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Machine learning implements backpropagation via abundant training samples. Wedemonstrate a multi-stage learning system realized by a promising non-volatilememory device, the domain-wall magnetic tunnel junction (DW-MTJ). The systemconsists of unsupervised (clustering) as well as supervised sub-systems, andgeneralizes quickly (with few samples). We demonstrate interactions betweenphysical properties of this device and optimal implementation ofneuroscience-inspired plasticity learning rules, and highlight performance on asuite of tasks. Our energy analysis confirms the value of the approach, as thelearning budget stays below 20 $\mu J$ even for large tasks used typically inmachine learning.

Plasticity-Enhanced Domain-Wall MTJ Neural Networks for Energy-Efficient Online Learning