Demonstration of a subthreshold analog CMOS reservoir chip for temporal signal processing

Abstract We present a low-power analog CMOS reservoir computing chip that employs a simple ring topology for unidirectional nonlinear analog processing. Each node integrates a sample-and-hold circuit operating at 1 kHz, enabling compact and energy-efficient implementation in standard CMOS. The fabricated chip was evaluated on standard benchmarks, achieving a linear memory capacity of 13.4 and information processing capacities of 7.2 (second-order), 3.3 (third-order), and 1.2 (fourth-order). It also demonstrated competitive accuracy in both short- and long-term time-series forecasting tasks. Most notably, this performance was achieved with a power dissipation of only 20 μ W per core, highlighting the potential of analog CMOS reservoir computing for deployment in energy-constrained edge devices.

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Publication Details

Journal
npj Unconventional Computing
Published
2026-03-02
DOI
https://doi.org/10.1038/s44335-026-00059-3
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
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article

Demonstration of a subthreshold analog CMOS reservoir chip for temporal signal processing

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npj Unconventional Computing
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Demonstration of a subthreshold analog CMOS reservoir chip for temporal signal processing

Yukio Terasaki, Kota Ando, Kazuki Nakada, Shin-ichiro Mochizuki, Keita Suda, Eiji Suzuki, Tomoyuki Sasaki, Abe Yuki, Tetsuya Asai, Shimon Matsuno, Yukihiro Urakawa
article en

Abstract

Abstract We present a low-power analog CMOS reservoir computing chip that employs a simple ring topology for unidirectional nonlinear analog processing. Each node integrates a sample-and-hold circuit operating at 1 kHz, enabling compact and energy-efficient implementation in standard CMOS. The fabricated chip was evaluated on standard benchmarks, achieving a linear memory capacity of 13.4 and information processing capacities of 7.2 (second-order), 3.3 (third-order), and 1.2 (fourth-order). It also demonstrated competitive accuracy in both short- and long-term time-series forecasting tasks. Most notably, this performance was achieved with a power dissipation of only 20 μ W per core, highlighting the potential of analog CMOS reservoir computing for deployment in energy-constrained edge devices.

npj Unconventional ComputingVol. 3(1)
TDK (Japan) (JP), Hokkaido University (JP)
Affordable and clean energy
Openalex Percentile: Top 41%
Neural Networks and Reservoir Computing
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