The brain’s ability to quickly visually interpret our environment requires such an enormous number of computations it is pretty amazing that it accomplishes this feat so quickly and with seemingly little effort. Coming up with a computer-driven system that can mimic the human brain in visually recognizing objects, however, has proven difficult, but now Euginio Culurciello of Yale’s School of Engineering & Applied Sciences has developed a supercomputer based on the human visual system that operates more quickly and efficiently than ever before.

Dubbed NeuFlow, the system takes its inspiration from the mammalian visual system, mimicking its neural network to quickly interpret the world around it. The system uses complex vision algorithms developed by Yann LeCun at New York University to run large neural networks for synthetic vision applications. It is extremely efficient, simultaneously running more than 100 billion operations per second using only a few watts to accomplish what it takes desktop computers with multiple graphic processors more than 300 watts to achieve.

“One of our first prototypes of this system is already capable of outperforming graphic processors on vision tasks,” Culurciello said.

One potential application for the system that Culurciello and LeCun are focusing on is a system that would allow cars to drive themselves. In order to be able to recognize the various objects encountered on the road – such as other cars, people, stoplights, sidewalks, not to mention the road itself –NeuFlow processes tens of megapixel images in real time.

The algorithm used by the system employs temporal-difference image sensors to recognize objects and people’s postures in real time. It works with any regular off-the-shelf camera and image sensor array and is lightweight enough to be implemented in embedded platforms, sensor networks and mobile phones.

Culurciello embedded the supercomputer on a single chip, making the system much smaller, yet more powerful and efficient, than full-scale computers. “The complete system is going to be no bigger than a wallet, so it could easily be embedded in cars and other places,” Culurciello said.

Beyond the autonomous car navigation, the system could be used to improve robot navigation into dangerous or difficult-to-reach locations, to provide 360-degree synthetic vision for soldiers in combat situations, or in assisted living situations where it could be used to monitor motion and call for help should an elderly person fall, for example.

Culurciello presented a report on NeuFlow on Sept. 15 at the High Performance Embedded Computing (HPEC) workshop in Boston, Mass.

Overview of the NeuFlow system: