In this study, we have shown autonomous long-term prediction with a
spintronic physical reservoir. Due to the short-term memory property of the
magnetization dynamics, non-linearity arises in the reservoir states which
could be used for long-term prediction tasks using simple linear regression for
online training. During the prediction stage, the output is directly fed to the
input of the reservoir for autonomous prediction. We employ our proposed
reservoir for the modeling of the chaotic time series such as Mackey-Glass and
dynamic time-series data, such as household building energy loads. Since only
the last layer of a RC needs to be trained with linear regression, it is well
suited for learning in real time on edge devices. Here we show that a skyrmion
based magnetic tunnel junction can potentially be used as a prototypical RC but
any nanomagnetic magnetic tunnel junction with nonlinear magnetization behavior
can implement such a RC. By comparing our spintronic physical RC approach with
state-of-the-art energy load forecasting algorithms, such as LSTMs and RNNs, we
conclude that the proposed framework presents good performance in achieving
high predictions accuracy, while also requiring low memory and energy both of
which are at a premium in hardware resource and power constrained edge
applications. Further, the proposed approach is shown to require very small
training datasets and at the same time being at least 16X energy efficient
compared to the state-of-the-art sequence to sequence LSTM for accurate
household load predictions.

通过使用具有非线性磁化特性的纳米磁隧道结构作为物理水库，结合简单的线性回归进行在线训练和自主预测，本研究在动态时间序列数据和混沌时间序列数据上进行了建模，并在高精度、低内存和低能耗的硬件资源上实现了预测。