Presentation
1 August 2021 Unsupervised learning in a purely spintronic multilayer perceptron enabled by four-terminal domain wall magnetic tunnel junction neuron
Naimul Hassan, Wesley H. Brigner, Christopher H. Bennett, Alvaro Velasquez, Xuan Hu, Samuel Liu, Can Cui, Matthew J. Marinella, Felipe Garcia-Sanchez, Jean Anne C. Incorvia, Joseph S. Friedman
Author Affiliations +
Abstract
We propose a four-terminal domain wall-magnetic tunnel junction (DW-MTJ) neuron that enables the first-ever purely spintronic multilayer perceptron with unsupervised learning. The leaky integrate-and-fire neuron has a ferromagnetic DW track coupled to a binary MTJ by an electrically insulated layer. Current through the DW track performs integration by moving the DW. Leaking occurs by moving the DW in the opposite direction of integration due to either dipolar magnetic field, anisotropy gradient, or shape variation. When the DW passes underneath the MTJ, it fires by switching between the resistive and conductive states. In a crossbar perceptron, the DW track of each neuron is connected to the analog three-terminal DW-MTJ synapses and the MTJ terminals cascade multiple layers. Finally, an unsupervised learning algorithm results from the feedback between the neuron MTJ and the analog synapses, providing best results of 98.11% accuracy on the Wisconsin breast cancer clustering task.
Conference Presentation
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Naimul Hassan, Wesley H. Brigner, Christopher H. Bennett, Alvaro Velasquez, Xuan Hu, Samuel Liu, Can Cui, Matthew J. Marinella, Felipe Garcia-Sanchez, Jean Anne C. Incorvia, and Joseph S. Friedman "Unsupervised learning in a purely spintronic multilayer perceptron enabled by four-terminal domain wall magnetic tunnel junction neuron", Proc. SPIE 11805, Spintronics XIV, 118051G (1 August 2021); https://doi.org/10.1117/12.2594330
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