Spintronic Physical Reservoir for Autonomous Prediction and Long-Term Household Energy Load Forecasting

With the growing use of artificial neural networks (ANNs) in temporal data processing tasks, the cost of training of complex ANNs is an increasing concern. Physical reservoir computing (RC), a variation of recurrent neural networks (RNNs), obviates the need for most data intensive matrix vector multiplication in the recurrent layer by evolving the RC’s internal states with inherent nonlinear dynamics and short-term memory. In this study, we show that magnetic skyrmion confined to a fixed geometry forming the soft layer of a magnetic tunnel junction (MTJ) can work as a RC and perform autonomous long-term prediction of temporal data. Our proposed skyrmion reservoir allows for manipulation of spin dynamics with ultra energy efficient voltage controlled magnetic anisotropy (VCMA) modulation method. Furthermore, the boundary effect on the skyrmion from the geometric edges provides the necessary consistency property of the reservoir. We employ our proposed reservoir for the modeling and prediction of chaotic time series such as Mackey-Glass and dynamic time-series data, such as household building energy loads. For autonomous run, the predicted output is fed to the input of the reservoir. By comparing our spintronic physical RC approach with energy load forecasting algorithms, such as long short-term memory (LSTM) 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 16 times more energy efficient compared to the sequence to sequence LSTM for accurate household load predictions. Higher endurance, fast processing and well-established technology (i.e., MTJ) to integrate with CMOS technology makes such spintronic reservoir attractive over other emergent physical reservoirs.