Superconducting Nanowire Spiking Element for Neural Networks

As
the limits of traditional von Neumann computing come into view,
the brain’s ability to communicate vast quantities of information
using low-power spikes has become an increasing source of inspiration
for alternative architectures. Key to the success of these largescale
neural networks is a power-efficient spiking element that is scalable
and easily interfaced with traditional control electronics. In this
work, we present a spiking element fabricated from superconducting
nanowires that has pulse energies on the order of ∼10 aJ. We
demonstrate that the device reproduces essential characteristics of
biological neurons, such as a refractory period and a firing threshold.
Through simulations using experimentally measured device parameters,
we show how nanowire-based networks may be used for inference in image
recognition and that the probabilistic nature of nanowire switching
may be exploited for modeling biological processes and for applications
that rely on stochasticity.