Smarter Cars: Auto Makers Experiment With Chips That Think Like Humans
By Sara Castellanos
Experimental computer chips that try to mimic the way human
brains work could accelerate the use of voice and gesture commands
in automobiles, researchers at Intel Corp. and Accenture PLC
The cutting-edge technique, known as neuromorphic computing,
could use significantly less energy than traditional computer- and
graphic-processing units that connect wirelessly to a car via the
cloud. Today's cars don't have the AI capabilities to recognize
many speech and gesture commands, in part because of the energy
requirements necessary to make those functions work.
Car makers are recognizing the need for AI methods that consume
less energy, which is one reason why neuromorphic computing can be
beneficial, said Tim Shea, technology researcher at Accenture Labs.
"They're already running up against limitations of [current chips]
not being scalable enough," he said.
German auto maker Mercedes-Benz AG announced last week it had
joined the Intel Neuromorphic Research Community to explore how
neuromorphic chips could help increase energy efficiency, speed and
accuracy for vehicle-related AI uses.
"With the knowledge we'll gain, we want to achieve a significant
boost for our AI applications in and around our vehicles," said
Jasmin Eichler, director of future technologies at Mercedes-Benz,
in a statement.
Intel's neuromorphic chips could begin selling commercially
within five years, according to Mike Davies, director of Intel's
Neuromorphic Computing Lab.
Applications powered by neuromorphic chips inside a car could
help recognize when a person is shivering and automatically adjust
the temperature, Accenture Labs researchers say. They could also
recognize a voice command to turn on the car or roll down the
window. The chips would be integrated in the car itself and would
not need to connect to the cloud in order to work.
Accenture Labs worked on a neuromorphic computing experiment
this year with an undisclosed car maker. In the experiment, a
neuromorphic chip made by Intel Labs, named Loihi, recognized voice
commands such as "start the engine." The chip consumed 1,000 times
less power and responded 200 milliseconds faster than a standard
GPU, Mr. Shea said.
Intel is among several companies, universities and startups,
such as International Business Machines Corp., SynSense and Applied
Brain Research, that are studying neuromorphic computing. "The
industry is looking for new ways of developing AI systems with much
lower power consumptions," said Alan Priestley, AI technologies
analyst at research firm Gartner Inc.
Energy consumption is an impediment to some AI deployments.
Developing a single AI model, for example, can have a carbon
footprint equivalent to the lifetime emissions of five average U.S.
cars, according to researchers at the University of Massachusetts,
With neuromorphic computing, it is possible to train
machine-learning models using a fraction of the data it takes to
train them on traditional computing hardware. That means the models
learn similarly to the way human babies learn, by seeing an image
or toy once and being able to recognize it forever, The Wall Street
Journal has previously reported.
The technique uses significantly less energy than today's GPUs,
which are one of the main computer chips used for AI systems,
especially neural networks. Neural networks are used in speech
recognition and understanding, as well as computer vision.
Another advantage of the computing technique is that it is
"event-driven, " meaning it is only computing and using energy when
it is activated by an event, such as a voice or gesture command.
"It's not just computing all the time in a uniform way, whether
there's activity or not," said Alex Kass, a fellow and principal
director at Accenture Labs.
Neuromorphic chips can be placed inside cars to do the computing
"at the edge," or inside the car itself, without needing to access
the cloud. That means the AI functions always work, even in areas
with bad connectivity, such as national forests, Accenture
The chips are expected to be the predominant computing
architecture for new, advanced forms of AI deployments by 2025,
according to Gartner. By that year, Gartner predicts the technology
will displace graphics-processing units.
Write to Sara Castellanos at email@example.com
(END) Dow Jones Newswires
December 10, 2020 15:40 ET (20:40 GMT)
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