This story is from the category Artificial Intelligence
Date posted: 27/05/2014
Your brain is incredibly well-suited to handling whatever comes along, plus it’s tough and operates on little energy. Those attributes — dealing with real-world situations, resiliency and energy efficiency — are precisely what might be possible with neuro-inspired computing.
“Today’s computers are wonderful at bookkeeping and solving scientific problems often described by partial differential equations, but they’re horrible at just using common sense, seeing new patterns, dealing with ambiguity and making smart decisions,” said John Wagner, cognitive sciences manager at Sandia National Laboratories.
In contrast, the brain is “proof that you can have a formidable computer that never stops learning, operates on the power of a 20-watt light bulb and can last a hundred years,” he said.
Although brain-inspired computing is in its infancy, Sandia has included it in a long-term research project whose goal is future computer systems. Neuro-inspired computing seeks to develop algorithms that would run on computers that function more like a brain than a conventional computer.
“We’re evaluating what the benefits would be of a system like this and considering what types of devices and architectures would be needed to enable it,” said microsystems researcher Murat Okandan.
Sandia’s facilities and past research make the laboratories a natural for this work: its Microsystems & Engineering Science Applications (MESA) complex, a fabrication facility that can build massively interconnected computational elements; its computer architecture group and its long history of designing and building supercomputers; strong cognitive neurosciences research, with expertise in such areas as brain-inspired algorithms; and its decades of work on nationally important problems, Wagner said.
New technology often is spurred by a particular need. Early conventional computing grew from the need for neutron diffusion simulations and weather prediction. Today, big data problems and remote autonomous and semiautonomous systems need far more computational power and better energy efficiency.
Neuro-inspired computers would be ideal for operating such systems as unmanned aerial vehicles, robots and remote sensors, and solving big data problems, such as those the cyber world faces and analyzing transactions whizzing around the world, “looking at what’s going where and for what reason,” Okandan said.
Such computers would be able to detect patterns and anomalies, sensing what fits and what doesn’t. Perhaps the computer wouldn’t find the entire answer, but could wade through enormous amounts of data to point a human analyst in the right direction, Okandan said.
“If you do conventional computing, you are doing exact computations and exact computations only. If you’re looking at neurocomputation, you are looking at history, or memories in your sort of innate way of looking at them, then making predictions on what’s going to happen next,” he said. “That’s a very different realm.”
Modern computers are largely calculating machines with a central processing unit and memory that stores both a program and data. They take a command from the program and data from the memory to execute the command, one step at a time, no matter how fast they run. Parallel and multicore computers can do more than one thing at a time but still use the same basic approach and remain very far removed from the way the brain routinely handles multiple problems concurrently.
The architecture of neuro-inspired computers would be fundamentally different, uniting processing and storage in a network architecture “so the pieces that are processing the data are the same pieces that are storing the data, and the data will be processed with all nodes functioning concurrently,” Wagner said. “It won’t be a serial step-by-step process; it’ll be this network processing everything all at the same time. So it will be very efficient and very quick.”
Unlike today’s computers, neuro-inspired computers would inherently use the critical notion of time. “The things that you represent are not just static shots, but they are preceded by something and there’s usually something that comes after them,” creating episodic memory that links what happens when. This requires massive interconnectivity and a unique way of encoding information in the activity of the system itself, Okandan said.
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