Conventional computing based on von Neumann architecture cannot satisfy the demands of artificial intelligence (AI) applications anymore. Neuromorphic computing, emulating structures and principles based on the human brain, provides an alternative and promising approach for efficient and low consumption information processing. Herein, recent progress in neuromorphic computing enabled by emerging two-dimensional (2D) materials is introduced from devices design and hardware implementation to system integration. Especially, the advances of hopeful artificial synapses and neurons utilizing the resistive-switching-based devices, 2D ferroelectric-based memories and transistors, ultrafast flash, and promising transistors with attractive structures are highlighted. The device features, performance merits, bottlenecks, and possible improvement strategies, along with large-scale brain-inspired network fulfillment, are presented. Challenges and prospects of system application for neuromorphic computing are briefly discussed, shedding light on its great potential for AI.

中文翻译：

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神经形态计算：二维材料推动的设备、硬件和系统应用

基于冯诺依曼架构的传统计算已经无法满足人工智能（AI）应用的需求。神经形态计算模拟基于人脑的结构和原理，为高效、低消耗的信息处理提供了一种替代且有前景的方法。在此，从设备设计和硬件实现到系统集成，介绍了由新兴二维 (2D) 材料实现的神经形态计算的最新进展。特别是，利用基于电阻开关的器件、基于 2D 铁电的存储器和晶体管、​​超快闪存和具有吸引力结构的有前途的晶体管的有希望的人工突触和神经元的进步得到了强调。设备特性、性能优点、瓶颈和可能的改进策略，以及大规模的受大脑启发的网络实现。简要讨论了神经形态计算系统应用的挑战和前景，揭示了其在人工智能方面的巨大潜力。