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Magnetic Domain Wall Leaky Integrate-and-Fire Neurons with Shape-Based Configurable Activation Functions

ORAL

Abstract

Neuromorphic computing attempts to mimic the neurons and synapses in a human brain in order to provide significant improvements in the computation of unstructured, real-world data. In past research, we have proposed three separate leaky integrate-and-fire (LIF) neurons that provide the leaking, integrating, and firing characteristics without the use of any additional circuitry [1]-[3]. These neurons are therefore able to significantly reduce the area and energy overhead of neuromorphic circuits. To improve the biomimicry of these neurons and to better match the neurons to different neuromorphic schema and algorithms, it is desirable for neuron leaking to implement specific mathematical functions in addition to exhibiting the three basic LIF neuronal functionalities. By varying the shape of the devices, it is possible to implement a variety of leaking characteristics. In this work, we will discuss the implementation of linear and sigmoidal leaking characteristics.

[1] Hassan, et al., JAP, 2018.
[2] Brigner, et al., JxCDC, 2019.
[3] Brigner, et al., TED, 2019.

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Presenters

  • Wesley Brigner

    Electrical and Computer Engineering, University of Texas at Dallas, University of Texas at Dallas

Authors

  • Wesley Brigner

    Electrical and Computer Engineering, University of Texas at Dallas, University of Texas at Dallas

  • Naimul Hassan

    University of Texas at Dallas

  • Xuan Hu

    Electrical and Computer Engineering, University of Texas at Dallas, University of Texas at Dallas, Electrical & Computer Engineering, University of Texas at Dallas

  • Christopher H Bennett

    Sandia National Laboratories

  • Felipe Garcia-Sanchez

    Universidad de Salamanca, University of Salamanca, Applied Physics, Universidad de Salamanca, Department of Applied Physics, Universidad de Salamanca

  • Matthew J. Marinella

    Sandia National Laboratories, Sandia National Laboratories, Albuquerque NM USA

  • Jean Anne C. Incorvia

    Electrical and Computer Engineering, University of Texas at Austin, University of Texas at Austin, ECE, The University of Texas at Austin, Electrical and Computer Engineering Dept., University of Texas at Austin, Austin TX USA

  • Joseph S. Friedman

    Electrical and Computer Engineering, University of Texas at Dallas, University of Texas at Dallas, Electrical and Computer Engineering Dept., University of Texas at Dallas, Richardson TX USA, Electrical & Computer Engineering, University of Texas at Dallas, Department of Electrical and Computer Engineering, University of Texas at Dallas