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

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

arXiv · 2003.02357

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

Christopher H. Bennett·T. Patrick Xiao·Can Cui·Naimul Hassan·Otitoaleke G. Akinola·Jean Anne C. Incorvia·Alvaro Velasquez·Joseph S. Friedman·Matthew J. Marinella

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Abstract

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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Christopher H. Bennett, T. Patrick Xiao, Can Cui, Naimul Hassan, Otitoaleke G. Akinola, Jean Anne C. Incorvia, Alvaro Velasquez, Joseph S. Friedman, Matthew J. Marinella. 2020-03-04. Plasticity-Enhanced Domain-Wall MTJ Neural Networks for Energy-Efficient Online Learning. https://arxiv.org/abs/2003.02357

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