Ab 2. November 2020 findet das Herbstsemester 2020 online statt. Ausnahmen: Veranstaltungen, die nur mit Präsenz vor Ort durchführbar sind. Bitte beachten Sie die per E-Mail kommunizierten Informationen der Dozierenden.

Onur Mutlu: Katalogdaten im Herbstsemester 2017

NameHerr Prof. Dr. Onur Mutlu
LehrgebietInformatik
Adresse
Dep. Inf.techno.u.Elektrotechnik
ETH Zürich, ETZ G 61.2
Gloriastrasse 35
8092 Zürich
SWITZERLAND
Telefon+41 44 632 88 53
E-Mailonur.mutlu@safari.ethz.ch
URLhttps://people.inf.ethz.ch/omutlu/
DepartementInformatik
BeziehungOrdentlicher Professor

NummerTitelECTSUmfangDozierende
263-2210-00LComputer Architecture Information 8 KP6G + 1AO. Mutlu
KurzbeschreibungComputer architecture is the science and art of selecting and interconnecting hardware components to create a computer that meets functional, performance and cost goals. This course introduces the basic hardware structure of a modern programmable computer, including the basic laws underlying performance evaluation.
LernzielWe will learn, for example, how to design the control and data path hardware for a MIPS-like processor, how to make machine instructions execute simultaneously through pipelining and simple superscalar execution, and how to design fast memory and storage systems.
InhaltThe principles presented in the lecture are reinforced in the laboratory through the design and simulation of a register transfer (RT) implementation of a MIPS-like pipelined processor in System Verilog. In addition, we will develop a cycle-accurate simulator of this processor in C, and we will use this simulator to explore processor design options.
Voraussetzungen / BesonderesDigital technology
263-3504-00LHardware Acceleration for Data Processing Information 2 KP2SG. Alonso, T. Hoefler, O. Mutlu, C. Zhang
KurzbeschreibungThe seminar will cover topics related to data processing using new hardware in general and hardware accelerators (GPU, FPGA, specialized processors) in particular.
LernzielThe seminar will cover topics related to data processing using new hardware in general and hardware accelerators (GPU, FPGA, specialized processors) in particular.
InhaltThe general application areas are big data and machine learning. The systems covered will include systems from computer architecture, high performance computing, data appliances, and data centers.
Voraussetzungen / BesonderesStudents taking this seminar should have the necessary background in systems and low level programming.