Output Estimation of Photonic Reservoir Systems Considering Characteristics of Analog MAC Circuits

Photonic reservoir computing (PRC) has attracted considerable attention as an emerging AI accelerator for ultrafast intelligent processing with low power consumption. However, performing all processing optically remains challenging, neces-sitating the support of electrical circuits for post-processing. To address this, we have prototyped an analog multiply-and-accumulate (MAC) circuit designed for the readout process of the reservoir computing. Our results demonstrate that nonlinearity and noise due to the circuit do not significantly affect the PRC system. It suggests analog circuits could be used for readout and actively use this nonlinearity for reservoir computing.

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Publication Details

Published
2024-11-18
DOI
https://doi.org/10.1109/icecs61496.2024.10849040
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
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Output Estimation of Photonic Reservoir Systems Considering Characteristics of Analog MAC Circuits

Satoru Tanoi, Akifumi Kasamatsu, Satoshi Sunada, Kawori Sekine et al.
Neural Networks and Reservoir Computing
article

Output Estimation of Photonic Reservoir Systems Considering Characteristics of Analog MAC Circuits

Satoru Tanoi, Akifumi Kasamatsu, Satoshi Sunada, Kawori Sekine, Shinsuke Hara, Kazuyuki Wada, Yuichiro Ohtsuka
article en

Abstract

Photonic reservoir computing (PRC) has attracted considerable attention as an emerging AI accelerator for ultrafast intelligent processing with low power consumption. However, performing all processing optically remains challenging, neces-sitating the support of electrical circuits for post-processing. To address this, we have prototyped an analog multiply-and-accumulate (MAC) circuit designed for the readout process of the reservoir computing. Our results demonstrate that nonlinearity and noise due to the circuit do not significantly affect the PRC system. It suggests analog circuits could be used for readout and actively use this nonlinearity for reservoir computing.

Kanazawa University (JP), Meiji University (JP), National Institute of Information and Communications Technology (JP)
Clean water and sanitation
Openalex Percentile: Top 76%
Neural Networks and Reservoir Computing
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Output Estimation of Photonic Reservoir Systems Considering Characteristics of Analog MAC Circuits — Satoru Tanoi, Akifumi Kasamatsu, et al. · (2024) | TGRS Research Map | TGRS