Onur Mutlu: Catalogue data in Autumn Semester 2017 |
Name | Prof. Dr. Onur Mutlu |
Field | Computer Science |
Address | Dep. Inf.techno.u.Elektrotechnik ETH Zürich, ETZ G 61.2 Gloriastrasse 35 8092 Zürich SWITZERLAND |
Telephone | +41 44 632 88 53 |
onur.mutlu@safari.ethz.ch | |
URL | https://people.inf.ethz.ch/omutlu/ |
Department | Computer Science |
Relationship | Full Professor |
Number | Title | ECTS | Hours | Lecturers | |
---|---|---|---|---|---|
263-2210-00L | Computer Architecture | 8 credits | 6G + 1A | O. Mutlu | |
Abstract | Computer 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. | ||||
Objective | We 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. | ||||
Content | The 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. | ||||
Prerequisites / Notice | Digital technology | ||||
263-3504-00L | Hardware Acceleration for Data Processing | 2 credits | 2S | G. Alonso, T. Hoefler, O. Mutlu, C. Zhang | |
Abstract | The seminar will cover topics related to data processing using new hardware in general and hardware accelerators (GPU, FPGA, specialized processors) in particular. | ||||
Objective | The seminar will cover topics related to data processing using new hardware in general and hardware accelerators (GPU, FPGA, specialized processors) in particular. | ||||
Content | The 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. | ||||
Prerequisites / Notice | Students taking this seminar should have the necessary background in systems and low level programming. |