Hardware Acceleration for Data Processing

Overview

The seminar is intended to cover recent results in the increasingly important field of hardware acceleration for data science, both in dedicated machines or in data centers. The seminar aims at students interested in the system aspects of data processing who are willing to bridge the gap across traditional disciplines: machine learning, databases, systems, and computer architecture. The seminar should be of special interest to students interested in completing a master thesis or even a doctoral dissertation in related topics.

The seminar will start on September 21 with an overview of the general topics and the intended format of the seminar. Students are expected to present one paper in a 30 minute talk and complete a report (max 4 pages, excluding references) on the main idea of the paper and how they relate to the other papers presented at the seminar and the discussions around those papers. The presentation will be given during the semester in the allocated time slot. The report is due on the last day of the semester (Dec. 17th).

Attendance to the seminar is mandatory to complete the credit requirements. Active participation is also expected, including having read every paper to be presented in advance and contributing to the questions and discussions of each paper during the seminar. 

Due to the uncertainties created by the Coronavirus, this seminar will be held online (via ZOOM).