The brain's secret to life-long learning can now come as hardware for artificial intelligence

Date:
February 3, 2022
Source:
Purdue University
Summary:
As companies use more and more data to improve how AI recognizes
images, learns languages and carries out other complex tasks, a
recent article shows a way that computer chips could dynamically
rewire themselves to take in new data like the brain does, helping
AI to keep learning over time.



FULL STORY ==========================================================================
When the human brain learns something new, it adapts. But when artificial intelligence learns something new, it tends to forget information it
already learned.


==========================================================================
As companies use more and more data to improve how AI recognizes images,
learns languages and carries out other complex tasks, a paper publishing
in Science this week shows a way that computer chips could dynamically
rewire themselves to take in new data like the brain does, helping AI
to keep learning over time.

"The brains of living beings can continuously learn throughout their
lifespan.

We have now created an artificial platform for machines to learn
throughout their lifespan," said Shriram Ramanathan, a professor in
Purdue University's School of Materials Engineering who specializes in discovering how materials could mimic the brain to improve computing.

Unlike the brain, which constantly forms new connections between neurons
to enable learning, the circuits on a computer chip don't change. A
circuit that a machine has been using for years isn't any different than
the circuit that was originally built for the machine in a factory.

This is a problem for making AI more portable, such as for autonomous
vehicles or robots in space that would have to make decisions on their
own in isolated environments. If AI could be embedded directly into
hardware rather than just running on software as AI typically does,
these machines would be able to operate more efficiently.

In this study, Ramanathan and his team built a new piece of hardware
that can be reprogrammed on demand through electrical pulses. Ramanathan believes that this adaptability would allow the device to take on all
of the functions that are necessary to build a brain-inspired computer.



==========================================================================
"If we want to build a computer or a machine that is inspired by the
brain, then correspondingly, we want to have the ability to continuously program, reprogram and change the chip," Ramanathan said.

Toward building a brain in chip form The hardware is a small, rectangular device made of a material called perovskite nickelate, which is very
sensitive to hydrogen. Applying electrical pulses at different voltages
allows the device to shuffle a concentration of hydrogen ions in a
matter of nanoseconds, creating states that the researchers found could
be mapped out to corresponding functions in the brain.

When the device has more hydrogen near its center, for example, it can act
as a neuron, a single nerve cell. With less hydrogen at that location,
the device serves as a synapse, a connection between neurons, which is
what the brain uses to store memory in complex neural circuits.

Through simulations of the experimental data, the Purdue team's
collaborators at Santa Clara University and Portland State University
showed that the internal physics of this device creates a dynamic
structure for an artificial neural network that is able to more
efficiently recognize electrocardiogram patterns and digits compared
to static networks. This neural network uses "reservoir computing,"
which explains how different parts of a brain communicate and transfer information.



========================================================================== Researchers from The Pennsylvania State University also demonstrated in
this study that as new problems are presented, a dynamic network can "pick
and choose" which circuits are the best fit for addressing those problems.

Since the team was able to build the device using standard semiconductor- compatible fabrication techniques and operate the device at room
temperature, Ramanathan believes that this technique can be readily
adopted by the semiconductor industry.

"We demonstrated that this device is very robust," said Michael Park,
a Purdue Ph.D. student in materials engineering. "After programming the
device over a million cycles, the reconfiguration of all functions is remarkably reproducible." The researchers are working to demonstrate
these concepts on large-scale test chips that would be used to build a brain-inspired computer.

Experiments at Purdue were conducted at the FLEX Lab and Birck
Nanotechnology Center of Purdue's Discovery Park. The team's collaborators
at Argonne National Laboratory, the University of Illinois, Brookhaven
National Laboratory and the University of Georgia conducted measurements
of the device's properties.

The research was supported by the U.S. Department of Energy Office of
Science, the Air Force Office of Scientific Research and the National
Science Foundation.

========================================================================== Story Source: Materials provided by Purdue_University. Original written
by Kayla Wiles. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Hai-Tian Zhang, Tae Joon Park, A. N. M. Nafiul Islam, Dat
S. J. Tran,
Sukriti Manna, Qi Wang, Sandip Mondal, Haoming Yu, Suvo
Banik, Shaobo Cheng, Hua Zhou, Sampath Gamage, Sayantan
Mahapatra, Yimei Zhu, Yohannes Abate, Nan Jiang, Subramanian
K. R. S. Sankaranarayanan, Abhronil Sengupta, Christof Teuscher,
Shriram Ramanathan. Reconfigurable perovskite nickelate electronics
for artificial intelligence. Science, 2022; 375 (6580): 533 DOI:
10.1126/science.abj7943 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/02/220203160544.htm

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