Spintronic Physical Reservoir for Autonomous Prediction and Long-Term Household Energy Load Forecasting
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866910335519162368 |
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| author | Misba, Walid Al Mavikumbure, Harindra S. Rajib, Md Mahadi Marino, Daniel L. Cobilean, Victor Manic, Milos Atulasimha, Jayasimha |
| author_facet | Misba, Walid Al Mavikumbure, Harindra S. Rajib, Md Mahadi Marino, Daniel L. Cobilean, Victor Manic, Milos Atulasimha, Jayasimha |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_03343 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Spintronic Physical Reservoir for Autonomous Prediction and Long-Term Household Energy Load Forecasting Misba, Walid Al Mavikumbure, Harindra S. Rajib, Md Mahadi Marino, Daniel L. Cobilean, Victor Manic, Milos Atulasimha, Jayasimha Machine Learning 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. |
| title | Spintronic Physical Reservoir for Autonomous Prediction and Long-Term Household Energy Load Forecasting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2304.03343 |