Publications

Plasticity-enhanced domain-wall MTJ neural networks for energy-efficient online learning

Bennett, Christopher H.; Xiao, T.P.; Cui, Can; Hassan, Naimul; Akinola, Otitoaleke G.; Incorvia, Jean A.; Velasquez, Alvaro; Friedman, Joseph S.; Marinella, Matthew J.

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 20µJ even for large tasks used typically in machine learning.