All-optical AI device identifies objects at speed of light

All-optical AI device identifies objects at speed of light

Technology News |
By Rich Pell

Created with a 3D printer and comprising a series of polymer layers, the physical artificial neural network works using light that travels through it. According to the researchers, the device can analyze large volumes of data and identify objects at the actual speed of light, and could have applications in medicine, robotics, and security.

Unlike devices that use cameras or optical sensors to “see” an object, say the researchers, their device – called a “diffractive deep neural network” – uses the light bouncing from the object itself to identify it in as little time as it would take for a computer to simply “see” the object. The device does not need advanced computing programs to process an image of an object to decide what the object is, and the device consumes no energy because it only uses diffraction of light.

New technologies based on the device could be used to speed up data-intensive tasks that involve sorting and identifying objects, say the researchers. For example, a driverless car using the technology could react instantaneously — even faster than it does using current technology — to a stop sign, “reading” the sign as soon as the light from the sign hits it.

Current driverless technology requires having to “wait” for a car’s camera to image the object and then use its computers to figure out what the object is. Technology based on the invention could also be used in microscopic imaging and medicine, say the researchers, for example, to sort through millions of cells for signs of disease.

“This work opens up fundamentally new opportunities to use an artificial intelligence-based passive device to instantaneously analyze data, images, and classify objects,” says Aydogan Ozcan, the study”s lead investigator and the UCLA Chancellor’s Professor of Electrical and Computer Engineering. “This optical artificial neural network device is intuitively modeled on how the brain processes information. It could be scaled up to enable new camera designs and unique optical components that work passively in medical technologies, robotics, security or any application where image and video data are essential.”

To create the artificial neural network, the researchers began with a computer-simulated design, and then used a 3D printer to create very thin, 8 centimeter-square polymer wafers. Each wafer has uneven surfaces, which help diffract light coming from the object in different directions.

While opaque to the eye, the layers allow submillimeter-wavelength terahertz frequencies of light to travel through them. Each layer is composed of tens of thousands of artificial neurons – in this case, tiny pixels that the light travels through – and together, the series of pixelated layers functions as an “optical network” that shapes how incoming light from the object travels through them.

The network identifies an object because the light coming from the object is mostly diffracted toward a single pixel that is assigned to that type of object. The researchers trained the network using a computer to identify the objects in front of it by learning the pattern of diffracted light each object produces as the light from that object passes through the device.

“This is intuitively like a very complex maze of glass and mirrors,” says Ozcan. “The light enters a diffractive network and bounces around the maze until it exits. The system determines what the object is by where most of the light ends up exiting.”

In experiments, say the researchers, they demonstrated that the device could accurately identify handwritten numbers and items of clothing — both of which are commonly used tests in artificial intelligence studies. To do that, they placed images in front of a terahertz light source and let the device “see” those images through optical diffraction.

They also trained the device to act as a lens that projects the image of an object placed in front of the optical network to the other side of it — much like how a typical camera lens works, but using artificial intelligence instead of physics.

Because its components can be created by a 3D printer, say the researchers, the artificial neural network can be made with larger and additional layers, resulting in a device with hundreds of millions of artificial neurons. Those bigger devices could identify many more objects at the same time or perform more complex data analysis. Further, the components can be made inexpensively — the device created by the UCLA team could be reproduced for less than $50, they say.

While the current study used light in the terahertz frequencies, the researchers say it would also be possible to create neural networks that use visible, infrared, or other frequencies of light, and such a network could also be made using lithography or other printing techniques. For more, see “All-optical machine learning using diffractive deep neural networks.”

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