Although Intel funds the labs, it doesn’t own the intellectual property, and the research is widely shared and published, Teixeria says. Intel won’t disclose how much it’s spending on its university research projects, but its overall R&D budget is expected to exceed $5 billion this year.

The real goal, Teixeria says, is to see if the labs can unearth something that Intel might then be able to take in-house and develop further.

“It’s this notion of both helping to grow the technology and seeing where there is a usage for it within Intel,” he says.

Does “accelerate” include pointing to technologies that have already become startups, like Xen and variants of sensors? :-)

The reference to parallel search of vast, unindexed data is intriguing — it reminds me of the scale of challenges the wayback machine is facing for the Internet Archive — how could P2P help a 40TB+ search problem, given that one is willing to trade off longer response times against much lower (centralized) costs/better-shared costs?

ACM News Service

“Intel Goes to School”
Computerworld (03/28/05) P. 40; Vijayan, Jaikumar

Intel Research is funding a quartet of university “lablets” to identify and investigate technologies that merit “acceleration and amplification,” according to company representative Kevin Teixeria. He says Intel has no claim on the intellectual property produced by the labs, because it is interested in “helping to grow the technology and seeing where there is a usage for it within Intel.” Intel’s UC Berkeley lablet is focusing on systems that employ wirelessly networked sensors to collect a wealth of information about the environment, and the TinyOS operating system and TinyDB query-processing technologies have been notable breakthroughs. Researchers are currently devising the Tiny Application Sensor Kit, a suite of tools that lab director Joseph Hellerstein says will simplify the deployment of applications that use sensor networks. A second Intel lablet at the University of Washington is combining radio frequency identification (RFID) technologies and data mining software into the System for Human Activity Recognition and Prediction, which is supposed to predict human behavior by monitoring the objects people touch and how they are used. A key tool of this research is the RFID-enabled iGlove that extracts data from objects with affixed RFID tags. Another lablet based at England’s University of Cambridge under the supervision of Derek McAuley is looking into highly distributed applications, examples of which include Xen, a “virtual-channel processing” technology that allows a single system to support multiple operating systems and users more efficiently than software-based virtualization. The Carnegie Mellon University lablet’s area of concentration is software for widely distributed storage systems, with emphasis on interactive searching of massive archives of non-indexed data, and the acceleration and enhanced accuracy of searches via embedded processors.

The Carnegie Mellon Intel lablet is investigating software for widely distributed storage systems. Researchers working with Seagate Technology LLC are trying to enable interactive searching of terabyte-size collections of nonindexed data.

As part of that effort, researchers are studying how to speed up searches and make them more accurate by embedding processors either close to or on storage devices so they can examine and discard irrelevant data close to the source.