Novel RRAM-enabled 1T1R synapse capable of low-power STDP via burst-mode communication and real-time unsupervised machine learning

Abstract

We present a new electronic synapse for neuromorphic computing consisting of a 1T1R structure based on HfO2 RRAM technology, and capable of STDP and pattern learning. Power consumption is reduced by adopting short POST spike and burst-mode integration. MNIST classification shows promising learning and classification efficiency. These results support RRAM as an enabling technology for low-power neuromorphic hardware.