  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
