Intel Labs invests $30 million in cloud and embedded computing

Intel Labs announced two new Intel Science and Technology
Centers (ISTC) hosted at Carnegie Mellon University focused on cloud and
embedded computing research.

These centers represent the next $30 million installment
of Intel’s recently announced 5-year, $100 million ISTC program to increase
university research and accelerate innovation in a handful of key areas.

As with previously announced ISTCs for visual computing
and secure computing, the new centers encourage tighter collaboration between
university thought leaders and Intel. To encourage further collaboration, the
ISTCs use open IP models with results publically available through technical
publications and open-source software releases.

These new ISTCs are expected to open amazing
possibilities,” said Justin Rattner, Intel chief technology officer.

Imagine, for example, future cars equipped with embedded
sensors and microprocessors to constantly collect and analyze traffic and
weather data. That information could be shared and analyzed in the cloud so
that drivers could be provided with suggestions for quicker and safer routes,”
Rattner added.

The ISTC forms a new cloud computing research community
that broadens Intel’s Cloud 2015″ vision with new ideas from top academic
researchers, and includes research that extends and improves on Intel’s
existing cloud computing initiatives.

The center combines top researchers from Carnegie Mellon
University, Georgia Institute of Technology, University of California Berkeley,
Princeton University, and Intel.

The researchers will explore technology that will have
has important future implications for the cloud, including built-in application
optimization, more efficient and effective support of big data analytics on
massive amounts of online data, and making the cloud more distributed and
localized by extending cloud capabilities to the network edge and even to
client devices.

A key area of research is to make it easier for these
everyday devices to continuously collect, analyze and act on useful data from
both sensors and online databases in a way that is timely, scalable and

For example, in cars, this data could be used to
customize in-vehicle entertainment options when specific passengers are
recognized, and provide them better routing, retail, dining, and entertainment
recommendations while on-the-road.

By Team

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