Neuromorphic computation with a single magnetic domain wall

Abstract

Machine learning techniques are commonly used to model complex relationships but implementations on digital hardware are relatively inefficient due to poor matching between conventional computer architectures and the structures of the algorithms they are required to simulate. Neuromorphic devices, and in particular reservoir computing architectures, utilize the inherent properties of physical systems to implement machine learning algorithms and so have the potential to be much more efficient. In this work, we demonstrate that the dynamics of individual domain walls in magnetic nanowires are suitable for implementing the reservoir computing paradigm in hardware. We modelled the dynamics of a domain wall placed between two anti-notches in a nickel nanowire using both a 1D collective coordinates model and micromagnetic simulations. When driven by an oscillating magnetic field, the domain exhibits non-linear dynamics within the potential well created by the anti-notches that are analogous to those of the Duffing oscillator. We exploit the domain wall dynamics for reservoir computing by modulating the amplitude of the applied magnetic field to inject time-multiplexed input signals into the reservoir, and show how this allows us to perform machine learning tasks including: the classification of (1) sine and square waves; (2) spoken digits; and (3) non-temporal 2D toy data and hand written digits. Our work lays the foundation for the creation of nanoscale neuromorphic devices in which individual magnetic domain walls are used to perform complex data analysis tasks.

Key points in the determination of the interfacial Dzyaloshinskii-Moriya interaction from asymmetric bubble domain expansion

“…The presence of DMI also alters the dynamic regime, preventing the transition from Néel to Bloch DW up to high fields. Therefore, the Walker breakdown field is remarkably increased [14], allowing high DW velocities, such that DWs might be efficiently used as information carriers in storage and logic devices [15], driven by currents or by magnetic fields [16,17]. This motivated a strong interest in the optimization of the DMI, and the accurate evaluation of its strength.…”

Machine learning using magnetic stochastic synapses

“…In reservoir computing, a fixed reservoir performs a non-linear spatial-temporal transformation of an input sequence such that the output representation is linearly separable. The advantage of RC is that the reservoir transform can be offloaded to a physical system with appropriate properties and there has been considerable recent interest in developing magnetic (spintronics) based physical reservoir computing [7,[59][60][61][62][63][64][65][66]. There is potential to connect our magnetic DW based neural network to these reservoirs to create a complete hardware reservoir computing system.…”

Chiral Spin Textures for Next-Generation Memory and Unconventional Computing

“…Future studies on spin-based stochastic computing may also find that, in the presence of thermal stimuli, the inherently stochastic nature of DWs and skyrmions can be used to implement an error-resistant computing framework or enhance the biorealistic nature of hardware accelerated neural networks . Reservoir computing initiatives are also poised to exploit the non-linear dynamics of DWs and skyrmions, potentially improving the processing performance of high-dimensional data. Additionally, topological quantum computing is expected to similarly outperform conventional computational models and traditional quantum computers for certain types of problems .…”