Energy-efficient neural network using an anisotropy field gradient-based self-resetting neuron and meander synapse

Abstract

Neuromorphic computing (NC) is considered a potential solution for energy-efficient artificial intelligence applications. The development of reliable neural network (NN) hardware with low energy and area footprints plays a crucial role in realizing NC. Even though neurons and synapses have already been investigated using a variety of spintronic devices, the research is still in the primitive stages. Particularly, there is not much experimental research on the self-reset (and leaky) aspect(s) of domain wall (DW) device-based neurons. Here, we have demonstrated an energy-efficient NN using a spintronic DW device-based neuron with self-reset (leaky) and integrate-and-fire functions. An “anisotropy field gradient” provides the self-resetting behavior of auto-leaky, integrate, and fire neurons. The leaky property of the neuron was experimentally demonstrated using a voltage-assisted modification of the anisotropy field. A synapse with a meander wire configuration was used to achieve multiple-resistance states corresponding to the DW position and controlled pinning of the DW. The NN showed an energy efficiency of 0.189 nJ/image/epoch while achieving an accuracy of 92.4%. This study provides a fresh path for developing more energy-efficient DW-based NN systems.