Development in memristor-based spiking neural network

Abstract

The field of neuromorphic computing has experienced remarkable growth driven by the need to overcome the limitations of traditional von Neumann architecture in handling big data, IoT, and AI tasks. This paper provides an overview of recent advancements in brain-inspired computing, particularly focusing on the integration of various memristive materials, including metal oxides, perovskites, and 2D materials, into neuromorphic hardware. The evolution of artificial neural network (ANN) technology, ranging from perceptron to deep neural networks (DNNs) and spiking neural networks (SNNs), is discussed, emphasizing the potential of SNNs for energy-efficient hardware implementation. Challenges in integrating memristors, especially 2D material-based memristors, into SNNs are highlighted, along with recent developments in neuromorphic hardware utilizing memristors and complementary metal-oxide-semiconductor (CMOS) technology. Through simulations and experimental demonstrations, researchers have shown the feasibility of using memristors for implementing artificial neurons and synapses, paving the way for efficient and scalable neuromorphic computing systems.