packaging). The emphasis will be on programmable parallel architectures with application focus on machine learning and data analytics. The main SoC architectural families will be covered: namely, multi and many- cores, GPUs, vector accelerators, application-specific processors, heterogeneous platforms. The course will cover the complex design choices required to achieve scalability and energy proportionality. The course will will also delve into system design, touching on hardware-software tradeoffs and full-system analysis and optimization taking into account non-functional constraints and quality metrics, such as power consumption, thermal dissipation, reliability and variability. The application focus will be on machine learning both in the cloud and at the edges (near-sensor analytics).
SkriptSlides will be provided to accompany lectures. Pointers to scientific literature will be given. Exercise scripts and tutorials will be provided.
LiteraturJohn L. Hennessy, David A. Patterson, Computer Architecture: A Quantitative Approach (The Morgan Kaufmann Series in Computer Architecture and Design) 6th Edition, 2017.
Voraussetzungen / BesonderesKnowledge of digital design at the level of "Design of Digital Circuits SS12" is required.

Knowledge of basic VLSI design at the level of "VLSI I: Architectures of VLSI Circuits" is required


Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Leistungskontrolle als Semesterkurs
ECTS Kreditpunkte6 KP
PrüfendeL. Benini
RepetitionDie Leistungskontrolle wird in jeder Session angeboten. Die Repetition ist ohne erneute Belegung der Lerneinheit möglich.
Prüfungsmodusmündlich 30 Minuten
Diese Angaben können noch zu Semesterbeginn aktualisiert werden; verbindlich sind die Angaben auf dem Prüfungsplan.


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


Keine Informationen zu Gruppen vorhanden.


Keine zusätzlichen Belegungseinschränkungen vorhanden.

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