用于神经形态计算的二维材料忆阻器器件

Dafydd Ravenscroft, L. Occhipinti
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引用次数: 2

摘要

神经形态计算提供了克服与传统冯·诺伊曼架构相关的缺点。它们受到内存和处理分离造成的瓶颈的影响,从而导致大延迟和高功耗。实现这一目标的主要方法之一是使用忆阻器。忆阻器的大规模开发提出了许多具有挑战性的问题,这些问题可以通过使用提供一系列独特性能的二维材料来解决。在这项工作中,我们探索了不同的二维材料,这些材料显示出用作忆阻器的潜力。我们探讨了这种忆阻器的一些应用,并评估了该领域可能发展的挑战和未来方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
2D Material Memristor Devices for Neuromorphic Computing
Neuromorphic computing offers to overcome the shortcomings associated with traditional von Neumann architectures. These suffer from bottlenecks caused by the separation of memory and processing, leading to large latency and high power consumption. One of the leading methods of achieving this is using memristors. The mass development of memristors presents a number of challenging problems which may be addressed through the use of 2D materials which offer a range of unique properties. In this work we explore the different 2D materials which demonstrate potential to be used as memristors. We explore some applications of such memristors and assess the challenges and future directions this area could develop.
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