论文标题
非线性干扰自旋波多检测的高性能物理储层计算的实验证明
Experimental Demonstration of High-Performance Physical Reservoir Computing with Nonlinear Interfered Spin Wave Multi-Detection
论文作者
论文摘要
物理储层计算是实现高效人工智能设备的一种有前途的方法，它需要具有非线性，褪色内存的物理系统以及在高维度中映射的能力。尽管可以预期，在某些微磁模拟中，自旋波干扰可以作为高效的储层计算，但迄今为止尚无实验验证。本文中，我们演示了储层计算，该计算利用了Yttrium铁石榴石单晶中的多发现非线性旋转波干扰。当使用手写数字识别，二阶非线性动力学任务和非线性自动回归运动平均值（NARMA）时，主题计算系统可实现出色的性能。特别值得注意的是，NARMA2的归一化均方根误差（NMSE）和二阶非线性动力学任务分别为1.81x10-2和8.37x10-5，这是迄今为止报道的任何实验性物理储层的最低数字。上述高性能是通过较高的非线性和干扰自旋波多检测的较大记忆力来实现的。
Physical reservoir computing, which is a promising method for the implementation of highly efficient artificial intelligence devices, requires a physical system with nonlinearity, fading memory, and the ability to map in high dimensions. Although it is expected that spin wave interference can perform as highly efficient reservoir computing in some micromagnetic simulations, there has been no experimental verification to date. Herein, we demonstrate reservoir computing that utilizes multidetected nonlinear spin wave interference in an yttrium iron garnet single crystal. The subject computing system achieved excellent performance when used for hand-written digit recognition, second-order nonlinear dynamical tasks, and nonlinear autoregressive moving average (NARMA). It is of particular note that normalized mean square errors (NMSEs) for NARMA2 and second-order nonlinear dynamical tasks were 1.81x10-2 and 8.37x10-5, respectively, which are the lowest figures for any experimental physical reservoir so far reported. Said high performance was achieved with higher nonlinearity and the large memory capacity of interfered spin wave multi-detection.
