Reservoir computing with two-bit input task using dipole-coupled nanomagnet array
Hikaru Nomura, Kazuki Tsujimoto, Minori Goto, Naoki Samura, Ryoichi Nakatani, Yoshishige Suzuki · Japanese Journal of Applied Physics · 2019
Abstract Reservoir computing (RC), which utilizes physical phenomena, is a candidate for low energy consumption recurrent neural networks. Recently, we proposed RC which uses a dipole-coupled nanomagnet array as a reservoir, which applies the direction of the static magnetic moment of each nanomagnet as a node state in the array. It is possible to learn binary tasks with one-bit input data using this reservoir. Moreover, more complex tasks such as motion detection utilize multi-bit input data. This study simulates a two-bit binary input RC by employing the dipole coupled nanomagnet array as a reservoir. We apply a binary task with two-bit input data, which requires a memory capacity and non-linear computing capability of the reservoir. Due to the simulations with a macrospin model at a temperature of 0 K, this RC was able to learn these binary tasks by utilizing 24 nanomagnets.