ABOVE: All-optical deep learning uses 3-D–printed, passive optical components to implement complex functions at the speed of light.
OZCAN LAB @ UCLA
If you want an extremely fast image- or object-recognition system to detect moving items like a missile or cars on the road, a digital camera hooked up to a computer just won’t do, according to electrical engineer Aydogan Ozcan of the University of California, Los Angeles. So, using machine learning, optics tools, and 3-D printing, he and his colleagues have created a system that is more rapid, operates using light and, unlike computers, does not require a power source other than the initial light source and a simple detector. Their results are published today (July 26) in Science.
“This is a very innovative approach to construct a physical artificial neural network made of stacked layers of optical elements,” Demetri Psaltis, a professor of optics and electrical engineering at the ...
