From: Helmholtz-Zentrum Dresden-Rossendorf
December 7, 2020 -- Neural networks are
some of the most important tools in artificial intelligence (AI): they mimic
the operation of the human brain and can reliably recognize texts, language and
images, to name but a few. So far, they run on traditional processors in the form
of adaptive software, but experts are working on an alternative concept, the
"neuromorphic computer." In this case, the brain's switching points
-- the neurons -- are not simulated by software but reconstructed in hardware
components. A team of researchers at the Helmholtz-Zentrum Dresden-Rossendorf
(HZDR) has now demonstrated a new approach to such hardware -- targeted
magnetic waves that are generated and divided in micrometer-sized wafers.
Looking to the future, this could mean that optimization tasks and pattern
recognition could be completed faster and more energy efficiently. The
researchers have presented their results in the journal Physical Review
Letters.
The team based its investigations on a
tiny disc of the magnetic material iron nickel, with a diameter just a few
micrometers wide. A gold ring is placed around this disc: When an alternating
current in the gigahertz range flows through it, it emits microwaves that
excite so-called spin waves in the disc. "The electrons in the iron nickel
exhibit a spin, a sort of whirling on the spot rather like a spinning
top," Helmut Schultheiß, head of the Emmy Noether Group
"Magnonics" at HZDR, explains. "We use the microwave impulses to
throw the electron top slightly off course." The electrons then pass on
this disturbance to their respective neighbors -- which causes a spin wave to
shoot through the material. Information can be transported highly efficiently
in this way without having to move the electrons themselves, which is what
occurs in today's computer chips.
Back in 2019, the Schultheiß group
discovered something remarkable: under certain circumstances, the spin wave
generated in the magnetic vortex can be split into two waves, each with a
reduced frequency. "So-called non-linear effects are responsible for
this," explains Schultheiß's colleague Lukas Körber. "They are only
activated when the irradiated microwave power crosses a certain
threshold." Such behavior suggests spin waves as promising candidates for
artificial neurons because there is an amazing parallel with the workings of
the brain: these neurons also only fire when a certain stimulus threshold has
been crossed.
Microwave decoy
At first, however, the scientists were
unable to control the division of the spin wave very precisely. Körber explains
why: "When we sent the microwave into the disc, there was a time lag
before the spin wave divided into two new waves. And this was difficult to
control." So, the team had to think up a way around the problem, which
they have now described in the Physical Review Letters: In addition to the gold
ring, a small magnetic strip is attached close to the magnetic wafer. A short
microwave signal generates a spin wave in this strip which can interact with
the spin wave in the wafer and thus act as a kind of decoy. The spin wave in
the strip causes the wave in the wafer to divide faster. "A very short
additional signal is sufficient to make the split happen faster," Körber
explains. "This means we can now trigger the process and control the time
lag."
Which also means that, in principle, it
has been proven that spin wave wafers are suitable for artificial hardware
neurons -- they switch similarly to nerve cells in the brain and can be
directly controlled. "The next thing we want to do is build a small network
with our spin wave neurons," Helmut Schultheiß announces. "This
neuromorphic network should then perform simple tasks such as recognizing
straightforward patterns."
Facial recognition and traffic
optimization Pattern recognition is one of the major applications of AI. Facial
recognition on a smartphone, for instance, obviates the necessity for a
password. In order for it to work, a neural network must be trained in advance,
which involves huge computing power and massive amounts of data. Smartphone
manufacturers transfer this network to a special chip that is then integrated
in the cell phone. But the chip has a weakness. It is not adaptive, so cannot
recognize faces wearing a Covid mask, for example.
A neuromorphic computer, on the other
hand, could also deal with situations like this: in contrast to conventional
chips, its components are not hard wired but function like nerve cells in the
brain. "Because of this, a neuromorphic computer could process big volumes
of data at once, just like a human -- and very energy efficiently at
that," Schultheiß enthuses. Apart from pattern recognition, the new type
of computer could also prove useful in another economically relevant field: for
optimization tasks such as high-precision smartphone route planners.
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