Intrinsic Spike Timing Dependent Plasticity in Stochastic Magnetic Tunnel Junctions Mediated by Heat Dynamics
This work addresses the need for efficient neuromorphic computing by mimicking synaptic behavior, but it is incremental as it applies existing device physics to a known biological mechanism.
The authors tackled the problem of implementing biological Spike Timing Dependent Plasticity (STDP) in neuromorphic computing by proposing a method using Magnetic Tunnel Junction (MTJ) devices, where simulations demonstrated that STDP can be imitated by applying simple voltage waveforms across an MTJ.
The quest for highly efficient cognitive computing has led to extensive research interest for the field of neuromorphic computing. Neuromorphic computing aims to mimic the behavior of biological neurons and synapses using solid-state devices and circuits. Among various approaches, emerging non-volatile memory technologies are of special interest for mimicking neuro-synaptic behavior. These devices allow the mapping of the rich dynamics of biological neurons and synapses onto their intrinsic device physics. In this letter, we focus on Spike Timing Dependent Plasticity (STDP) behavior of biological synapses and propose a method to implement the STDP behavior in Magnetic Tunnel Junction (MTJ) devices. Specifically, we exploit the time-dependent heat dynamics and the response of an MTJ to the instantaneous temperature to imitate the STDP behavior. Our simulations, based on a macro-spin model for magnetization dynamics, show that, STDP can be imitated in stochastic magnetic tunnel junctions by applying simple voltage waveforms as the spiking response of pre- and post-neurons across an MTJ device.