Hardware Acceleration for Data Processing

Overview

The seminar is intended to cover recent results in the increasingly important field of hardware acceleration for data processing, both in dedicated machines or in data centers. The seminar is aimed 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 Tuesday September 17, 2024 with an overview of the general topics and the intended format of the seminar. This semester the course will differ slightly. Taking inspiration from Dr. Klimovic's ML systems class, we will be doing presentations in groups.You can self-form groups of four. Each student will pick a paper they'd like to present. We will then schedule two presentations per class session. During the 40 minute presentation, there will be 4 approximately 10 minute sections: (1) the historian who gives relevant background to the paper (2) the presenter who presents the work as their own (3) the reviewer who critiques the work and (4) the PhD student who presents ideas for future work (including follow-up work actually proposed) and leads a discussion of the work.

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