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Engineers put tens of thousands of artificial brain synapses on a single chip

A close-up view of a new neuromorphic “brain-on-a-chip” that
includes tens of thousands of memristors, or memory transistors:
Credit: Peng Lin

Engineers put tens of thousands of artificial brain snyapses on
a single chip. This design could advance the development of small,
portable AI devices.

MIT engineers have designed a “brain-on-a-chip,” smaller
than a piece of confetti, that is made from tens of thousands of
artificial brain synapses known as memristors — silicon-based
components that mimic the information-transmitting synapses in the
human brain.

The researchers borrowed from principles of metallurgy to
fabricate each memristor from alloys of silver and copper, along
with silicon. When they ran the chip through several visual tasks,
the chip was able to “remember” stored images and reproduce
them many times over, in versions that were crisper and cleaner
compared with existing memristor designs made with unalloyed
elements.

Their results, published today in the journalNature
Nanotechnology, demonstrate a promising new memristor design for
neuromorphic devices — electronics that are based on a new
type of circuit that processes information in a way that mimics the
brain’s neural architecture. Such brain-inspired circuits
could be built into small, portable devices, and would carry out
complex computational tasks that only today’s supercomputers
can handle.

“So far, artificial synapse networks exist as software.
We’re trying to build real neural network hardware for
portable artificial intelligence systems,†says Jeehwan Kim,
associate professor of mechanical engineering at MIT. “Imagine
connecting a neuromorphic device to a camera on your car, and
having it recognize lights and objects and make a decision
immediately, without having to connect to the internet. We hope to
use energy-efficient memristors to do those tasks on-site, in
real-time.â€

Wandering ions

Memristors, or memory transistors, are an essential element in
neuromorphic computing. In a neuromorphic device, a memristor would
serve as the transistor in a circuit, though its workings would
more closely resemble a brain synapse — the junction between
two neurons. The synapse receives signals from one neuron, in the
form of ions, and sends a corresponding signal to the next
neuron.

A transistor in a conventional circuit transmits information by
switching between one of only two values, 0 and 1, and doing so
only when the signal it receives, in the form of an electric
current, is of a particular strength. In contrast, a memristor
would work along a gradient, much like a synapse in the brain. The
signal it produces would vary depending on the strength of the
signal that it receives. This would enable a single memristor to
have many values, and therefore carry out a far wider range of
operations than binary transistors.

Like a brain synapse, a memristor would also be able to
“remember†the value associated with a given current
strength, and produce the exact same signal the next time it
receives a similar current. This could ensure that the answer to a
complex equation, or the visual classification of an object, is
reliable — a feat that normally involves multiple transistors
and capacitors.

Ultimately, scientists envision that memristors would require
far less chip real estate than conventional transistors, enabling
powerful, portable computing devices that do not rely on
supercomputers, or even connections to the Internet.


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Originally published by
Jennifer Chu | MIT News
Office
June 8, 2020