Bilayer-skyrmion-based design of neuron and synapse for spiking neural network

TL;DR

This paper introduces a bilayer skyrmion-based design for neurons and synapses in spiking neural networks, addressing skyrmion Hall effect issues and demonstrating high classification accuracy on MNIST.

Contribution

It proposes a novel bilayer device design that nullifies the Magnus force, enabling robust skyrmion-based neurons and synapses with linear weight updates.

Findings

The bilayer device functions effectively as neuron and synapse.

The skyrmionic synapse exhibits linear and symmetric weight updates.

Achieved 96.23% accuracy on MNIST classification.

Abstract

Magnetic skyrmion technology is promising for the next-generation spintronics-based memory and neuromorphic computing due to their small size, non-volatility and low depinning current density. However, the Magnus force originating from the skyrmion Hall effect causes the skyrmion to move along a curved trajectory, which may lead to the annihilation of the skyrmion in a nanotrack during current-induced skyrmion motion. Consequently, circuits utilizing skyrmionic motion need to be designed to limit the impact of the skyrmion Hall effect. In this work, we propose a design of an artificial neuron, and a synapse using the bilayer device consisting of two antiferromagnetically exchange coupled ferromagnetic layers, which achieves robustness against the skyrmion Hall effect by nullifying the Magnus force. Using micromagnetic simulations, we show that the bilayer device can work as an artificial neuron and also as a synapse by modifying its uniaxial anisotropy. We also demonstrate that our proposed skyrmionic synapse has an intrinsic property of perfectly linear and symmetric weight update, which is highly desirable for the synapse operation. A spiking neural network implemented using our proposed synapse and neuron was simulated and showed to achieve 96.23\% accuracy in performing classification on the MNIST handwritten digit dataset.