Emerging memcapacitive nanoscale devices have the potential to perform computations in new ways. In this article, we systematically study, to the best of our knowledge for the first time, the computational capacity of complex memcapacitive networks, which function as reservoirs in reservoir computing, one of the brain-inspired computing architectures. Memcapacitive networks are composed of memcapacitive devices randomly connected through nanowires. Previous studies have shown that both regular and random reservoirs provide sufficient dynamics to perform simple tasks. How do complex memcapacitive networks illustrate their computational capability, and what are the topological structures of memcapacitive networks that solve complex tasks with efficiency? Studies show that small-world power-law (SWPL) networks offer an ideal trade-off between the communication properties and the wiring cost of networks. In this study, we illustrate the computing nature of SWPL memcapacitive reservoirs by exploring the two essential properties: fading memory and linear separation through measurements of kernel quality. Compared to ideal reservoirs, nanowire memcapacitive reservoirs had a better dynamic response and improved their performance by 4.67% on three tasks: MNIST, Isolated Spoken Digits, and CIFAR-10. On the same three tasks, compared to memristive reservoirs, nanowire memcapacitive reservoirs achieved comparable performance with much less power, on average, about 99× , 17×, and 277×, respectively. Simulation results of the topological transformation of memcapacitive networks reveal that that topological structures of the memcapacitive SWPL reservoirs did not affect their performance but significantly contributed to the wiring cost and the power consumption of the systems. The minimum trade-off between the wiring cost and the power consumption occurred at different network settings of α and β : 4.5 and 0.61 for Biolek reservoirs, 2.7 and 1.0 for Mohamed reservoirs, and 3.0 and 1.0 for Najem reservoirs. The results of our research illustrate the computational capacity of complex memcapacitive networks as reservoirs in reservoir computing. Such memcapacitive networks with an SWPL topology are energy-efficient systems that are suitable for low-power applications such as mobile devices and the Internet of Things.

中文翻译：

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复杂记忆电容网络的计算能力

新兴的memcapacitive 纳米级设备有可能以新的方式执行计算。在本文中，我们首次系统地研究了复杂的记忆电容网络的计算能力，这些网络在水库计算，受大脑启发的计算架构之一。记忆电容网络由通过纳米线随机连接的记忆电容设备组成。先前的研究表明，常规和随机水库都提供了足够的动力来执行简单的任务。复杂的记忆电容网络如何说明它们的计算能力，以及高效解决复杂任务的记忆电容网络的拓扑结构是什么？研究表明，小世界幂律 (SWPL) 网络在通信属性和网络布线成本之间提供了理想的权衡。在这项研究中，我们通过探索两个基本属性来说明 SWPL 容性储层的计算性质：衰减记忆和通过测量内核质量的线性分离。与理想水库相比，纳米线memcapacitive储层具有更好的动态响应，并在三个任务上提高了4.67%的性能：MNIST、孤立的语音数字和CIFAR-10。在相同的三个任务中，与忆阻式储层相比，纳米线忆阻式储层实现了相当的性能，而功耗却小得多，平均分别约为 99×、17× 和 277×。记忆电容网络拓扑变换的仿真结果表明，记忆电容SWPL水库的拓扑结构不影响其性能，但显着增加了系统的布线成本和功耗。布线成本和功耗之间的最小权衡发生在不同的网络设置下 67% 完成三项任务：MNIST、孤立的语音数字和 CIFAR-10。在相同的三个任务中，与忆阻式储层相比，纳米线忆阻式储层实现了相当的性能，而功耗却小得多，平均分别约为 99×、17× 和 277×。记忆电容网络拓扑变换的仿真结果表明，记忆电容SWPL水库的拓扑结构不影响其性能，但显着增加了系统的布线成本和功耗。布线成本和功耗之间的最小权衡发生在不同的网络设置下 67% 完成三项任务：MNIST、孤立的语音数字和 CIFAR-10。在相同的三个任务中，与忆阻式储层相比，纳米线忆阻式储层实现了相当的性能，而功耗却小得多，平均分别约为 99×、17× 和 277×。记忆电容网络拓扑变换的仿真结果表明，记忆电容SWPL水库的拓扑结构不影响其性能，但显着增加了系统的布线成本和功耗。布线成本和功耗之间的最小权衡发生在不同的网络设置下 平均而言，分别约为 99×、17× 和 277×。记忆电容网络拓扑变换的仿真结果表明，记忆电容SWPL水库的拓扑结构不影响其性能，但显着增加了系统的布线成本和功耗。布线成本和功耗之间的最小权衡发生在不同的网络设置下 平均而言，分别约为 99×、17× 和 277×。记忆电容网络拓扑变换的仿真结果表明，记忆电容SWPL水库的拓扑结构不影响其性能，但显着增加了系统的布线成本和功耗。布线成本和功耗之间的最小权衡发生在不同的网络设置下α和β: 4.5 和 0.61比奥莱克水库，2.7 和 1.0穆罕默德水库，以及 3.0 和 1.0纳杰姆水库。我们的研究结果说明了复杂的memcapacitive网络作为储层计算中的储层的计算能力。这种具有 SWPL 拓扑的内存电容网络是节能系统，适用于移动设备和物联网等低功耗应用。