Via IEEE Spectrum
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Photo: University of Michigan and TSMC
One of several varieties of University of Michigan micromotes. This one incorporates 1 megabyte of flash memory.
Computer scientist David Blaauw
pulls a small plastic box from his bag. He carefully uses his
fingernail to pick up the tiny black speck inside and place it on the
hotel café table. At 1 cubic millimeter, this is one of a line of the
world’s smallest computers. I had to be careful not to cough or sneeze
lest it blow away and be swept into the trash.
Blaauw and his colleague Dennis Sylvester,
both IEEE Fellows and computer scientists at the University of
Michigan, were in San Francisco this week to present 10 papers related
to these “micromote” computers at the IEEE International Solid-State Circuits Conference (ISSCC). They’ve been presenting different variations on the tiny devices for a few years.
Their broader goal is to make smarter, smaller sensors for medical
devices and the Internet of Things—sensors that can do more with less
energy. Many of the microphones, cameras, and other sensors that make up
the eyes and ears of smart devices are always on alert, and frequently
beam personal data into the cloud because they can’t analyze it
themselves. Some have predicted that by 2035, there will be 1 trillion such devices.
“If you’ve got a trillion devices producing readings constantly, we’re
going to drown in data,” says Blaauw. By developing tiny,
energy-efficient computing sensors that can do analysis on board, Blaauw
and Sylvester hope to make these devices more secure, while also saving
energy.
At the conference, they described micromote designs that use only a
few nanowatts of power to perform tasks such as distinguishing the sound
of a passing car and measuring temperature and light levels. They
showed off a compact radio that can send data from the small computers
to receivers 20 meters away—a considerable boost compared to the
50-centimeter range they reported last year at ISSCC. They also described their work with TSMC
(Taiwan Semiconductor Manufacturing Company) on embedding flash memory
into the devices, and a project to bring on board dedicated, low-power
hardware for running artificial intelligence algorithms called deep
neural networks.
Blaauw and Sylvester say they take a holistic approach to adding new
features without ramping up power consumption. “There’s no one answer”
to how the group does it, says Sylvester. If anything, it’s “smart
circuit design,” Blaauw adds. (They pass ideas back and forth rapidly,
not finishing each other’s sentences but something close to it.)
The memory research is a good example of how the right trade-offs can
improve performance, says Sylvester. Previous versions of the
micromotes used 8 kilobytes of SRAM (static RAM), which makes for a
pretty low-performance computer. To record video and sound, the tiny
computers need more memory. So the group worked with TSMC to bring flash
memory on board. Now they can make tiny computers with 1 megabyte of
storage.
Flash can store more data in a smaller footprint than SRAM, but it
takes a big burst of power to write to the memory. With TSMC, the group
designed a new memory array that uses a more efficient charge pump for
the writing process. The memory arrays are a bit less dense than TSMC’s
commercial products, for example, but still much better than SRAM. “We
were able to get huge gains with small trade-offs,” says Sylvester.
Another micromote they presented at the ISSCC incorporates a deep-learning
processor that can operate a neural network while using just 288
microwatts. Neural networks are artificial intelligence algorithms that
perform well at tasks such as face and voice recognition. They typically
demand both large memory banks and intense processing power, and so
they’re usually run on banks of servers often powered by advanced GPUs.
Some researchers have been trying to lessen the size and power demands
of deep-learning AI with dedicated hardware that’s specially designed to
run these algorithms. But even those processors still use over 50
milliwatts of power—far too much for a micromote. The Michigan group
brought down the power requirements by redesigning the chip
architecture, for example by situating four processing elements within
the memory (in this case, SRAM) to minimize data movement.
The idea is to bring neural networks to the Internet of Things. “A
lot of motion detection cameras take pictures of branches moving in the
wind—that’s not very helpful,” says Blaauw. Security cameras and other
connected devices are not smart enough to tell the difference between a
burglar and a tree, so they waste energy sending uninteresting footage
to the cloud for analysis. Onboard deep-learning processors could make
better decisions, but only if they don’t use too much power. The
Michigan group imagine that deep-learning processors could be integrated
into many other Internet-connected things besides security systems. For
example, an HVAC system could decide to turn the air-conditioning down
if it sees multiple people putting on their coats.
After demonstrating many variations on these micromotes in an
academic setting, the Michigan group hopes they will be ready for market
in a few years. Blaauw and Sylvester say their startup company, CubeWorks,
is currently prototyping devices and researching markets. The company
was quietly incorporated in late 2013. Last October, Intel Capital announced they had invested an undisclosed amount in the tiny computer company.