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The figures-of-merit for reservoir computing (RC), using spintronics devicescalled magnetic tunnel junctions (MTJs), are evaluated. RC is a type ofrecurrent neural network. The input information is stored in certain parts ofthe reservoir, and computation can be performed by optimizing a lineartransform matrix for the output. While all the network characteristics shouldbe controlled in a general recurrent neural network, such optimization is notnecessary for RC. The reservoir only has to possess a non-linear response withmemory effect. In this paper, macromagnetic simulation is conducted for thespin-dynamics in MTJs, for reservoir computing. It is determined that theMTJ-system possesses the memory effect and non-linearity required for RC. WithRC using 5-7 MTJs, high performance can be obtained, similar to an echo-statenetwork with 20-30 nodes, even if there are no magnetic and/or electricalinteractions between the magnetizations.

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