An optoacoustic field-programmable perceptron for recurrent neural networks

Becker S, Englund D, Stiller B (2024)

Publication Type: Journal article

Publication year: 2024

Journal

Nature Communications Nature Publishing Group: Nature Communications

Book Volume: 15

Article Number: 3020

Journal Issue: 1

DOI: 10.1038/s41467-024-47053-6

Abstract

Recurrent neural networks (RNNs) can process contextual information such as time series signals and language. But their tracking of internal states is a limiting factor, motivating research on analog implementations in photonics. While photonic unidirectional feedforward neural networks (NNs) have demonstrated big leaps, bi-directional optical RNNs present a challenge: the need for a short-term memory that (i) programmable and coherently computes optical inputs, (ii) minimizes added noise, and (iii) allows scalability. Here, we experimentally demonstrate an optoacoustic recurrent operator (OREO) which meets (i, ii, iii). OREO contextualizes the information of an optical pulse sequence via acoustic waves. The acoustic waves link different optical pulses, capturing their information and using it to manipulate subsequent operations. OREO’s all-optical control on a pulse-by-pulse basis offers simple reconfigurability and is used to implement a recurrent drop-out and pattern recognition of 27 optical pulse patterns. Finally, we introduce OREO as bi-directional perceptron for new classes of optical NNs.

Authors with CRIS profile

Steven Becker
Birgit Stiller

Involved external institutions

Max-Planck-Institut für die Physik des Lichts (MPL) / Max Planck Institute for the Science of Light DE Germany (DE) Massachusetts Institute of Technology (MIT) US United States (USA) (US)

How to cite

APA:

Becker, S., Englund, D., & Stiller, B. (2024). An optoacoustic field-programmable perceptron for recurrent neural networks. Nature Communications, 15(1). https://doi.org/10.1038/s41467-024-47053-6

MLA:

Becker, Steven, Dirk Englund, and Birgit Stiller. "An optoacoustic field-programmable perceptron for recurrent neural networks." Nature Communications 15.1 (2024).

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