Computers continue to pale in comparison to the brain’s sophistication. The cortex of a mouse, for instance, operates 9,000 times faster than a personal computer simulation of its functions. Not only is the personal computer (PC) slower, but it takes 40,000 times more power to run, writes Kwabena Boahen, associate professor of bioengineering at Stanford, in an article for the Proceedings of the IEEE.
“From a pure energy perspective, the brain is hard to match,” says Boahen, whose article surveys how “neuromorphic” researchers in the United States and Europe are using silicon and software to build electronic systems that mimic neurons and synapses.
Boahen and his team have developed Neurogrid, a circuit board consisting of 16 custom-designed “Neurocore” chips. Together these 16 chips can simulate 1 million neurons and billions of synaptic connections. The team designed these chips with power efficiency in mind. Their strategy was to enable certain synapses to share hardware circuits. The result was Neurogrid–a device about the size of an iPad that can simulate orders of magnitude more neurons and synapses than other brain mimics on the power it takes to run a tablet computer.
The National Institutes of Health funded development of this million-neuron prototype with a five-year Pioneer Award. Now Boahen stands ready for the next steps–lowering costs and creating compiler software that would enable engineers and computer scientists with no knowledge of neuroscience to solve problems–such as controlling a humanoid robot–using Neurogrid.
Its speed and low power characteristics make Neurogrid ideal for more than just modeling the human brain. Boahen is working with other Stanford scientists to develop prosthetic limbs for paralyzed people that would be controlled by a Neurocore-like chip.
But much work lies ahead. Each of the current million-neuron Neurogrid circuit boards cost about $40,000. Boahen believes dramatic cost reductions are possible. Neurogrid is based on 16 Neurocores, each of which supports 65,536 neurons. Those chips were made using 15-year-old fabrication technologies.
By switching to modern manufacturing processes and fabricating the chips in large volumes, he could cut a Neurocore’s cost 100-fold – suggesting a million-neuron board for $400 a copy. With that cheaper hardware and compiler software to make it easy to configure, these neuromorphic systems could find numerous applications.
For instance, a chip as fast and efficient as the human brain could drive prosthetic limbs with the speed and complexity of our own actions – but without being tethered to a power source. Krishna Shenoy, an electrical engineering professor at Stanford and Boahen’s neighbor at the interdisciplinary Bio-X center, is developing ways of reading brain signals to understand movement. Boahen envisions a Neurocore-like chip that could be implanted in a paralyzed person’s brain, interpreting those intended movements and translating them to commands for prosthetic limbs without overheating the brain.
A small prosthetic arm in Boahen’s lab is currently controlled by Neurogrid to execute movement commands in real time. For now it doesn’t look like much, but its simple levers and joints hold hope for robotic limbs of the future.
In his article, Boahen notes that Neurogrid is about 100,000 times more energy efficient than a personal computer simulation of 1 million neurons. Yet it is an energy hog compared to our biological CPU. “The human brain, with 80,000 times more neurons than Neurogrid, consumes only three times as much power,” Boahen writes. “Achieving this level of energy efficiency while offering greater configurability and scale is the ultimate challenge neuromorphic engineers face.”
