A perspective on physical reservoir computing with nanomagnetic devices
A perspective on physical reservoir computing with nanomagnetic devices
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DOI：
10.1063/5.0119040
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发表时间：
2023-01-23
影响因子：
4
通讯作者：
Wringe, Chester
中科院分区：
文献类型：
--
作者：
Allwood, Dan A.;Ellis, Matthew O. A.;Wringe, Chester
神经网络彻底改变了人工智能领域，并将变革性应用引入了几乎所有科学领域和行业。然而，这一成功是有代价的;训练高级模型所需的能源是不可持续的。解决这一紧迫问题的一个有希望的方法是开发直接支持算法要求的低能耗神经形态硬件。自旋电子器件固有的非易失性、非线性和记忆性使它们成为神经形态器件的有吸引力的候选者。在这里，我们专注于水库计算范式，一个循环网络与一个简单的训练算法，适用于计算与自旋电子器件，因为它们可以提供非线性和记忆的属性。我们审查的技术和方法开发neuromorphic自旋电子器件，并得出结论，关键的开放问题，以解决这种设备被广泛使用之前。
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.