用于节能在线学习的可塑性增强畴壁MTJ神经网络
Plasticity-Enhanced Domain-Wall MTJ Neural Networks for Energy-Efficient Online Learning
摘要：
机器学习通过大量的训练样本实现反向传播。我们展示了一个多阶段的学习系统，实现了一个有前途的非易失性存储器件，畴壁磁性隧道结（DW-MTJ）。该系统由无监督（聚类）以及监督子系统组成，并快速推广（样本少）。我们展示了该设备的物理特性与神经科学启发的可塑性学习规则的最佳实现之间的相互作用，并突出了一系列任务的性能。我们的能量分析证实了该方法的价值，因为即使对于机器学习中通常使用的大型任务，学习预算也保持在20 $μJ$以下。
英文摘要：
Machine learning implements backpropagation via abundant training samples. We demonstrate a multi-stage learning system realized by a promising non-volatile memory device, the domain-wall magnetic tunnel junction (DW-MTJ). The system consists of unsupervised (clustering) as well as supervised sub-systems, and generalizes quickly (with few samples). We demonstrate interactions between physical properties of this device and optimal implementation of neuroscience-inspired plasticity learning rules, and highlight performance on a suite of tasks. Our energy analysis confirms the value of the approach, as the learning budget stays below 20 $μJ$ even for large tasks used typically in machine learning.
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