Autumn Semester 2020 takes place in a mixed form of online and classroom teaching.
Please read the published information on the individual courses carefully.

227-0150-00L  Energy-Efficient Parallel Computing Systems for Data Analytics

SemesterSpring Semester 2018
LecturersL. Benini
Periodicityyearly recurring course
Language of instructionEnglish
CommentPreviously called "Advanced System-on-chip Design: Integrated Parallel Computing Architectures"

AbstractAdvanced Parallel Computing Architectures and related design issues. It will cover multi-cores, many-cores, vector engines, GP-GPUs, application-specific processors and heterogeneous compute accelerators. Focus on integrated architectures for data analytics applications. Special emphasis given to energy-efficiency issues and hardware-software design for power and energy minimizazion.
ObjectiveGive 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 computing platforma for data anlytics applications. Practical experience will also be gained through practical exercises and mini-projects (hardware and software) assigned on specific topics.
ContentThe course will cover advanced parallel computing architectures 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, namely, multi and many- cores, GPUs, vector accelerators, application-specific processors, heterogeneous platforms, and 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 emerging data analytics both in the cloud at at the edges (near-sensor analytics).
Lecture notesSlides will be provided to accompany lectures. Pointers to scientific literature will be given. Exercise scripts and tutorials will be provided.
LiteratureD. Patterson, J. Hennessy, Computer Architecture, Fifth Edition: A Quantitative Approach (The Morgan Kaufmann Series in Computer Architecture and Design), 2011.

D. Patterson, J. Hennessy, Computer Organization and Design, Fifth Edition: The Hardware/Software Interface (The Morgan Kaufmann Series in Computer Architecture and Design), 2013.
Prerequisites / NoticeKnowledge 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