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Weighing in on photonic-based machine learning for automotive mobility

Nature Photonics volume 15, pages 66–67 (2021)Cite this article

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To the Editor — Optical processing for artificial neural networks is being re-evaluated for its potential to address large power consumption, data shuttling and thermal dissipation challenges faced by complementary metal–oxide–semiconductor (CMOS) technology. Though recent advances demonstrate an exceptionally high number of photonic calculations per unit energy, photonic processing must find a niche in the modern market. In applications requiring limited numeric precision, high inference speed and parallelism, this alternative computing framework could outperform traditional von Neumann architectures. At the inflection point of a digital transformation where connected products provide insight into the user experience, optical processing may be the key to pit edge computing against higher-bandwidth mobile networks.

Around 1993 when Apple sold its ten millionth Macintosh that ran on a 400 kB floppy drive, real-time facial recognition emerged at the California Institute of Technology (Caltech)1. While we are all familiar with today’s facial recognition software that runs on microprocessors in some smartphones, this early demonstration by Caltech was built on a form of photonic machine learning.

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Fig. 1: The future of in-vehicle processing balances between cloud and edge computing.

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  1. Toyota Research Institute of North America, Ann Arbor, MI, USA

    Sean Phillip Rodrigues, Ziqi Yu, Paul Schmalenberg, Jae Lee, Hideo Iizuka & Ercan M. Dede

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  1. Sean Phillip Rodrigues
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Correspondence to Sean Phillip Rodrigues.

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The authors work at the Toyota Research Institute of North America and are employees of Toyota Motor Engineering & Manufacturing North America.

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Rodrigues, S.P., Yu, Z., Schmalenberg, P. et al. Weighing in on photonic-based machine learning for automotive mobility. Nat. Photonics 15, 66–67 (2021). https://doi.org/10.1038/s41566-020-00736-0

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