Let the Data Flow!

SystemX Affiliates: login to view related content.

Let the Data Flow!
Thursday, January 6, 2022 - 5:30pm to 6:30pm
Zoom (Webinar)
Kunle Olukotun - Stanford University
Abstract / Description: 

*To receive email announcements and live stream information for upcoming seminars, please subscribe to the SystemX Seminar/EE310 Mailing list here.

As the benefits from Moore’s Law diminish, future computing performance improvements must rely on specialized accelerators for applications in high performance computing, artificial intelligence, and traditional data processing. These challenging applications are characterized by terabyte sized models, data sparsity and irregular control flow. In this talk, we explain how Reconfigurable Dataflow Accelerators (RDAs) can be used to accelerate a broad set of data-intensive applications with these characteristics. SambaNova Systems is using RDA technology contained in Reconfigurable Dataflow Units (RDUs) to achieve record-setting performance on challenging machine learning tasks. We will describe how RDAs can also be used to accelerate database and HPC applications with irregular control  and data flow using a new execution model called Dataflow threads.


Kunle Olukotun is the Cadence Design Professor of Electrical Engineering and Computer Science at Stanford University. Olukotun is a pioneer in multi-core processor design and the leader of the Stanford Hydra chip multiprocessor (CMP) research project.

In 2017 Olukotun co-founded SambaNova Systems, a Machine Learning and Artificial Intelligence company, and continues to lead as their Chief Technologist. Prior to SambaNova Systems, Olukotun founded Afara Websystems to develop high- throughput, low-power multi-core processors for server systems. The Afara multi-core processor, called Niagara, was acquired by Sun Microsystems and now powers Oracle’s SPARC-based servers.

Olukotun is the Director of the Pervasive Parallel Lab and a member of the Data Analytics for What’s Next (DAWN) Lab, developing infrastructure for usable machine learning.

Olukotun is an ACM Fellow and IEEE Fellow for contributions to multiprocessors on a chip and multi-threaded processor design. Olukotun recently won the IEEE Computer Society’s Harry H. Goode Memorial Award and was also elected to the National Academy of Engineering.

Kunle received his Ph.D. in Computer Engineering from The University of Michigan.