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

Abstract

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 nonvolatility, 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 metaloxide-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.

A Review of Technical Standards for Smart Cities

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Video Super-Resolution Based on Generative Adversarial Network and Edge Enhancement

“…Google announced the Super Res Zoom technology [36], which focused on solving the problem that the images taken by handheld devices are not clear enough. Dong et al proposed [37,38], which combine the SR with mersisters. Qian et al proposed the Trinity Enhancement Network (TENet) [15], which can solve multiple problems at the same time.…”

Memristor‐based stateful logic gates for multi‐functional logic circuit

“…Since the Hewlett‐Packard demonstrated the existence of memristor in 2008, various memristor models have been proposed [7, 24–29], of which the voltage threshold adaptive memristor (VTEAM) model is feasible, simple, accurate and has generality. This paper uses the VTEAM model for all logic designs.…”

“…The parameters used in this paper of VTEAM are chosen to produce a full switching time <1 ns and to fit practical devices. The parameters of simulations are listed in Table 1, which refers to [7, 22].…”