A Superconducting Nanowire-based Architecture for Neuromorphic Computing

TL;DR

This paper introduces a superconducting nanowire-based hardware architecture for neuromorphic computing, providing a methodology to translate neural algorithms into physical circuit specifications, demonstrated through solving linear systems with spiking neural networks.

Contribution

It presents a novel tool and methodology to convert neuromorphic algorithms into superconducting circuit parameters, bridging physics and neuroscience for hardware implementation.

Findings

Established correspondence between neuroscience models and circuit dynamics.

Successfully implemented a superconducting neuromorphic system for linear system solving.

Demonstrated the practical application of superconducting nanowire circuits in neuromorphic tasks.

Abstract

Neuromorphic computing is poised to further the success of software-based neural networks by utilizing improved customized hardware. However, the translation of neuromorphic algorithms to hardware specifications is a problem that has been seldom explored. Building superconducting neuromorphic systems requires extensive expertise in both superconducting physics and theoretical neuroscience. In this work, we aim to bridge this gap by presenting a tool and methodology to translate algorithmic parameters into circuit specifications. We first show the correspondence between theoretical neuroscience models and the dynamics of our circuit topologies. We then apply this tool to solve linear systems by implementing a spiking neural network with our superconducting nanowire-based hardware.