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

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.