Deep Learning at the Speed of Light on Nanophotonic Chips

Deep learning has transformed the field of artificial intelligence, but the limitations of conventional computer hardware are already hindering progress. Researchers at MIT think their new “nanophotonic” processor could be the answer by carrying out deep learning at the speed of light.

In the 1980s, scientists and engineers hailed optical computing as the next great revolution in information technology, but it turned out that bulky components like fiber optic cables and lenses didn’t make for particularly robust or compact computers.

In particular, they found it extremely challenging to make scalable optical logic gates, and therefore impractical to make general optical computers, according to MIT physics post-doc Yichen Shen. One thing light is good at, though, is multiplying matrices—arrays of numbers arranged in columns and rows. You can actually mathematically explain the way a lens acts on a beam of light in terms of matrix multiplications.

This also happens to be a core component of the calculations involved in deep learning. Combined with advances in nanophotonics—the study of light’s behavior at the nanometer scale—this has led to a resurgence in interest in optical computing.

“Deep learning is mainly matrix multiplications, so it works very well with the nature of light,” says Shen. “With light you can make deep learning computing much faster and thousands of times more energy-efficient.”

 

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2018-03-22T14:33:15+00:00

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