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Novel superconductive neuron demonstrates ideal ReLU activation in neural networks, highlighting high-speed and low-power capabilities.
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 that utilizes magnetic flux quantum determined by the fundamental physical quantities. 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/cm² Nb four-layer 1.0-μm fabrication process. Experimental measurements at 4.2 K confirmed correct operation up to 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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Ueno et al. (2025) studied this question.
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