Opportunities for 2D‐Material‐Based Multifunctional Devices and Systems in Bioinspired Neural Networks

Abstract The growing demand for intelligent, real‐time systems pushes artificial intelligence beyond the confines of centralized data centers toward distributed, edge‐based applications such as autonomous robotics, mobile platforms, and IoT sensors. However, the energy and space requirements of conventional artificial intelligence (AI) hardware such as graphic processing units and AI‐specific application‐specific integrated circuits, pose fundamental limitations for deployment at the edge. Bioinspired computing offers a compelling alternative, emulating the efficiency and adaptability of biological systems to achieve low‐power, real‐time intelligence. Among these approaches, spiking neural networks stand out for their sparse, event‐driven computation and have demonstrated orders‐of‐magnitude energy efficiency gains on neuromorphic platforms such as SpiNNaker and Intel's Loihi. Yet, to realize the full potential of bioinspired intelligence in edge environments, a new class of customized hardware is imperative. Emerging innovations in material science, particularly the integration of 2D materials, can enable the design of compact, reconfigurable neuromorphic devices that mimic complex neuronal dynamics with minimal power consumption. These advances promise a new generation of scalable, multifunctional edge AI systems that are capable of perception, adaptation, and autonomous decision‐making, heralding a transformative leap in energy‐efficient computing for pervasive intelligent technologies.