论文标题
塑性增强的域墙MTJ神经网络用于节能在线学习
Plasticity-Enhanced Domain-Wall MTJ Neural Networks for Energy-Efficient Online Learning
论文作者
论文摘要
机器学习通过丰富的培训样本实现反向传播。我们演示了一个多阶段的学习系统，该系统通过有希望的非易失性存储器设备，域磁通隧道结（DW-MTJ）实现。该系统由无监督（聚类）和监督子系统组成，并迅速概括（很少）。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方法的价值，因为学习预算仍保持在20美元$μj$以下，即使对于通常用于机器学习中的大型任务。
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.
