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 increase the biological accuracy 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 discuss the implementation of linear leaking characteristics. In the neurons of [1]-[3], the leaking DWs accelerate towards the ends of the tracks. To counter this, it is necessary to alter these devices. In fact, by simply introducing slight exponential variations in the sides of the neuron from [3], as in Figure 1, we are capable of constructing neurons with considerably more linear leaking characteristics than those discussed in [1]-[3]. As the exponential variations become more pronounced, the linearity of the neuron’s leaking increases as well. Beyond a certain point, however, continued increases in the curvature causes a regression in the leaking functionality’s linearity. Figure 2 demonstrates the effect of the curvature of the DW track on the device’s leaking functionality, starting with no curvature and progressing to more pronounced curvature.References: [1] N. Hassan, X. Hu, L. Jiang-Wei et al., “Magnetic domain wall neuron with lateral inhibition,” J. Appl. Phys., vol. 124, no. 15, p. 152127, 2018. [2] W. H. Brigner, X. Hu, N. Hassan, et al., “Graded-Anisotropy-Induced Magnetic Domain Wall Drift for an Artificial Spintronic Leaky Integrate-and-Fire Neuron,” IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, vol. 5, no. 1, pp. 19–24, 2019. [3] W. H. Brigner, N. Hassan, L. Jiang-Wei, et al., “Shape-Based Magnetic Domain Wall Drift for an Artificial Spintronic Leaky Integrate-and-Fire Neuron,” IEEE Transactions on Electron Devices, vol. 66, no. 11, pp. 4970-4975, 2019.

Linear Intrinsic Leaking in a Domain-Wall Magnetic Tunnel Junction Neuron

A5: Neuromorphic Computing with Domain Walls and Chiral Spin Textures

technical paper

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Wesley Brigner

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