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arXiv · 1805.09977

Macromagnetic simulation for reservoir computing utilizing spin dynamics in magnetic tunnel junctions

Taishi Furuta·Keisuke Fujii·Kohei Nakajima·Sumito Tsunegi·Hitoshi Kubota·Yoshishige Suzuki·Shinji Miwa
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Abstract

The figures-of-merit for reservoir computing (RC), using spintronics devices called magnetic tunnel junctions (MTJs), are evaluated. RC is a type of recurrent neural network. The input information is stored in certain parts of the reservoir, and computation can be performed by optimizing a linear transform matrix for the output. While all the network characteristics should be controlled in a general recurrent neural network, such optimization is not necessary for RC. The reservoir only has to possess a non-linear response with memory effect. In this paper, macromagnetic simulation is conducted for the spin-dynamics in MTJs, for reservoir computing. It is determined that the MTJ-system possesses the memory effect and non-linearity required for RC. With RC using 5-7 MTJs, high performance can be obtained, similar to an echo-state network with 20-30 nodes, even if there are no magnetic and/or electrical interactions between the magnetizations.

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Taishi Furuta, Keisuke Fujii, Kohei Nakajima, Sumito Tsunegi, Hitoshi Kubota, Yoshishige Suzuki, Shinji Miwa. 2018-09-27. Macromagnetic simulation for reservoir computing utilizing spin dynamics in magnetic tunnel junctions. https://doi.org/10.1103/physrevapplied.10.034063

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