用于自主预测与长期家庭能源负荷预测的自旋物理储备池
Spintronic Physical Reservoir for Autonomous Prediction and Long-Term Household Energy Load Forecasting
AI总结：
本研究提出一种基于自旋物理储备池的自主长期预测方法，利用磁化动力学的非线性特性，在仅需极小训练数据集和低能耗的条件下，实现了与LSTMs和RNNs相当的高精度家庭能源负荷预测，适用于资源受限的边缘设备。
摘要：
在本研究中，我们展示了利用自旋物理储备池进行自主长期预测。由于磁化动力学的短期记忆特性，储备池状态中产生了非线性，这可通过简单的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，输出被直接反馈到储备池的输入端以实现自主预测。我们将所提出的储备池用于混沌时间序列（如Mackey-Glass）和动态时间序列数据（如家庭建筑能源负荷）的建模。由于RC仅需使用线性回归训练最后一层，因此非常适合在边缘设备上进行实时学习。在此我们表明，基于斯格明子的磁性隧道结可潜在地用作原型RC，但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。通过将我们的自旋物理RC方法与能源负荷预测算法（如LSTMs和RNNs）进行比较，我们得出结论：所提出的框架在实现高预测精度方面表现出良好的性能，同时仅需低内存和低能耗，这两者在硬件资源受限和功率受限的边缘应用中尤为珍贵。此外，结果表明，所提出的方法需要非常小的训练数据集，同时与用于准确家庭负荷预测的序列到序列LSTM相比，能效至少提高16倍。
英文摘要：
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.
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