Brain-Like Chip May Solve Computers' Big Problem: Energy
Image: Kwabena Boahen/Stanford University
Kwabena Boahen’s love affair with digital computers began and ended in 1981, when he was 16.
Boahen lived outside the city of Accra in the West African nation of Ghana. His family’s sprawling block house stood in a quiet field of mango and banana trees. One afternoon Boahen’s father rolled down the driveway with a surprise in the trunk of his Peugeot: a RadioShack TRS-80—the family’s first computer—purchased in England.
Young Boahen parked the machine at a desk on the porch, where he usually dismantled radios and built air guns out of PVC pipe. He plugged the computer into a TV set to provide a screen and a cassette recorder so he could store programs on tapes, and soon he was programming it to play Ping-Pong. But as he read about the electronics that made it and all other digital computers work, he soured on the toy.
Moving the Ping-Pong ball just one pixel across the screen required thousands of 1s and 0s, generated by transistors in the computer’s processor that were switching open and shut 2.5 million times per second. Boahen had expected to find elegance at the heart of his new computer. Instead he found a Lilliputian bureaucracy of binary code. “I was totally disgusted,” he recalls. “It was so brute force.” That disillusionment inspired a dream of a better solution, a vision that would eventually guide his career.
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Boahen has since crossed the Atlantic Ocean and become a prominent scientist at Stanford University in California. There he is working to create a computer that will fulfill his boyhood vision—a new kind of computer, based not on the regimented order of traditional silicon chips but on the organized chaos of the human brain. Designing this machine will mean rejecting everything that we have learned over the past 50 years about building computers. But it might be exactly what we need to keep the information revolution going for another 50.
The human brain runs on only about 20 watts of power, equal to the dim light behind the pickle jar in your refrigerator. By contrast, the computer on your desk consumes a million times as much energy per calculation. If you wanted to build a robot with a processor as smart as the human brain, it would require 10 to 20 megawatts of electricity. “Ten megawatts is a small hydroelectric plant,” Boahen says dismissively. “We should work on miniaturizing hydroelectric plants so we can put them on the backs of robots.” You would encounter similar problems if you tried to build a medical implant to replace just 1 percent of the neurons in the brain, for use in stroke patients. That implant would consume as much electricity as 200 households and dissipate as much heat as the engine in a Porsche Boxster.
“Energy efficiency isn’t just a matter of elegance. It fundamentally limits what we can do with computers,” Boahen says. Despite the amazing progress in electronics technology—today’s transistors are 1/100,000 the size that they were a half century ago, and computer chips are 10 million times faster—we still have not made meaningful progress on the energy front. And if we do not, we can forget about truly intelligent humanlike machines and all the other dreams of radically more powerful computers.
Getting there, Boahen realized years ago, will require rethinking the fundamental balance between energy, information, and noise. We encounter the trade-offs this involves every time we strain to hear someone speaking through a crackly cell phone connection. We react instinctively by barking more loudly into the phone, trying to overwhelm the static by projecting a stronger signal. Digital computers operate with almost zero noise, but operating at this level of precision consumes a huge amount of power—and therein lies the downfall of modern computing.
In the palm of his hand, Boahen flashes a tiny, iridescent square, a token of his progress in solving that problem. This silicon wafer provides the basis for a new neural supercomputer, called Neurogrid, that he has nearly finished building. The wafer is etched with millions of transistors like the ones in your PC. But beneath that veneer of familiarity hides a radical rethinking of the way engineers do business.
Traditional digital computers depend on millions of transistors opening and closing with near perfection, making an error less than once per 1 trillion times. It is impressive that our computers are so accurate—but that accuracy is a house of cards. A single transistor accidentally flipping can crash a computer or shift a decimal point in your bank account. Engineers ensure that the millions of transistors on a chip behave reliably by slamming them with high voltages—essentially, pumping up the difference between a 1 and a 0 so that random variations in voltage are less likely to make one look like the other. That is a big reason why computers are such power hogs.
Radically improving that efficiency, Boahen says, will involve trade-offs that would horrify a chip designer. Forget about infinitesimal error rates like one in a trillion; the transistors in Neurogrid will crackle with noise, misfiring at rates as high as 1 in 10. “Nobody knows how we’re going to compute with that,” Boahen admits. “The only thing that computes with this kind of crap is the brain.”
It sounds cockamamy, but it is true. Scientists have found that the brain’s 100 billion neurons are surprisingly unreliable. Their synapses fail to fire 30 percent to 90 percent of the time. Yet somehow the brain works. Some scientists even see neural noise as the key to human creativity. Boahen and a small group of scientists around the world hope to copy the brain’s noisy calculations and spawn a new era of energy-efficient, intelligent computing. Neurogrid is the test to see if this approach can succeed.
Most modern supercomputers are the size of a refrigerator and devour $100,000 to $1 million of electricity per year. Boahen’s Neurogrid will fit in a briefcase, run on the equivalent of a few D batteries, and yet, if all goes well, come close to keeping up with these Goliaths.
The problem of computing with noise first occurred to a young neuroscientist named Simon Laughlin three decades ago. Laughlin, then at the Australian National University in Canberra, spent much of 1975 sitting in a black-walled, windowless laboratory with the lights off. The darkness allowed him to study the retinas of blowflies captured from Dumpsters around campus. In hundreds of experiments he glued a living fly to a special plastic platform under a microscope, sunk a wisp-thin electrode into its honeycombed eye, and recorded how its retina responded to beams of light. Laughlin would begin recording at noon and finish after midnight. As he sat in the gloomy lab, watching neural signals dance in green light across an oscilloscope, he noticed something strange.
Each fly neuron’s response to constant light jittered up and down from one millisecond to the next. Those fluctuations showed up at every step in the neurons’ functioning, from the unreliable absorption of light by pigment molecules to the sporadic opening of electricity-conducting proteins called ion channels on the neurons’ surfaces. “I began to realize that noise placed a fundamental limit on the ability of neurons to code information,” Laughlin says.
Susan Payton is the Managing Partner of 
