Neuro-inspired computing seeks to develop algorithms that would run on computers that function more like a brain than a conventional computer. “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, Sandia’s cognitive sciences manager.
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 climate-modeling world faces and optimizing renewable-energy systems such as wind-turbine rotors or more efficiently harvesting solar energy, “looking at what’s going where and for what reason,” said microsystems researcher Murat Okandan (in Sandia’s MEMS* Technologies Dept.).
“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,” 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.
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. Each neuron in a neural structure can have connections coming in from about 10,000 neurons, which in turn can connect to 10,000 other neurons in a dynamic way. Conventional computer transistors, on the other hand, connect on average to four other transistors in a static pattern. “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.
He estimates a project dedicated to brain-inspired computing will develop early examples of a new architecture in the first several years, but said higher levels of complexity could take decades, even with the many efforts around the world working toward the same goal.
For more information, visit the 2014 Neuro-Inspired Computational Elements Workshop website.
Read the Sandia news release.
* micro electro-mechanical systems