Kwabena Boahen's research on building brain-like computers, or neuromorphic computers, is moving toward creating physical devices that are more energy efficient and robust. Kwabena envisions this technology would be most useful in embedded systems that have extremely tight energy requirements, such as very low-power neural implants or on-board computers in autonomous drones.
While others have built brain-inspired computers, he and his collaborators have developed a five-point prospectus for how to build neuromorphic computers that directly mimic in silicon what the brain does in flesh and blood.
The first two points of the prospectus concern neurons themselves, which unlike computers operate in a mix of digital and analog mode. In their digital mode, neurons send discrete, all-or-nothing signals in the form of electrical spikes, akin to the ones and zeros of digital computers. But they process incoming signals by adding them all up and firing only once a threshold is reached – more akin to a dial than a switch.
That observation led Kwabena to try using transistors in a mixed digital-analog mode. Doing so, it turns out, makes chips both more energy efficient and more robust when the components do fail, as about 4 percent of the smallest transistors are expected to do.
From there, Kwabena builds on neurons' hierarchical organization, distributed computation and feedback loops to create a vision of an even more energy efficient, powerful and robust neuromorphic computer.
Over the last 30 years, Kwabena's lab has actually implemented most of their ideas in physical devices, including Neurogrid, one of the first truly neuromorphic computers. In another two or three years, Boahen said, he expects they will have designed and built computers implementing all of the prospectus's five points.
He states that neuromorphic computers will not replace current computers. The two are complementary.
An additional challenge is getting others, especially chip manufacturers, on board. Kwabena is not the only one thinking about what to do about the end of Moore's law or looking to the brain for ideas. IBM's TrueNorth, for example, takes cues from neural networks to produce a radically more efficient computer architecture. On the other hand, it remains fully digital, and, Kwabena said, 20 times less efficient than Neurogrid would be had it been built with TrueNorth's 28-nanometer transistors.
Professor Kwabena Boahen is also a member of Stanford SystemX and the Stanford Computer Forum. His work was supported by a Director's Pioneer Award and a Transformative Research Award from the U.S. National Institutes of Health and a Long Range Science and Technology Grant from the U.S. Office of Naval Research.
Below, Professor Kwabena Boahen shares his research with Electrical Engineering undergraduates who are in the REU program (Research Experience for Undergrads).
Excerpted from Stanford News, "As Moore's law nears its physical limits, a new generation of brain-like computers comes of age in a Stanford lab"
Image credit (top): Linda A. Cicero / Stanford News Service