Stochastic spintronic device based synapses and spiking neurons for neuromorphic computation

Abstract

Spintronics devices such as magnetic tunnel junction (MTJ) have been investigated for the neuromorphic computation. However, there are still a number of challenges for hardware implementation of the bio-inspired computing, for instance how to use the binary MTJ to mimic the analog synapse. In this paper, a compound scheme is firstly proposed, which employs multiple MTJs connected in parallel operating in the stochastic regime to jointly behave a single synapse, aiming to achieve an analog-like weight spectrum. To further exploit its stochastic switching property for the bio-inspired computing, we present a MTJ based stochastic spiking neuron (SSN) circuit, which can also realize the neural rate coding scheme. A case study is made on the MNIST database for handwritten digital recognition with the proposed compound magnetoresistive synapse (CMS) and SSN. System-level simulation results show that the proposed CMS and SSN can implement neuromorphic computation with high accuracy and immunity to device variation.