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A role for optics in AI hardware

Experiments show how an all-optical version of an artificial neural network — a type of artificial-intelligence system — could potentially deliver better energy efficiency can conventional computing approaches.
  1. Geoffrey W. Burr

    1. Geoffrey W. Burr is at IBM Research – Almaden, San Jose, California 95120, USA.

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Optical fibres transmit data across the world in the form of light and are the backbone of modern telecommunications1. However, when such data need to be analysed, they get converted from light into electrons and are then processed using electronics. There was a time when optics was considered as the basis for a potential computing technology2, but it became difficult for optical computing to compete with the rapid improvements made by its electronic counterpart. In the past few years, however, concern has been growing about the energy costs of computation. Therefore, optics is receiving attention again, both as a way to decrease energy requirements3, and as a special-purpose hardware for accelerating artificial-intelligence algorithms such as deep neural networks (DNNs). Writing in Nature, Feldmann et al.4 report an intriguing advance towards all-optical implementations of such networks.

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Nature 569, 199-200 (2019)

doi: https://doi.org/10.1038/d41586-019-01406-0

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