تقرير
A perspective on physical reservoir computing with nanomagnetic devices
| العنوان: | A perspective on physical reservoir computing with nanomagnetic devices |
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| المؤلفون: | Allwood, Dan A, Ellis, Matthew O A, Griffin, David, Hayward, Thomas J, Manneschi, Luca, Musameh, Mohammad F KH, O'Keefe, Simon, Stepney, Susan, Swindells, Charles, Trefzer, Martin A, Vasilaki, Eleni, Venkat, Guru, Vidamour, Ian, Wringe, Chester |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science Physics (Other) |
| مصطلحات موضوعية: | Computer Science - Emerging Technologies, Computer Science - Machine Learning, Physics - Applied Physics |
| الوصف: | Neural networks have revolutionized the area of artificial intelligence and introduced transformative applications to almost every scientific field and industry. However, this success comes at a great price; the energy requirements for training advanced models are unsustainable. One promising way to address this pressing issue is by developing low-energy neuromorphic hardware that directly supports the algorithm's requirements. The intrinsic non-volatility, non-linearity, and memory of spintronic devices make them appealing candidates for neuromorphic devices. Here we focus on the reservoir computing paradigm, a recurrent network with a simple training algorithm suitable for computation with spintronic devices since they can provide the properties of non-linearity and memory. We review technologies and methods for developing neuromorphic spintronic devices and conclude with critical open issues to address before such devices become widely used. |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1063/5.0119040 |
| URL الوصول: | http://arxiv.org/abs/2212.04851 |
| رقم الأكسشن: | edsarx.2212.04851 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1063/5.0119040 |
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