Physical reservoir computing is a promising way to develop efficient artificial intelligence using physical devices exhibiting nonlinear dynamics. Although magnetic materials have advantages in miniaturization, the need for a magnetic field and large electric current results in high electric power consumption and a complex device structure. To resolve these issues, we propose a redox-based physical reservoir utilizing the planar Hall effect and anisotropic magnetoresistance, which are phenomena described by different nonlinear functions of the magnetization vector that do not need a magnetic field to be applied. The expressive power of this reservoir based on a compact all-solid-state redox transistor is higher than the previous physical reservoir. The normalized mean square error of the reservoir on a second-order nonlinear equation task was 1.69 × 10^–3, which is lower than that of a memristor array (3.13 × 10^–3) even though the number of reservoir nodes was fewer than half that of the memristor array.

中文翻译：

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氧化还原机制下的磁化矢量旋转储层计算

物理油藏计算是利用表现出非线性动力学的物理设备开发高效人工智能的一种有前途的方法。虽然磁性材料在小型化方面具有优势，但由于需要磁场和大电流，导致功耗高、器件结构复杂。为了解决这些问题，我们提出了一种利用平面霍尔效应和各向异性磁阻的基于氧化还原的物理储层，这些现象是由磁化矢量的不同非线性函数描述的，不需要施加磁场。这种基于紧凑型全固态氧化还原晶体管的水库的表现力比以前的物理水库更高。蓄水池在二阶非线性方程任务上的归一化均方误差为 1.69 × 10 ^–3，低于忆阻器阵列 (3.13 × 10 ^–3 )，尽管蓄水池节点数量不到一半忆阻器阵列的。