Key to resilient energy-efficient AI/machine learning may reside in
human brain

Date:
November 1, 2021
Source:
Penn State
Summary:
A clearer understanding of how a type of brain cell known as
astrocytes function and can be emulated in the physics of hardware
devices, may result in artificial intelligence (AI) and machine
learning that autonomously self-repairs and consumes much less
energy than the technologies currently do, according to researchers.



FULL STORY ==========================================================================
A clearer understanding of how a type of brain cell known as astrocytes function and can be emulated in the physics of hardware devices,
may result in artificial intelligence (AI) and machine learning that autonomously self- repairs and consumes much less energy than the
technologies currently do, according to a team of Penn State researchers.


========================================================================== Astrocytes are named for their star shape and are a type of glial cell,
which are support cells for neurons in the brain. They play a crucial
role in brain functions such as memory, learning, self-repair and synchronization.

"This project stemmed from recent observations in computational
neuroscience, as there has been a lot of effort and understanding of how
the brain works and people are trying to revise the model of simplistic neuron-synapse connections," said Abhronil Sengupta, assistant professor
of electrical engineering and computer science. "It turns out there
is a third component in the brain, the astrocytes, which constitutes a significant section of the cells in the brain, but its role in machine
learning and neuroscience has kind of been overlooked." At the same time,
the AI and machine learning fields are experiencing a boom.

According to the analytics firm Burning Glass Technologies, demand for AI
and machine learning skills is expected to increase by a compound growth
rate of 71% by 2025. However, AI and machine learning faces a challenge
as the use of these technologies increase -- they use a lot of energy.

"An often-underestimated issue of AI and machine learning is the amount of power consumption of these systems," Sengupta said. "A few years back,
for instance, IBM tried to simulate the brain activity of a cat, and
in doing so ended up consuming around a few megawatts of power. And
if we were to just extend this number to simulate brain activity
of a human being on the best possible supercomputer we have today,
the power consumption would be even higher than megawatts." All this
power usage is due to the complex dance of switches, semiconductors
and other mechanical and electrical processes that happens in computer processing, which greatly increases when the processes are as complex as
what AI and machine learning demand. A potential solution is neuromorphic computing, which is computing that mimics brain functions. Neuromorphic computing is of interest to researchers because the human brain has
evolved to use much less energy for its processes than do a computer,
so mimicking those functions would make AI and machine learning a more energy-efficient process.



========================================================================== Another brain function that holds potential for neuromorphic computing
is how the brain can self-repair damaged neurons and synapses.

"Astrocytes play a very crucial role in self-repairing the brain,"
Sengupta said. "When we try to come up with these new device structures,
we try to form a prototype artificial neuromorphic hardware, these
are characterized by a lot of hardware-level faults. So perhaps we can
draw insights from computational neuroscience based on how astrocyte
glial cells are causing self-repair in the brain and use those concepts
to possibly cause self-repair of neuromorphic hardware to repair these
faults." Sengupta's lab primarily works with spintronic devices, a form
of electronics that process information via spinning electrons. The
researchers examine the devices' magnetic structures and how to make
them neuromorphic by mimicking various neural synaptic functions of the
brain in the intrinsic physics of the devices.

This research was part of a study published in January in Frontiers in Neuroscience. That research, in turn, resulted in the study recently
published in the same journal.

"When we started working on the aspects of self-repair in the previous
study, we realized that astrocytes also contribute to temporal information binding," Sengupta said.



========================================================================== Temporal information binding is how the brain can make sense of relations between separate events happening at separate times, and making sense
of these events as a sequence, which is an important function of AI and
machine learning.

"It turns out that the magnetic structures we were working with in
the prior study can be synchronized together through various coupling mechanisms, and we wanted to explore how you can have these synchronized magnetic devices mimic astrocyte-induced phase coupling, going beyond
prior work on solely neuro- synaptic devices," Sengupta said. "We want the intrinsic physics of the devices to mimic the astrocyte phase coupling
that you have in the brain." To better understand how this might be
achieved, the researchers developed neuroscience models, including those
of astrocytes, to understand what aspects of astrocyte functions would
be most relevant for their research. They also developed theoretical
modeling of the potential spintronic devices.

"We needed to understand the device physics and that involved a lot of theoretical modeling of the devices, and then we looked into how we could develop an end-to-end, cross-disciplinary modeling framework including everything from neuroscience models to algorithms to device physics,"
Sengupta said.

Creating such energy-efficient and fault-resilient "astromorphic
computing" could open the door for more sophisticated AI and machine
learning work to be done on power-constrained devices such as smartphones.

"AI and machine learning is revolutionizing the world around us every day,
you see it from your smartphones recognizing pictures of your friends
and family, to machine learning's huge impact on medical diagnosis
for different kinds of diseases," Sengupta said. "At the same time,
studying astrocytes for the type of self-repair and synchronization functionalities they can enable in neuromorphic computing is really in
its infancy. There's a lot of potential opportunities with these kinds
of components." Along with Sengupta, researchers in the first paper
released in January, "On the Self-Repair Role of Astrocytes in STDP
Enabled Unsupervised SNNs," include Mehul Rastogi, former research intern
in the Neuromorphic Computing Lab; Sen Lu, graduate research assistant
in computer science; and Nafiul Islam, graduate research assistant
in electrical engineering. Along with Sengupta, researchers in the
paper released in October, "Emulation of Astrocyte Induced Neural Phase Synchrony in Spin-Orbit Torque Oscillator Neurons," include Umang Garg,
who was a research intern at Penn State during the study, and Kezhou Yang, doctoral candidate in material science.

The National Science Foundation supported this work through the Early
Concept Grant for Exploratory Research program which is specifically
targeted for interdisciplinary high-risk, high-payoff projects with a transformative scope.

========================================================================== Story Source: Materials provided by Penn_State. Original written by
Jamie C. Oberdick. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Umang Garg, Kezhou Yang, Abhronil Sengupta. Emulation of Astrocyte
Induced Neural Phase Synchrony in Spin-Orbit Torque Oscillator
Neurons.

Frontiers in Neuroscience, 2021; 15 DOI: 10.3389/fnins.2021.699632 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/11/211101104858.htm

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