Spike time dependent plasticity (STDP) enabled learning in spiking neural networks using domain wall based synapses and neurons

摘要

We have implemented a Spiking Neural Network (SNN) architecture using a combination of spin orbit torque driven domain wall devices and transistor based peripheral circuits as both synapses and neurons. Learning in the SNN hardware is achieved both under completely unsupervised mode and partially supervised mode through mechanisms, incorporated in our spintronic synapses and neurons, that have biological plausibility, e.g., Spike Time Dependent Plasticity (STDP) and homoeostasis. High classification accuracy is obtained on the popular Iris dataset for both modes of learning.