Kim Seung Ju, Kim Sang Bum, Jang Ho Won
Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea.
iScience. 2020 Dec 3;24(1):101889. doi: 10.1016/j.isci.2020.101889. eCollection 2021 Jan 22.
The expeditious development of information technology has led to the rise of artificial intelligence (AI). However, conventional computing systems are prone to volatility, high power consumption, and even delay between the processor and memory, which is referred to as the von Neumann bottleneck, in implementing AI. To address these issues, memristor-based neuromorphic computing systems inspired by the human brain have been proposed. A memristor can store numerous values by changing its resistance and emulate artificial synapses in brain-inspired computing. Here, we introduce six types of memristors classified according to their operation mechanisms: ionic migration, phase change, spin, ferroelectricity, intercalation, and ionic gating. We review how memristor-based neuromorphic computing can learn, infer, and even create, using various artificial neural networks. Finally, the challenges and perspectives in the competing memristor technology for neuromorphic computing systems are discussed.
信息技术的迅速发展导致了人工智能（AI）的兴起。然而，传统计算系统在实现人工智能时容易出现波动性、高功耗，甚至处理器与内存之间的延迟，即所谓的冯·诺依曼瓶颈。为了解决这些问题，人们提出了受人类大脑启发的基于忆阻器的神经形态计算系统。忆阻器可以通过改变其电阻来存储大量值，并在受大脑启发的计算中模拟人工突触。在此，我们介绍根据其操作机制分类的六种忆阻器：离子迁移、相变、自旋、铁电、插层和离子门控。我们回顾了基于忆阻器的神经形态计算如何使用各种人工神经网络进行学习、推理甚至创造。最后，讨论了用于神经形态计算系统的忆阻器技术竞争中的挑战和前景。
