Abstract
Spin waves propagating through a stripe domain structure and reservoir computing with their spin dynamics have been numerically studied with focusing on the relation between physical phenomena and computing capabilities. Our system utilizes a spin-wave-based device that has a continuous magnetic garnet film and 1-input/72-output electrodes on the top. To control spatially-distributed spin dynamics, a stripe magnetic domain structure and amplitude-modulated triangular input waves were used. The spatially-arranged electrodes detected spin vector outputs with various nonlinear characteristics that were leveraged for reservoir computing. By moderately suppressing nonlinear phenomena, our system achieves 100$\%$ prediction accuracy in temporal exclusive-OR (XOR) problems with a delay step up to 5. At the same time, it shows perfect inference in delay tasks with a delay step more than 7 and its memory capacity has a maximum value of 21. This study demonstrated that our spin-wave-based reservoir computing has a high potential for edge-computing applications and also can offer a rich opportunity for further understanding of the underlying nonlinear physics.
Highlights
Reservoir computing is a computational framework which is originally based on recurrent neural networks [1,2]
In the operation of an actual device, spin waves in the magnetic garnet film are excited by an input voltage, they propagate through the magnetic garnet film, and their dynamics beneath the output electrodes are detected by output voltages
Toward deep understanding of physical reservoir computing, it is worthwhile to discuss the relationship between the reservoir-computing capabilities CP and MCacc, with suggestions on the physical properties of the spin waves behind the reservoir computing
Summary
Reservoir computing is a computational framework which is originally based on recurrent neural networks [1,2]. Owing to its unique feature, reservoir computing models require much less training cost than deep neural networks It is promising for machine-learning-based edge computing [3,4] that can perform energy-efficient information processing of a time-series data obtained from mobile devices and sensors. High capabilities in extremely efficient information processing are expected for excitable continuous medium reservoirs utilizing propagation of waves triggered by stimulation inputs [14], without internal wiring In these reservoirs, a high dimensionality can be realized by large numbers of spatially arranged inputs and/or detectors for input/output signals. The relation between physical phenomena and computing capabilities is discussed
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