Neuromorphic weighted sums with magnetic skyrmions

TL;DR

This paper demonstrates a biologically-inspired, scalable method for performing weighted sum operations in neuromorphic computing using magnetic skyrmions, potentially enhancing hardware efficiency.

Contribution

It introduces a novel approach to implement weighted sums with magnetic skyrmions, enabling scalable and efficient neuromorphic computations.

Findings

Weighted sum operations performed using magnetic skyrmions.

Experimental demonstration with two inputs.

Potential for scaling to multiple inputs and outputs.

Abstract

Integrating magnetic skyrmions into neuromorphic computing could help improve hardware efficiency and computational power. However, developing a scalable implementation of the weighted sum of neuron signals - a core operation in neural networks - has remained a challenge. Here, we show that weighted sum operations can be performed in a compact, biologically-inspired manner by using the non-volatile and particle-like characteristics of magnetic skyrmions that make them easily countable and summable. The skyrmions are electrically generated in numbers proportional to an input with an efficiency given by a non-volatile weight. The chiral particles are then directed using localized current injections to a location where their presence is quantified through non-perturbative electrical measurements. Our experimental demonstration, which currently has two inputs, can be scaled to accommodate multiple inputs and outputs using a crossbar array design, potentially nearing the energy efficiency observed in biological systems.