Mimicking Leaky-Integrate-Fire Spiking Neuron Using Automotion of Domain Walls for Energy-Efficient Brain-Inspired Computing

Although an average human brain might not be able to compete with modern day computers in performing arithmetic operations, when it comes to recognition and classification tasks, biological systems are clear winners in terms of performance and energy efficiency. Building blocks of all such biological systems are neurons and synapses. In order to exploit the benefits of such systems, novel devices are being explored to mimic the behavior of neurons and synapses. We propose a leaky-integrate-fire (LIF) neuron using the physics of automotion in magnetic domain walls (DWs). Due to the shape anisotropy in a high-aspect ratio magnet, DW has a tendency to move automatically, without any external driving force. This property can be exploited to mimic the realistic dynamics of spiking neurons, without any extra energy penalty. We analyze the dynamics of a DW under automotion and show that it can be approximated to mimic the LIF neuronal dynamics. We propose a compact, energy-efficient magnetic neuron, which can directly be cascaded to memristive crossbar array of synapses, thereby evading additional interfacing circuitry. Furthermore, we develop a device-to-system-level behavioral model to underscore the applicability of the proposal in a typical handwritten-digit recognition application.