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arXiv · 2504.12363

Fast Parameter Optimization of Delayed Feedback Reservoir with Backpropagation and Gradient Descent

Sosei Ikeda·Hiromitsu Awano·Takashi Sato
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Abstract

A delayed feedback reservoir (DFR) is a reservoir computing system well-suited for hardware implementations. However, achieving high accuracy in DFRs depends heavily on selecting appropriate hyperparameters. Conventionally, due to the presence of a non-linear circuit block in the DFR, the grid search has only been the preferred method, which is computationally intensive and time-consuming and thus performed offline. This paper presents a fast and accurate parameter optimization method for DFRs. To this end, we leverage the well-known backpropagation and gradient descent framework with the state-of-the-art DFR model for the first time to facilitate parameter optimization. We further propose a truncated backpropagation strategy applicable to the recursive dot-product reservoir representation to achieve the highest accuracy with reduced memory usage. With the proposed lightweight implementation, the computation time has been significantly reduced by up to 1/700 of the grid search.

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Sosei Ikeda, Hiromitsu Awano, Takashi Sato. 2025-04-16. Fast Parameter Optimization of Delayed Feedback Reservoir with Backpropagation and Gradient Descent. https://doi.org/10.23919/date58400.2024.10546773

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