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 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 sequence to sequence LSTM for accurate household load predictions.

翻译：在本研究中，我们展示了利用自旋电子物理储层实现自主长期预测的能力。由于磁化动力学的短时记忆特性，储层状态中产生了非线性特征，可通过简单线性回归进行在线训练，从而用于长期预测任务。在预测阶段，输出结果直接反馈至储层输入，实现自主预测。我们将所提出的储层应用于混沌时间序列（如Mackey-Glass系统）和动态时序数据（如家庭建筑能耗）的建模。由于仅需对储层计算（RC）的最后一层进行线性回归训练，该方案特别适用于边缘设备上的实时学习。研究表明，基于斯格明子的磁性隧道结可潜在地用作原型RC，而任何具有非线性磁化行为的纳米磁性隧道结均可实现此类RC。通过将自旋电子物理RC方法与长短期记忆网络（LSTM）和循环神经网络（RNN）等能耗预测算法进行对比，我们得出结论：所提框架在实现高预测精度的同时，仅需较低的内存与能耗——这在硬件资源与功率受限的边缘应用中至关重要。此外，该方案被证明仅需极小的训练数据集，且在家庭负荷精确预测中，其能效相比序列到序列LSTM至少提升16倍。