Proposal for a Leaky-Integrate-Fire Spiking Neuron based on Magneto-Electric Switching of Ferro-magnets

TL;DR

This paper introduces a novel spin-based leaky-integrate-fire neuron using magneto-electric switching of ferro-magnets, offering a low-energy, probabilistic model inspired by biological neurons for neuromorphic computing.

Contribution

It proposes a new spintronic LIF neuron design based on magneto-electric effects, demonstrating its potential for energy-efficient neuromorphic systems.

Findings

Probabilistic switching mimics biological neuron stochasticity

Lower energy consumption compared to CMOS LIF neurons

Feasibility shown through device-to-system simulations for digit recognition

Abstract

The efficiency of the human brain in performing classification tasks has attracted considerable research interest in brain-inspired neuromorphic computing. Hardware implementations of a neuromorphic system aims to mimic the computations in the brain through interconnection of neurons and synaptic weights. A leaky-integrate-fire (LIF) spiking model is widely used to emulate the dynamics of neuronal action potentials. In this work, we propose a spin based LIF spiking neuron using the magneto-electric (ME) switching of ferro-magnets. The voltage across the ME oxide exhibits a typical leaky-integrate behavior, which in turn switches an underlying ferro-magnet. Due to the effect of thermal noise, the ferro-magnet 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 non-volatility of ferro-magnets result in lower energy consumption, when compared to 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 problem