高性能物理储备池计算的非线性干涉自旋波多重检测实验验证
Experimental Demonstration of High-Performance Physical Reservoir Computing with Nonlinear Interfered Spin Wave Multi-Detection
摘要：
物理储备池计算是实现高效人工智能设备的一种有前途的方法，需要具有非线性、衰减记忆和在高维中映射的能力的物理系统。尽管在一些微磁模拟中期望自旋波干涉可以执行高效的储备池计算，但是迄今为止还没有实验验证。在这里，我们证明了储备池计算，利用多检测非线性自旋波干涉在钇铁石榴石单晶。本课题的计算系统在手写数字识别、二阶非线性动力学任务和非线性自回归滑动平均（NARMA）等方面取得了优异的性能。特别值得注意的是，NARMA 2和二阶非线性动力学任务的归一化均方误差（NMSE）分别为1.81x10-2和8.37x10-5，这是迄今为止报道的任何实验物理水库的最低数字。上述高性能是通过高非线性和干涉自旋波多重检测的大存储容量实现的。
英文摘要：
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.
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