High-speed, ultra-low-power, and robust superconductive neuron with ReLU activation

Abstract We propose a novel ultra-high-speed neuron device utilizing a superconductive single flux quantum (SFQ) circuit to realize an ideal rectified linear unit (ReLU) activation function. This circuit generates quantum-accurate voltage output through frequency conversion within the SFQ digital circuit. A significant advantage of this design is its combination of high-speed and ultra-low-power operation with inherent tolerance to device parameter variations. This crucial feature mitigates performance degradation often observed in large-scale neural networks that rely on analog neuron circuits susceptible to characteristic variation of neuron devices. We designed and implemented the proposed neuron circuit using a 10 kA cm −2 Nb four-layer 1.0 μ m fabrication process. Experimental measurements at 4.2 K confirmed correct operation up to approximately 41.2 GHz input. Results from multiple chips successfully demonstrated ideal ReLU input–output characteristics, showcasing both the high-speed nature of the device and the scalability and robustness of our neuron circuits for next-generation artificial neural network hardware.

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Publication Details

Journal
Neuromorphic Computing and Engineering
Published
2025-09-24
DOI
https://doi.org/10.1088/2634-4386/ae0aee
Citations
1
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
2.48
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article

High-speed, ultra-low-power, and robust superconductive neuron with ReLU activation

Yuki Yamanashi, Yuki Hironaka, Nobuyuki Yoshikawa, Yuto Ueno
1 citations
Neuromorphic Computing and Engineering
Neural Networks and Applications
2.48
article

High-speed, ultra-low-power, and robust superconductive neuron with ReLU activation

Yuki Yamanashi, Yuki Hironaka, Nobuyuki Yoshikawa, Yuto Ueno
article en
1 citations

Abstract

Abstract We propose a novel ultra-high-speed neuron device utilizing a superconductive single flux quantum (SFQ) circuit to realize an ideal rectified linear unit (ReLU) activation function. This circuit generates quantum-accurate voltage output through frequency conversion within the SFQ digital circuit. A significant advantage of this design is its combination of high-speed and ultra-low-power operation with inherent tolerance to device parameter variations. This crucial feature mitigates performance degradation often observed in large-scale neural networks that rely on analog neuron circuits susceptible to characteristic variation of neuron devices. We designed and implemented the proposed neuron circuit using a 10 kA cm −2 Nb four-layer 1.0 μ m fabrication process. Experimental measurements at 4.2 K confirmed correct operation up to approximately 41.2 GHz input. Results from multiple chips successfully demonstrated ideal ReLU input–output characteristics, showcasing both the high-speed nature of the device and the scalability and robustness of our neuron circuits for next-generation artificial neural network hardware.

Neuromorphic Computing and EngineeringVol. 5(4)
Yokohama National University (JP)
Openalex Percentile: Top 8%
Neural Networks and Applications
2.48
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