Today’s computing chips contain billions of nano-scale transistors, allowing for fast, high-performance computers, pocket-sized smartphones that far outpace early desktop computers, and an explosion in handheld tablets.
But none of these devices comes close to rivaling the computing capabilities of the human brain.
At least not yet. A Boise State University research team says it could soon change that.
Electrical and computer engineering faculty members Elisa Barney Smith, Kris Campbell and Vishal Saxena are working to develop a new kind of computing architecture that works more like a brain than a traditional digital computer.
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“By mimicking the brain’s billions of interconnections and pattern recognition capabilities, we may ultimately introduce a new paradigm in speed and power, and potentially enable systems that include the ability to learn, adapt and respond to their environment,” says Barney Smith, the principal investigator.
The team members have expertise in machine learning (artificial intelligence), integrated circuit design and memristor devices.
A memristor is a resistor that can be programmed to a new resistance by applying electrical pulses. It remembers its new resistance value once the power is removed.
The project’s success rests on memristors. Memristors were first hypothesized to exist in 1972, but were fully realized as nano-scale devices only in the last decade.
One of the first memristors was built in Campbell’s Boise State lab, thought to be one of only five or six labs worldwide that are up to the task.
The team’s research builds on recent work from scientists who have derived mathematical algorithms to explain the electrical interaction between brain synapses and neurons.
“By employing these models in combination with a new device technology that exhibits similar electrical response to the neural synapses, we will design entirely new computing chips that mimic how the brain processes information,” says Barney Smith.
The researchers say the chips will consume power at an order of magnitude lower than current computing processors, despite the fact that they match existing chips in physical dimensions. This could open the door for ultra low-power electronics intended for applications with scarce energy resources, such as environmental sensors, biomedical implants or devices in space.
The trio’s work is funded by a three-year, $500,000 National Science Foundation grant.