Accelerating AI Systems: Let the Data Flow!
Kunle Olukotun
2022 Invited Talk
Abstract
As the benefits from Moore’s Law diminish, future computing performance improvements must rely on specialized accelerators for applications in artificial intelligence and data processing. In the future, these applications will be characterized by terabyte sized models, data sparsity and irregular control flow that will challenge the capabilities of conventional CPUs and GPUs.
In this talk, I explain how Reconfigurable Dataflow Accelerators (RDAs) can be used to boost the performance of 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.
Speaker
Kunle Olukotun
Kunle Olukotun is the Cadence Design Professor of Electrical Engineering and Computer Science at Stanford University. Olukotun is a pioneer in multicore processor design and the leader of the Stanford Hydra chip multiprocessor (CMP) research project. Olukotun founded Afara Websystems to develop high-throughput, low-power multicore 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 co-founded SambaNova Systems, a Machine Learning and Artificial Intelligence company, and continues to lead as their Chief Technologist. 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 member of National Academy of Engineering, an ACM Fellow, and an IEEE Fellow for contributions to multiprocessors on a chip design and the commercialization of this technology. He received the Harry H. Goode Memorial Award. Olukotun received his Ph.D. in Computer Engineering from The University of Michigan.
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