Hybrid dual-complementary metal-oxide-semiconductor/memristor synapse-based neural network with its applications in image super-resolution

Biology-inspired neural computing is a potential candidate for the implementation of next-generation intelligent systems. Memristor is a passive electrical element with resistance-switching dynamics. Owing to its natural advantages of non-volatility, nanoscale geometries, and variable conductance, memristor can effectively simulate the synaptic connecting strength between the neurones in the multilayer neural networks. This study presents a kind of memristor synapse-based multilayer neural network hardware architecture with a suitable training methodology. Specifically, a novel dual-complementary metal-oxide-semiconductor/memristor synaptic circuit is presented, which is capable of performing the negative, zero, and positive synaptic weights via controlling the direction of current passing through the memristors. Then, the neurone circuit synthesised with multiple synaptic circuits and an activation unit is further designed, which can be utilised to constitute a compact multilayer neural network with fully connected configuration. Also, a hardware-friendly chip-in-the-loop training method is provided during the network training phase. For the verification purpose, the presented neural network is applied for the realisation of single image super-resolution reconstruction.