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Researchers Aim for Self-Learning Chips

09 October 2013

Carnegie Mellon University's Center for Silicon System Implementation (CSSI) has received a $2.6 million grant from the National Science Foundation (NSF) to develop next-generation chips that holistically integrate the management of a chip's resources using machine learning.

"This grant will help us give silicon chips the ability to learn more from their own big data so they can change their mission to better match the needs of the user," said Shawn Blanton, head of the CSSI and a professor of electrical and computer engineering at Carnegie Mellon, in a statement.

The three-year initiative is to fund the research of eight faculty members from Carnegie Mellon's Robotics Institute. Collectively, the faculty members have expertise in circuit design, computer architecture, IC chip manufacturing, cybersecurity and statistical learning.

The impetus for the research work is to see how a new age of ubiquitous computing devices can better manage its internal resources and adjust for optimal operation.

To that end, Carnegie Mellon researchers have launched the Statistical Learning in Chip (SLIC) project, which seeks to holistically integrate the management of a silicon chip's resources using machine learning.

"A SLIC-enabled integrated circuit could continually monitor its own performance and conditions to ensure that it is always operating at optimal efficiency," Blanton said.

According to Blanton, SLIC technology also can be applied to critical infrastructure supervisory control and data systems, such as the electric power grid, air traffic control and telecommunications infrastructure.

CSSI, founded in 2000, has more than 80 graduate students working in or across various areas that including manufacturing, circuits, systems and emerging technologies and builds on over 25 years of experience in the electronic design automation industry that began with a sustaining grant from industry consortium Semiconductor Research Corporation.

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