Quantifying the computational capability of a nanomagnetic reservoir computing platform with emergent magnetisation dynamics.
Quantifying the computational capability of a nanomagnetic reservoir computing platform with emergent magnetisation dynamics.
复制标题
利用新兴磁化动力学量化纳米磁性储层计算平台的计算能力。
DOI：
10.1088/1361-6528/ac87b5
复制
发表时间：
2022
期刊：
影响因子：
3.5
通讯作者：
Vidamour IT
中科院分区：
文献类型：
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作者：
Vidamour IT
基于具有紧急磁化动力学的相互连接的磁性纳米环阵列的设备最近被提出用于储集层计算应用，但为了使它们在计算上有用，必须能够优化它们的动态响应。在这里，我们使用一个现象学模型来证明，通过调整超参数来控制数据的缩放和使用旋转磁场向系统输入数据的速率，可以优化这样的储存库来完成分类任务。我们使用与任务无关的指标来评估环在每组超参数下的计算能力，并展示这些指标如何与口语和书面数字识别任务中的表现直接相关。然后，我们证明，通过扩展储存库的输出以包括环形阵列的磁状态的多个并发测量，这些指标以及任务中的性能可以进一步提高。
Devices based on arrays of interconnected magnetic nano-rings with emergent magnetization dynamics have recently been proposed for use in reservoir computing applications, but for them to be computationally useful it must be possible to optimise their dynamical responses. Here, we use a phenomenological model to demonstrate that such reservoirs can be optimised for classification tasks by tuning hyperparameters that control the scaling and input-rate of data into the system using rotating magnetic fields. We use task-independent metrics to assess the rings' computational capabilities at each set of these hyperparameters and show how these metrics correlate directly to performance in spoken and written digit recognition tasks. We then show that these metrics, and performance in tasks, can be further improved by expanding the reservoir's output to include multiple, concurrent measures of the ring arrays' magnetic states.