Stochastic spintronic device based synapses and spiking neurons for neuromorphic computation

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.