Memristor-based synapse design and training scheme for neuromorphic computing architecture

Abstract

Memristors have been rediscovered recently and then gained increasing attentions. Their unique properties, such as high density, nonvolatility, and recording historic behavior of current (or voltage) profile, have inspired the creation of memristor-based neuromorphic computing architecture. Rather than the existing crossbar-based neuron network designs, we focus on memristor-based synapse and the corresponding training circuit to mimic the real biological system. In this paper, first, the basic synapse design is presented. On top of it, we will discuss the training sharing scheme and explore design implication on multi-synapse neuron system. Energy saving method such as self-training is also investigated.