Chip that was demoed at Jeff Bezos’s secretive tech conference. It could be key to the future of AI.
This chip
was demoed at Jeff Bezos’s secretive tech conference. It could be key to the
future of AI.
The chip on show at
Amazon’s MARS event—alongside karate-chopping robots and Martian bases—is many
times more efficient than conventional silicon chips.
by Will Knight May 1, 2019
Recently, on a dazzling morning in Palm Springs,
California, Vivienne Sze took to a small stage to deliver perhaps the
most nerve-racking presentation of her career.
She knew the subject matter inside-out. She was to tell the
audience about the chips, being developed in her lab at MIT, that promise to
bring powerful artificial intelligence to a multitude of devices where power is
limited, beyond the reach of the vast data centers where most AI computations
take place. However, the event—and the audience—gave Sze pause.
“It was, I guess you’d say, a pretty high-caliber
audience,” Sze recalls with a laugh.
Other MARS speakers would introduce a karate-chopping
robot, drones that flap like large, eerily silent insects, and even optimistic
blueprints for Martian colonies. Sze’s chips might seem more modest; to the
naked eye, they’re indistinguishable from the chips you’d find inside any
electronic device. But they are arguably a lot more important than anything
else on show at the event.
New capabilities
Newly designed chips, like the ones being
developed in Sze’s lab, may be crucial to future progress in AI—including stuff
like the drones and robots found at MARS. Until now, AI software has largely
run on graphical chips, but new hardware could make AI algorithms more
powerful, which would unlock new applications. New AI chips could make
warehouse robots more common or let smartphones create photo-realistic
augmented-reality scenery.
Sze’s chips are both extremely efficient and flexible in
their design, something that is crucial for a field that’s evolving incredibly
quickly.
The microchips are designed to squeeze more out of the
“deep-learning” AI algorithms that have already turned the world upside down.
And in the process, they may inspire those algorithms themselves to evolve. “We
need new hardware because Moore’s law has slowed down,” Sze says, referring to
the axiom coined by Intel cofounder Gordon Moore that predicted that the number
of transistors on a chip will double roughly every 18 months—leading to a
commensurate performance boost in computer power.
The high stakes attached to investing in next-generation AI
chips—and maintaining America’s dominance in chipmaking overall—aren’t lost on
the US government. Sze’s microchips are being developed with funding from a
Defense Advanced Research Projects Agency (DARPA) program meant to help develop
new AI chip designs (see “The out-there AI ideas designed to
keep the US ahead of China”).
But innovation in chipmaking has been spurred mostly by the
emergence of deep learning, a very powerful way for machines to learn to
perform useful tasks. Instead of giving a computer a set of rules to follow, a
machine basically programs itself. Training data is fed into a large, simulated
artificial neural network, which is then tweaked so that it produces the
desired result. With enough training, a deep-learning system can find subtle
and abstract patterns in data. The technique is applied to an ever-growing
array of practical tasks, from face recognition on smartphones to predicting
disease from medical images.
The new chip race
Deep learning is not so reliant on Moore’s
law. Neural nets run many mathematical computations in parallel, so they run
far more effectively on the specialized video-game graphics chips that perform
parallel computations for rendering 3-D imagery. But microchips designed
specifically for the computations that underpin deep learning should be even
more powerful.
The potential for new chip architectures to improve AI has
stirred up a level of entrepreneurial activity that the chip industry hasn’t
seen in decades (see “The Race to Power AI’s Silicon Brains”
and “China has never had a real chip
industry. Making AI chips could change that”).
The real opportunity, says Sze, isn’t building the
most-powerful deep-learning chips possible. Power efficiency is important
because AI also needs to run beyond the reach of large data centers, which
means relying only on the power available on the device itself to run. This is
known as operating on “the edge.”
“AI will be everywhere—and figuring out ways to make things
more energy-efficient will be extremely important,” says Naveen Rao, vice
president of the artificial intelligence products group at Intel.
For example, Sze’s hardware is more efficient partly
because it physically reduces the bottleneck between where data is stored
and where it’s analyzed, but also because it uses clever schemes for reusing
data. Before joining MIT, Sze pioneered this approach for improving the
efficiency of video compression while at Texas Instruments.
Sze’s chip is called Eyeriss. Developed in collaboration
with Joel Emer, a research scientist
at Nvidia and a professor at MIT, it was tested alongside a number of standard
processors to see how it handles a range of different deep-learning algorithms.
By balancing efficiency with flexibility, the new chip achieves performance 10
or even 1,000 times more efficient than existing hardware does, according to a
paper posted online last year.
Rao says the MIT chips are promising, but many factors will
determine whether a new hardware architecture succeeds. One of the most
important factors, he says, is developing software that lets programmers run
code on it. “Making something usable from a compiler standpoint is probably the
single biggest obstacle to adoption,” he says.
Sze’s lab is, in fact, also exploring ways of designing
software so that it better exploits the properties of existing computer chips.
And this work extends beyond just deep learning.
Together with Sertac Karaman, from MIT’s
Department of Aeronautics and Astronautics, Sze developed a low-power chip
called Navion that performs 3-D mapping and navigation incredibly efficiently,
for use on a tiny drone. Crucial to this effort was crafting the chip to
exploit the behavior of navigation-focused algorithms—and designing the
algorithm to make the most of a custom chip. Together with the work on deep
learning, Navion reflects the way AI software and hardware are now starting to
evolve in symbiosis.
Sze’s chips might not be as attention-grabbing as a
flapping drone, but the fact that they were showcased at MARS offers some sense
of how important her technology—and innovation in silicon more generally—will
be for the future of AI. After her presentation, Sze says, some
of the other MARS speakers expressed an interest in finding out more. “People
found a lot of important use cases,” she says.
In other words, expect the eye-catching robots and
drones at the next MARS conference to come with something rather special hidden
inside.
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