Brain-based computing chips not just for AI anymore

Date:
March 10, 2022
Source:
DOE/Sandia National Laboratories
Summary:
With the insertion of a little math, researchers have shown
that neuromorphic computers, which synthetically replicate
the brain's logic, can solve more complex problems than those
posed by artificial intelligence and may even earn a place in
high-performance computing.

Neuromorphic simulations employing random walks can track X-rays
passing through bone and soft tissue, disease passing through a
population, information flowing through social networks and the
movements of financial markets.



FULL STORY ==========================================================================
With the insertion of a little math, Sandia National Laboratories
researchers have shown that neuromorphic computers, which synthetically replicate the brain's logic, can solve more complex problems than
those posed by artificial intelligence and may even earn a place in high-performance computing.


==========================================================================
The findings, detailed in a recent article in the journal Nature
Electronics, show that neuromorphic simulations employing the statistical method called random walks can track X-rays passing through bone and
soft tissue, disease passing through a population, information flowing
through social networks and the movements of financial markets, among
other uses, said Sandia theoretical neuroscientist and lead researcher
James Bradley Aimone.

"Basically, we have shown that neuromorphic hardware can yield
computational advantages relevant to many applications, not just
artificial intelligence to which it's obviously kin," said Aimone. "Newly discovered applications range from radiation transport and molecular simulations to computational finance, biology modeling and particle
physics." In optimal cases, neuromorphic computers will solve problems
faster and use less energy than conventional computing, he said.

The bold assertions should be of interest to the high-performance
computing community because finding capabilities to solve statistical
problems is of increasing concern, Aimone said.

"These problems aren't really well-suited for GPUs [graphics processing
units], which is what future exascale systems are likely going to rely
on," Aimone said. "What's exciting is that no one really has looked at neuromorphic computing for these types of applications before." Sandia engineer and paper author Brian Franke said, "The natural randomness of
the processes you list will make them inefficient when directly mapped
onto vector processors like GPUs on next-generation computational efforts.

Meanwhile, neuromorphic architectures are an intriguing and radically
different alternative for particle simulation that may lead to a scalable
and energy- efficient approach for solving problems of interest to us."


========================================================================== Franke models photon and electron radiation to understand their effects
on components.

The team successfully applied neuromorphic-computing algorithms to
model random walks of gaseous molecules diffusing through a barrier,
a basic chemistry problem, using the 50-million-chip Loihi platform
Sandia received approximately a year and a half ago from Intel Corp.,
said Aimone. "Then we showed that our algorithm can be extended to more sophisticated diffusion processes useful in a range of applications."
The claims are not meant to challenge the primacy of standard computing
methods used to run utilities, desktops and phones. "There are, however,
areas in which the combination of computing speed and lower energy costs
may make neuromorphic computing the ultimately desirable choice," he said.

Unlike the difficulties posed by adding qubits to quantum computers
-- another interesting method of moving beyond the limitations of
conventional computing - - chips containing artificial neurons are cheap
and easy to install, Aimone said.

There can still be a high cost for moving data on or off the neurochip processor. "As you collect more, it slows down the system, and eventually
it won't run at all," said Sandia mathematician and paper author William Severa.

"But we overcame this by configuring a small group of neurons that
effectively computed summary statistics, and we output those summaries
instead of the raw data." Severa wrote several of the experiment's
algorithms.



==========================================================================
Like the brain, neuromorphic computing works by electrifying small
pin-like structures, adding tiny charges emitted from surrounding
sensors until a certain electrical level is reached. Then the pin, like
a biological neuron, flashes a tiny electrical burst, an action known
as spiking. Unlike the metronomical regularity with which information
is passed along in conventional computers, said Aimone, the artificial
neurons of neuromorphic computing flash irregularly, as biological ones
do in the brain, and so may take longer to transmit information. But
because the process only depletes energies from sensors and neurons if
they contribute data, it requires less energy than formal computing,
which must poll every processor whether contributing or not.

The conceptually bio-based process has another advantage: Its computing
and memory components exist in the same structure, while conventional
computing uses up energy by distant transfer between these two
functions. The slow reaction time of the artificial neurons initially
may slow down its solutions, but this factor disappears as the number
of neurons is increased so more information is available in the same
time period to be totaled, said Aimone.

The process begins by using a Markov chain -- a mathematical construct
where, like a Monopoly gameboard, the next outcome depends only on the
current state and not the history of all previous states. That randomness contrasts, said Sandia mathematician and paper author Darby Smith, with
most linked events. For example, he said, the number of days a patient
must remain in the hospital are at least partially determined by the
preceding length of stay.

Beginning with the Markov random basis, the researchers used Monte Carlo simulations, a fundamental computational tool, to run a series of random
walks that attempt to cover as many routes as possible.

"Monte Carlo algorithms are a natural solution method for radiation
transport problems," said Franke. "Particles are simulated in a process
that mirrors the physical process." The energy of each walk was recorded
as a single energy spike by an artificial neuron reading the result of
each walk in turn. "This neural net is more energy efficient in sum than recording each moment of each walk, as ordinary computing must do. This partially accounts for the speed and efficiency of the neuromorphic
process," said Aimone. More chips will help the process move faster
using the same amount of energy, he said.

The next version of Loihi, said Sandia researcher Craig Vineyard, will
increase its current chip scale from 128,000 neurons per chip to up to
one million.

Larger scale systems then combine multiple chips to a board.

"Perhaps it makes sense that a technology like Loihi may find its way
into a future high-performance computing platform," said Aimone. "This
could help make HPC much more energy efficient, climate-friendly and
just all around more affordable." The work was funded under the NNSA
Advanced Simulation and Computing program and Sandia's Laboratory Directed Research and Development program.

Video: https://youtu.be/O_8E26axKFY

========================================================================== Story Source: Materials provided by
DOE/Sandia_National_Laboratories. Note: Content may be edited for style
and length.


========================================================================== Journal Reference:
1. J. Darby Smith, Aaron J. Hill, Leah E. Reeder, Brian C. Franke,
Richard
B. Lehoucq, Ojas Parekh, William Severa, James
B. Aimone. Neuromorphic scaling advantages for energy-efficient
random walk computations. Nature Electronics, 2022; 5 (2): 102 DOI:
10.1038/s41928-021-00705-7 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/03/220310170837.htm

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