The development of an efficient neuromorphic computing system requires the use of nanodevices that intrinsically emulate the biological behavior of neurons and synapses. While numerous artificial synapses have been shown to store weights in a manner analogous to biological synapses, the challenge of developing an artificial neuron is impeded by the necessity to include leaking, integrating, firing, and lateral inhibition features. In particular, previous proposals for artificial neurons have required the use of external circuits to perform lateral inhibition, thereby decreasing the efficiency of the resulting neuromorphic computing system. This work therefore proposes a leaky integrate-and-fire neuron that intrinsically provides lateral inhibition, without requiring any additional circuitry. The proposed neuron is based on the previously proposed domain-wall magnetic tunnel junction devices, which have been proposed as artificial synapses and experimentally demonstrated for non-volatile logic. Single-neuron micromagnetic simulations are provided that demonstrate the ability of this neuron to implement the required leaking, integrating, and firing. These simulations are then extended to pairs of adjacent neurons to demonstrate, for the first time, lateral inhibition between neighboring artificial neurons. Finally, this intrinsic lateral inhibition is applied to a ten-neuron crossbar structure and trained to identify handwritten digits and shown via direct large-scale micromagnetic simulation for 100 digits to correctly identify the proper signal for 94% of the digits.
Magnetic domain wall neuron with lateral inhibition
Naimul Hassan, Xuan Hu, Lucian Jiang-Wei, Wesley H. Brigner, Otitoaleke G. Akinola, Felipe Garcia-Sanchez, Massimo Pasquale, Christopher H. Bennett, Jean Anne C. Incorvia, Joseph S. Friedman; Magnetic domain wall neuron with lateral inhibition. J. Appl. Phys. 21 October 2018; 124 (15): 152127. https://doi.org/10.1063/1.5042452
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