Stanford, Sambanova Systems
Are you building infrastructure or developer tools to power AI-first technology products? Head over to Air Street Capital.
The Research and Applied AI Summit (RAAIS) is a community for entrepreneurs and researchers who accelerate the science and applications of AI technology. In the lead up to our 5th annual event on June 28th 2019 in London, we’re running a series of speaker profiles to shed more light on what you can expect to learn on the day!
At RAAIS 2019, we’re running a spotlight on hardware for machine intelligence. We’re excited to welcome Kunle Olukotun, Cadence Design Systems Professor of Electrical Engineering and Computer Science at Stanford University. Kunle is well known as a pioneer in multicore processor design and the leader of the Stanford Hydra chip multiprocessor (CMP) research project. Kunle founded Afara Websystems in 2000 to develop high-throughput, low-power multicore processors for server systems. The Afara multicore processor, called Niagara, was acquired by Sun Microsystems in 2002. Niagara derived processors now power all Oracle SPARC-based servers. In 2018, Kunle co-founded SambaNova Systems where he serves as Chief Technologist. The company is focused on building high-performance machine learning and big data analytics platforms based on AI processors. Just yesterday, they announced a $150M Series B led by Intel Capital.
At Stanford, Kunle currently directs the Pervasive Parallelism Lab (PPL), which seeks to proliferate the use of heterogeneous parallelism in all application areas using Domain Specific Languages (DSLs). Kunle is a member of the Data Analytics for What’s Next (DAWN) Lab which is developing infrastructure for usable machine learning. He is an ACM Fellow and IEEE Fellow for contributions to multiprocessors on a chip and multi-threaded processor design. Olukotun received his Ph.D. in Computer Engineering from The University of Michigan and is originally from London!
In his talk at RAAIS 2019, Kunle will focus on computer architecture, parallel computing and its seminal role in enabling the proliferation of machine learning applications. You can catch up on Kunle’s research here.