Proposal for a Leaky-Integrate-Fire Spiking Neuron Based on Magnetoelectric Switching of Ferromagnets

The efficiency of the human brain in performing classification tasks has attracted considerable research interest in brain-inspired neuromorphic computing. The spiking neuromorphic architectures attempt to mimic the computations performed in the brain through a dense interconnection of the neurons and synaptic weights. A leakyintegrate-fire (LIF) spiking model is widely used to emulate the dynamics of the biological neurons. In this paper, we propose a spin-based LIF spiking neuron using the magnetoelectric (ME) switching of ferromagnets. The voltage across the ME oxide exhibits a typical leaky-integrate behavior, which in turn switches an underlying ferromagnet. Due to the effect of thermal noise, the ferromagnet exhibits probabilistic switching dynamics, which is reminiscent of the stochasticity exhibited by biological neurons. The energy efficiency of the ME switching mechanism coupled with the intrinsic nonvolatility of ferromagnets results in lower energy consumption, when compared with a CMOS LIF neuron. A device to system-level simulation framework has been developed to investigate the feasibility of the proposed LIF neuron for a hand-written digit recognition application.