As reported by MIT Technology Review: There isn’t much space between your ears, but what’s in there can do
many things that a computer of the same size never could. Your brain is
also vastly more energy efficient at interpreting the world visually or
understanding speech than any computer system.
That’s why academic
and corporate labs have been experimenting with “neuromorphic” chips
modeled on features seen in brains. These chips have networks of
“neurons” that communicate in spikes of electricity (see “Thinking in Silicon”).
They can be significantly more energy-efficient than conventional
chips, and some can even automatically reprogram themselves to learn new
skills.
Now a neuromorphic chip has been untethered from the lab
bench, and tested in a tiny drone aircraft that weighs less than 100
grams.
In the experiment, the prototype chip, with 576 silicon
neurons, took in data from the aircraft’s optical, ultrasound, and
infrared sensors as it flew between three different rooms.
The
first time the drone was flown into each room, the unique pattern of
incoming sensor data from the walls, furniture, and other objects caused
a pattern of electrical activity in the neurons that the chip had never
experienced before.
That triggered it to report that it was in a new
space, and also caused the ways its neurons connected to one another to
change, in a crude mimic of learning in a real brain. Those changes
meant that next time the craft entered the same room, it recognized it
and signaled as such.
The chip involved is far from ready for
practical deployment, but the test offers empirical support for the
ideas that have motivated research into neuromorphic chips, says Narayan
Srinivasa, who leads HRL’s Center for Neural and Emergent Systems.
“This shows it is possible to do learning literally on the fly, while
under very strict size, weight, and power constraints,” he says.
The drone, custom built for the test by drone-maker company Aerovironment,
based in Monrovia, California, is six inches square, 1.5 inches high,
and weighs only 93 grams, including the battery. HRL’s chip made up just
18 grams of the craft’s weight, and used only 50 milliwatts of power.
That wouldn’t be nearly enough for a conventional computer to run
software that could learn to recognize rooms, says Srinivasa.
The flight test was a challenge set by the Pentagon research agency DARPA as part of a project
under which it has funded HRL, IBM, and others to work on neuromorphic
chips. One motivation is the hope that neuromorphic chips might make it
possible for military drones to make sense of video and sensor data for
themselves, instead of always having to beam it down to earth for
analysis by computers or humans.
Prototypes made under DARPA’s program—like HRL’s—have delivered
promising results, but much work remains before such technology can
perform useful work, says Vishal Saxena, an assistant professor working on neuromorphic chips
at Boise State University. “The biggest challenge is identifying what
the applications will be and developing robust algorithms,” he says.
Researchers
also face a chicken-and-egg scenario, with chips being developed
without much idea of what algorithms they will run and algorithms being
written without a firm idea of what chip designs will become
established. At the same time, neuroscientists are still discovering new
things about how networks of real brain cells work on information.
“There’s a lot of work to be done collectively between circuit and
algorithm experts and the neuroscience community,” says Saxena.
Still,
HRL’s owners, GM and Boeing, are already considering how they might
commercialize the technology, says Srinivasa. One option could be to use
neuromorphic chips to build a degree of intelligence into the sensors
increasingly found in cars, planes, and other systems.
No comments:
Post a Comment