Millimeter-Scale Computers: Now With Deep Learning Neural Networks on Board

University of Michigan micro-motes aim to make the Internet of Things smarter without consuming more power

Photo: University of Michigan and TSMC
One of several varieties of University of Michigan micro motes. 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 one 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 ten papers related to these “micro mote” 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 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 micro mote designs that use only a few nanowatts of power to perform tasks such as distinguish 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 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 tradeoffs can improve performance, says Sylvester. Previous versions of the micro motes used 8 kilobytes of SRAM, 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 micro mote 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 micro mote. 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. On-board deep-learning processors could make better decisions, but only if they don’t use too much power. The Michigan group imagine deep-learning processors could be integrated into many other internet-connected things besides security systems. For example, an HVAC systems could decide to turn the air conditioning down if they see multiple people putting on their coats.

After demonstrating many variations on these micro motes in an academic setting, the Michigan group hopes they will be ready for market in a few years. Blaauw and Sylvester say their start-up 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. 

Millimeter-Scale Computers: Now With Deep Learning Neural Networks on Board