Optical units and control circuits can be etched onto a single chip. (credit: Glenn J.

Asakawa/MIT)
While there are lots of things that artificial intelligence can’t do yet—science being one of them—neural networks are proving themselves increasingly adept at a huge variety of pattern recognition tasks.

These tasks can range anywhere from recognizing specific faces in photos to identifying specific patterns of particle decays in physics.
Right now, neural networks are typically run on regular computers. Unfortunately, those networks are a poor architectural match; neurons combine both memory and calculations into a single unit, while our computers keep those functions separate.

For this reason, some companies are exploring dedicated neural network chips.

But a US-Canadian team is now suggesting an alternative: optical computing. While not as compact or complex as the competing options, optical computing is incredibly quick and energy-efficient.

Optical computing works because static optical elements perform transformations on light that are the equivalent of mathematical transformations.

For example, the authors note, a plain old lens like in a magnifying glass effectively performs a Fourier transform without using any power whatsoever.
It’s also possible to perform things like matrix operations using optical elements.
Speed comes from the fact that our light sources and detectors are fast, operating at speeds of up to 100GHz.
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