Supervised learning of an opto-magnetic neural network with ultrashort laser pulses
Supervised learning of an opto-magnetic neural network with ultrashort laser pulses
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DOI：
10.1063/1.5087648
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发表时间：
2019-05-13
影响因子：
4
通讯作者：
Rasing, Th.
中科院分区：
文献类型：
--
作者：
Chakravarty, A.;Mentink, J. H.;Rasing, Th.
数据及其相关能源消耗的爆炸性增长推动了开发用于数据处理和存储的节能大脑启发方案和材料的需求。在这里，我们实验表明，Co/Pt膜可以被用作人工突触通过操纵它们的磁化状态，在室温下使用圆偏振超短光脉冲。我们还展示了一个有效的实现监督感知器学习的光磁神经网络，从这样的磁突触。重要的是，我们证明了突触权重的优化可以使用全局反馈机制来实现，使得学习不依赖于外部存储或额外的优化方案。这些结果表明，在技术相关材料中使用光学控制磁化实现人工神经网络具有很高的潜力，不仅可以快速学习，而且节能。
The explosive growth of data and its related energy consumption is pushing the need to develop energy-efficient brain-inspired schemes and materials for data processing and storage. Here, we demonstrate experimentally that Co/Pt films can be used as artificial synapses by manipulating their magnetization state using circularly polarized ultrashort optical pulses at room temperature. We also show an efficient implementation of supervised perceptron learning on an opto-magnetic neural network, built from such magnetic synapses. Importantly, we demonstrate that the optimization of synaptic weights can be achieved using a global feedback mechanism, such that the learning does not rely on external storage or additional optimization schemes. These results suggest that there is high potential for realizing artificial neural networks using optically controlled magnetization in technologically relevant materials, which can learn not only fast but also energy-efficient.