论文标题
使用低能屏障磁力的建筑储层计算硬件
Building Reservoir Computing Hardware Using Low Energy-Barrier Magnetics
论文作者
论文摘要
生物学启发的复发性神经网络（例如储层计算机）很感兴趣地从硬件的角度从硬件角度设计时空数据处理器，这是由于简单的学习方案和与Kalman过滤器的深入连接。在这项工作中，我们讨论了使用深入的仿真研究使用一种模拟随机神经元细胞构建硬件储层计算机的方法，该细胞由低能磁铁的磁性隧道连接和一些晶体管构建。这使我们能够实现储层计算机数学模型的物理实施例。使用此类设备对储层计算机进行紧凑的实现，可以使构建紧凑的，节能的信号处理器，用于独立或原位机器在边缘设备中的认知。
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.
