Spintronic reservoir computing without driving current or magnetic field
arXiv:2306.13270 · doi:10.1038/s41598-022-14738-1
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.
13 pages, 5 figures
References in corpus (4)
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