Professor Kunle Olutokun, Stanford University
March 24, 2022
4:00 p.m. EST
RSVP to Prof. Olukotun’s lecture.
Title: Let the Data Flow!
Abstract: As the benefits from Moore’s Law diminish, future computing performance improvements will rely on specialized application accelerators. To justify the expense of designing an accelerator it should accelerate an important set of application areas. In my talk, I will explain how Reconfigurable Dataflow Accelerators (RDAs) can be used to accelerate a broad set of data intensive applications. RDAs can accelerate Machine Learning (ML) by efficiently executing the hierarchical dataflow that exists in many ML applications and models. I will explain how RDAs can also be used to accelerate irregular applications using a new programming model called Dataflow Threads. I will talk about future research directions for dataflow architectures including sparse ML applications, networking applications and dataflow architecture compilers
Bio: 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.