September 27, 2016 / By Tiffany Fox
San Diego, Calif., Sept. 27, 2016 — The scientific method traditionally begins with a hypothesis, which is then tested against data. Powerful new “brain-inspired” computing capabilities are turning that idea on its head by accelerating a “data science” experimental method -- a method that detects patterns in data as a critical first step in generating a hypothesis.
“Pattern recognition is a mode of epistemology, a way of knowing,” says University of California San Diego’s Larry Smarr, director of the California Institute for Telecommunications and Information Technology (Calit2). “It’s taking the same data that’s available to everyone and trying to let the data talk to you instead of putting your preconceived notions onto it.”
As both machine learning (ML) techniques and novel computer architectures continue to rapidly develop, a major challenge is emerging: how to optimize a variety of ML algorithms on different architectures and discover which are fastest and most energy efficient for specific applications across a wide range of disciplines. Furthermore, there must be flexibility to both process massive static arrays of data as well as myriad flows of data – and find the never-before-seen patterns in both.
To explore these trade-offs, Calit2 has created a Pattern Recognition Laboratory (PRLab), housed in Calit2’s Qualcomm Institute at UC San Diego. The PRLab is in the early stages of building a “garden of architectures” capable of performing massive amounts of high-speed processing without consuming as much power as traditional chips.
UC San Diego Professor Ken Kreutz-Delgado, a long-time member of the Electrical and Computer Engineering Department, is the PRLab’s first director. Kreutz-Delgado is taking a broad view of the disciplines to which pattern-recognition computing can be usefully applied.
“It isn’t just science and engineering problems, but also extends to arenas in sociology, politics, economics… any discipline where data can be collected and analyzed with models from the bottom up,” said Kreutz-Delgado.
Besides powerful traditional von Neumann architectures such as shared-memory multi-core and graphics processing units (GPUs), the PRLab has acquired non-von Neumann architectures such as high density Field Programmable Gate Arrays (FPGAs), IBM’s TrueNorth neuromorphic processor, and KnuEdge’s LambdaFabricTM neural computing systems.
The PRLab is the most recent development born from a decade-long collaboration between Smarr and Mark Anderson. Anderson is the founder and publisher of the widely-read Strategic News Service Global Report and the Future in Review, or “FiRe,”Conference, which explores how technology drives the global economy and has been described as “the best technology conference in the world” by The Economist. Smarr and Anderson, who often refer to Calit2 as “The “FiRe Lab,” jointly developed the PRLab concept and invited Professor Kreutz-Delgado to FiRe 2015 to announce its formation. FiRe 2016 – which begins today -- will explore in detail the “Power of Flows” and the necessity of pattern computing for interpreting them. Mark is a member of the Calit2 Advisory Board and Larry is a member of the Future in Review Advisory Board.
Both Anderson and Smarr liken pattern recognition to the methodology they have used over the years to make predictions about the future of science and technology.
“I’ve learned to be conscious of frames and filters, how they affect our perceptions, and how to drop them in order to see the patterns of the world objectively,” says Anderson. “Once you see present patterns clearly, accurate future predictions become a matter of patterns made and patterns broken. I believe the PRLab will begin to discover computational approaches that can do this with Big Data Flows.”
The Pattern Recognition Lab will also collaborate with the Pacific Research Platform (PRP), a regional cyberinfrastructure, funded by the National Science Foundation (NSF), that securely interconnects many dedicated research networks at speeds up to 1,000 times that of the commodity internet. This provides access to extremely large datasets of static and streaming data whose analysis and interpretation is of keen interest to scientists, engineers, businesses and policymakers.
“The Pattern Recognition Laboratory, connected to the Pacific Research Platform, is an early model of how we can do distributed, brain-inspired computing,” says QI research scientist Tom DeFanti, co-principal investigator on the PRP. “This will enable people to attempt rapid turnaround on significant computational problems that can only be dreamed of today.”