由磁隧道结实现的随机脉冲神经网络：从非电报到电报切换机制
Stochastic Spiking Neural Networks Enabled by Magnetic Tunnel Junctions: From Nontelegraphic to Telegraphic Switching Regimes
摘要：
基于纳米电子自旋器件的随机脉冲神经网络可能是实现“类脑”紧凑和节能认知智能的一条可能途径。计算模型试图利用纳米电子突触或神经组件的固有设备随机性来执行学习或推理。然而，已经有有限的分析随机自旋设备的标度效应及其对这种随机网络的操作在系统水平上的影响。本研究试图探索设计空间，并分析不同势垒高度的纳米磁体随机类脑计算架构的性能。我们说明了如何底层的网络架构必须进行修改，以占随机电报切换行为显示的低势垒高度的磁铁，因为它们被缩放到超顺磁制度。我们对MNIST数据集上的数字识别问题的深层神经网络架构进行了设备到系统级的分析。
英文摘要：
Stochastic spiking neural networks based on nanoelectronic spin devices can be a possible pathway to achieving "brainlike" compact and energy-effcient cognitive intelligence. The computational model attempt to exploit the intrinsic device stochasticity of nanoelectronic synaptic or neural components to perform learning or inference. However, there has been limited analysis on the scaling effect of stochastic spin devices and its impact on the operation of such stochastic networks at the system level. This work attempts to explore the design space and analyze the performance of nanomagnet-based stochastic neuromorphic computing architectures for magnets with different barrier heights. We illustrate how the underlying network architecture must be modified to account for the random telegraphic switching behavior displayed by magnets with low barrier heights as they are scaled into the superparamagnetic regime. We perform a device-to-system-level analysis on a deep neural-network architecture for a digit-recognition problem on the MNIST data set.
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