UCLA Scientists Improve Device That Processes Information at the Speed of Light

The new study focuses on optical neural networks, which could lead to intelligent cameras.

A team of scientists at the University of California Los Angeles (UCLA) has improved their previous work on a design of an optical neural network. What this means is that this device can now recognize objects or a process at the speed of light. 

Much like our human brain, on which the device is based on, it could improve autonomous cars. For example, it could do this by allowing them to make decisions more quickly and using less power than computer-based systems. 

Their study was published in the peer-reviewed journal Advanced Photonics on Monday this week.

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How this device could impact our day to day lives 

By using parallelization and scalability of optical-based computational systems, the device could create intelligent camera structures that put information together simply from the patterns of light that run through a 3D-engineered material structure. 

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UCLA Scientists Improve Device That Processes Information at the Speed of Light
Operation principles of a differential diffractive optical neural network. Source: UCLA

In turn, this could be used in self-driving car systems or robots, as their decision-making process would become near-instantaneous and would end up using less power to do so. 

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The cars or robots would identify objects much more quickly, and make better and more effective decisions. 

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Win-win. 

How does the device work?

The system combined 3D-printed uneven layers that transmit incoming light. 

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Behind these layers are a number of light detectors individually allocated in a computer that deduce what the input object is, through seeing where the most light is coming from. 

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UCLA Scientists Improve Device That Processes Information at the Speed of Light
The network, composed of a series of polymer layers, works using light that travels through it. Each layer is 8 centimeters square. (from August 2, 2018). Source: UCLA

What the team has improved upon is adding a second group of detectors, which hugely increases the device's accuracy. It's as though you go from weighing stones one by one in your hand to see what difference in weight they may have, to holding a stone in each hand and comparing that way. It has "helped UCLA researchers improve their prediction accuracy for unknown objects that were seen by their optical neural network."

Principal investigator of the research, and the Chancellor’s Professor of Electrical and Computer Engineering at UCLA, Aydogan Ozcan, said: "This advance could enable task-specific smart cameras that perform computation on a scene using only photons and light-matter interaction, making it extremely fast and power efficient."

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