Gated-ReRAM Based Strategies for On-Chip Supervised Learning

We report a gated-ReRAM synaptic devices-based strategy for on-chip supervised learning. A vacancy-driven compact model for gated-ReRAM is presented and corroborated with experimental results. A supervised learning architecture is proposed that allows the feedback to be provided via gate terminal of gated-ReRAM to update weights in a highly parallel manner.