David Patterson

David Patterson is the Pardee Professor of Computer Science, Emeritus at the University of California at Berkeley, which he joined after graduating from UCLA in 1976. Dave's research style is to identify critical questions for the IT industry and gather inter-disciplinary groups of faculty and graduate students to answer them. The answer is typically embodied in demonstration systems, and these demonstration systems are later mirrored in commercial products. In addition to research impact, these projects train leaders of our field. The best known projects were Reduced Instruction Set Computers (RISC), Redundant Array of Inexpensive Disks (RAID), and Networks of Workstations (NOW), each of which helped lead to billion dollar industries. A measure of the success of projects is the list of awards won by Patterson and as his teammates: the C & C Prize, the IEEE von Neumann Medal, the IEEE Johnson Storage Award, the SIGMOD Test of Time award, the ACM-IEEE Eckert-Mauchly Award, and the Katayanagi Prize. He was also elected to both AAAS societies, the National Academy of Engineering, the National Academy of Sciences, the Silicon Valley Engineering Hall of Fame, and to be a Fellow of the Computer History Museum. The full list includes about 35 awards for research, teaching, and service. In his spare time he coauthored seven books, including two with John Hennessy, who is past President of Stanford University. Patterson also served as Chair of the Computer Science Division at UC Berkeley, Chair of the Computing Research Association, and President of ACM.

Publications

Blog Posts

MLPerf: SPEC for ML

David Patterson Deep Learning, News, Open Source, Optimization, Reinforcement Learning, Systems, Uncategorized 0 Comments

The RISE Lab at UC Berkeley today joins Baidu, Google, Harvard University, and Stanford University to announce a new benchmark suite for machine learning called MLPerf at the O’Reilly AI conference in New York City (see https://mlperf.org/). The MLPerf effort aims to build a common set of benchmarks that enables the machine learning (ML) field to measure system performance eventually for both training and inference from mobile devices to cloud services. We believe that a widely accepted benchmark suite will benefit the entire community, including researchers, developers, builders of machine learning frameworks, cloud service providers, hardware manufacturers, application providers, and end users. Historical Inspiration. We are motivated in part by the System Performance Evaluation Cooperative (SPEC) benchmark for general-purpose computing that drove rapid, …