A Power Efficient Artificial Neuron Using Superconducting Nanowires

TL;DR

This paper introduces a superconducting nanowire-based artificial neuron that mimics biological spiking behavior, offering a power-efficient, scalable, and versatile platform for neuromorphic computing.

Contribution

It presents a novel superconducting nanowire neuron architecture with a variable inductive synapse, enabling excitatory/inhibitory control and fanout, advancing hardware SNNs.

Findings

Nanowire neuron reproduces biological spiking characteristics

Synapse design supports direct fanout and dual excitatory/inhibitory functions

Energy performance is competitive with existing technologies

Abstract

With the rising societal demand for more information-processing capacity with lower power consumption, alternative architectures inspired by the parallelism and robustness of the human brain have recently emerged as possible solutions. In particular, spiking neural networks (SNNs) offer a bio-realistic approach, relying on pulses analogous to action potentials as units of information. While software encoded networks provide flexibility and precision, they are often computationally expensive. As a result, hardware SNNs based on the spiking dynamics of a device or circuit represent an increasingly appealing direction. Here, we propose to use superconducting nanowires as a platform for the development of an artificial neuron. Building on an architecture first proposed for Josephson junctions, we rely on the intrinsic nonlinearity of two coupled nanowires to generate spiking behavior, and use electrothermal circuit simulations to demonstrate that the nanowire neuron reproduces multiple characteristics of biological neurons. Furthermore, by harnessing the nonlinearity of the superconducting nanowire's inductance, we develop a design for a variable inductive synapse capable of both excitatory and inhibitory control. We demonstrate that this synapse design supports direct fanout, a feature that has been difficult to achieve in other superconducting architectures, and that the nanowire neuron's nominal energy performance is competitive with that of current technologies.