Reservoir Computing with Spin Waves in Skyrmion Crystal
Mu-Kun Lee, Masahito Mochizuki

TL;DR
This paper introduces a novel reservoir computing approach using spin waves in skyrmion crystals, demonstrating their potential for efficient, non-fabricated spintronic information processing with promising experimental results.
Contribution
It proposes a new skyrmion-based reservoir computing system utilizing natural spin-wave dynamics, avoiding complex nanofabrication, and demonstrates its effectiveness through standard computational tasks.
Findings
The skyrmion spin-wave reservoir exhibits nonlinearity and short-term memory.
It performs well on duration-estimation, short-term memory, and parity-check tasks.
Magnetic skyrmion crystals are promising for spintronics reservoir devices.
Abstract
Magnetic skyrmions are nanometric spin textures characterized by a quantized topological invariant in magnets and often emerge in a crystallized form called skyrmion crystal in an external magnetic field. We propose that magnets hosting a skyrmion crystal possess high potential for application to reservoir computing, which is one of the most successful information processing techniques inspired by functions of human brains. Our skyrmion-based reservoir exploits precession dynamics of magnetizations, i.e., spin waves, propagating in the skyrmion crystal. Because of complex interferences and slow relaxations of the spin-wave dynamics, the skyrmion spin-wave reservoir attains several important characteristics required for reservoir computing, e.g., the generalization ability, the nonlinearity, and the short-term memory. We investigate these properties by imposing three standard tasks to…
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Taxonomy
TopicsNeural Networks and Reservoir Computing · Neural Networks and Applications · Advanced Memory and Neural Computing
