All-photonic reservoir computing for dynamic image classification via spatial-to-temporal encoding

We present an all-photonic reservoir computing device that integrates the reservoir and readout layers on a silicon photonic chip, enabling end-to-end optical processing of time-series data with ultralow latency. The photonic reservoir computing achieves real-time high-speed image processing by incorporating a photonic spatial-to-temporal encoding technique, which converts visual information into a time-domain signal at a rate of 25 Gigasamples per second. We experimentally demonstrated that the proposed photonic approach can recognize microsecond-scale switching events that are challenging to capture using conventional electronic systems. This approach offers a scalable solution for photonic processors targeting time-critical tasks such as anomaly detection and real-time sensing applications.

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Publication Details

Journal
Optics Express
Published
2025-12-30
DOI
https://doi.org/10.1364/oe.580562
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
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article

All-photonic reservoir computing for dynamic image classification via spatial-to-temporal encoding

Tomoaki Niiyama, S. Sunada, Ryota Nakayama, Keigo Takabayashi et al.
Optics Express
Neural Networks and Reservoir Computing
article

All-photonic reservoir computing for dynamic image classification via spatial-to-temporal encoding

Tomoaki Niiyama, S. Sunada, Ryota Nakayama, Keigo Takabayashi, Takeo Maruyama
article en

Abstract

We present an all-photonic reservoir computing device that integrates the reservoir and readout layers on a silicon photonic chip, enabling end-to-end optical processing of time-series data with ultralow latency. The photonic reservoir computing achieves real-time high-speed image processing by incorporating a photonic spatial-to-temporal encoding technique, which converts visual information into a time-domain signal at a rate of 25 Gigasamples per second. We experimentally demonstrated that the proposed photonic approach can recognize microsecond-scale switching events that are challenging to capture using conventional electronic systems. This approach offers a scalable solution for photonic processors targeting time-critical tasks such as anomaly detection and real-time sensing applications.

Optics ExpressVol. 34(2)
Kanazawa University (JP)
Openalex Percentile: Top 18%
Neural Networks and Reservoir Computing
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All-photonic reservoir computing for dynamic image classification via spatial-to-temporal encoding — Tomoaki Niiyama, S. Sunada, et al. · Optics Express (2025) | TGRS Research Map | TGRS