Spintronic reservoir computing without driving current or magnetic field

Abstract

Recent studies have shown that nonlinear magnetization dynamics excited in nanostructured ferromagnets are applicable to brain-inspired computing such as physical reservoir computing. The previous works have utilized the magnetization dynamics driven by electric current and/or magnetic field. This work proposes a method to apply the magnetization dynamics driven by voltage control of magnetic anisotropy to physical reservoir computing, which will be preferable from the viewpoint of low-power consumption. The computational capabilities of benchmark tasks in single MTJ are evaluated by numerical simulation of the magnetization dynamics and found to be comparable to those of echo-state networks with more than 10 nodes.

arXiv comment13 pages, 5 figures

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Cited by in corpus (11)

• A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning
• Computational capability for physical reservoir computing using a spin-torque oscillator with two free layers
• Input-driven chaotic dynamics in vortex spin-torque oscillator
• Non-periodic input-driven magnetization dynamics in voltage-controlled parametric oscillator
• Bifurcation to complex dynamics in largely modulated voltage-controlled parametric oscillator
• Magneto-Ionic Physical Reservoir Computing
• Echo state property and memory capacity of artificial spin ice
• Spintronic virtual neural network by a voltage controlled ferromagnet for associative memory
• Metrics for spin-based computing
• Predicting the future with magnons
• Learning-Performance Evaluation of a Physical Reservoir Based on a Vortex Spin-Torque Oscillator with a Modified Free Layer