Biologically inspired recurrent neural networks, such as reservoir computers are of interest in designing spatio-temporal data processors from a hardware point of view due to the simple learning scheme and deep connections to Kalman filters. In this work we discuss using in-depth simulation studies a way to construct hardware reservoir computers using an analog stochastic neuron cell built from a low energy-barrier magnet based magnetic tunnel junction and a few transistors. This allows us to implement a physical embodiment of the mathematical model of reservoir computers. Compact implementation of reservoir computers using such devices may enable building compact, energy-efficient signal processors for standalone or in-situ machine cognition in edge devices.

中文翻译：

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使用低能量势垒磁体建造水库计算硬件

由于简单的学习方案和与卡尔曼滤波器的深度连接，受生物学启发的循环神经网络（例如水库计算机）在从硬件的角度设计时空数据处理器方面很受关注。在这项工作中，我们讨论了使用深度模拟研究构建硬件水库计算机的方法，该方法使用模拟随机神经元单元，该神经元单元由基于低能量势垒磁铁的磁隧道结和几个晶体管构建而成。这使我们能够实现储层计算机数学模型的物理实施例。使用此类设备的油藏计算机的紧凑实现可以为边缘设备中的独立或原位机器认知构建紧凑、节能的信号处理器。