Deep Photonic Time-Delay Reservoir Computing for Information Processing Acceleration

We propose deep photonic time-delay-reservoir-computing for accelerated information processing. Experimental evaluation using image classification demonstrated a sixfold increase in processing speed with only a 0.6% performance degradation, while conventional TDRC experiences a 5.5% performance degradation.

Authors

Xianbin Yu (ORCID: https://orcid.org/0000-0003-0063-4460)
Oskars Ozoliņš (ORCID: https://orcid.org/0000-0001-9839-7488)
Xiaodan Pang (ORCID: https://orcid.org/0000-0003-4906-1704)
Lu Zhang (ORCID: https://orcid.org/0000-0001-9567-155X)
Jiahao Zhang (ORCID: https://orcid.org/0000-0003-0783-1408)

Institutions

Zhejiang University (CN)
KTH Royal Institute of Technology (SE)

Publication Details

Published: 2024-11-02
DOI: https://doi.org/10.1109/acp/ipoc63121.2024.10809590
Citations: 2
Primary Topic: Neural Networks and Reservoir Computing
Type: article
Field-Weighted Citation Impact: 0.69

Funders

National Natural Science Foundation of China
Vetenskapsrådet
Energimyndigheten
National Key Research and Development Program of China

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article

Deep Photonic Time-Delay Reservoir Computing for Information Processing Acceleration

Xianbin Yu, Oskars Ozoliņš, Xiaodan Pang, Lu Zhang et al.

Nov 2, 2024

2 citations

Neural Networks and Reservoir Computing

0.69

article

Deep Photonic Time-Delay Reservoir Computing for Information Processing Acceleration

Xianbin Yu, Oskars Ozoliņš, Xiaodan Pang, Lu Zhang, Jiahao Zhang

November 2, 2024article en

2 citations

Abstract

We propose deep photonic time-delay-reservoir-computing for accelerated information processing. Experimental evaluation using image classification demonstrated a sixfold increase in processing speed with only a 0.6% performance degradation, while conventional TDRC experiences a 5.5% performance degradation.

Zhejiang University (CN), KTH Royal Institute of Technology (SE)

National Natural Science Foundation of China, Vetenskapsrådet, Energimyndigheten, National Key Research and Development Program of China

Affordable and clean energy

Openalex Percentile: Top 22%

Neural Networks and Reservoir Computing

0.69

DOI Source OpenAlex

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