Giving Computers A Better Brain

A campus research team is using photonic integrated circuits such as these to design neural network technologies.

Next-generation computing systems modelled after the human
brain's information processing capability and energy efficiency are becoming a
reality through work by Dhireesha Kudithipudi.

Her research team focuses on brain- inspired computing, a
combination of neuro-science, nanotechnology and intelligent system design, to
build computing systems that can assess and integrate ever-larger quantities of

Brain-inspired computing is a sub-field of artificial
intelligence where the physical, neural network architecture and its complex
processing mechanisms are inspired by how the brain can recognize patterns and
retain information over time.

Today's systems have the potential to do this, Kudithipudi
explained, but require a more robust network architecture to acquire, manage
and assess data from multiple streams. The brain's ability to process multiple
concepts and its power efficiency and resiliency are remarkable characteristics
of evolutionary design, and studying the brain is an ideal model for computer information

“Neuroscientists are attempting to under-stand the
full-scale, functional models of the brain, yet nobody has a complete picture
of how it works,” said Kudithipudi, a professor of computer engineering and
co-lead of the brain-inspired computing pillar of the Center for Human-Aware
Artificial Intelligence. “This is what makes this research area challenging and
exciting. New discoveries are made every day that are shaping a new paradigm of
intelligent computer architectures.”

In a new NSF-funded project, Kudithipudi will be part of a
campus research team using photonic integrated circuits to design neural
network technologies to improve speed and address energy consumption. Work on
that project can have an impact as RIT continues its contributions to AIM
Photonics, the national manufacturing initiative.

In order to construct the neural networks for photonic
chips, the team will build upon known capabilities of electronics to overcome
the challenges of establishing better memory and amplification. This hybrid
approach, where electronics and photonics would be integrated together, enables
solutions to improve photonic chips.

“There are new application domains that are evolving where
AI is deployed,” said Kudithipudi. “There is a convergence of several fields
that can make AI very successful today.”

AI in the classroom

There are about 40 faculty-researchers in 27 lab groups
across the RIT campus involved in courses and research using artificial
intelligence. Examples of courses include Deep Learning for Vision, Machine
Intelligence, Brain-inspired Computing and Big Data Analytics.

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