基于神经形态原子开关网络的材料内储备池计算语音数字分类
Spoken Digit Classification by In-Materio Reservoir Computing with Neuromorphic Atomic Switch Networks
摘要：
包括碘化银（AgI）结的原子开关网络（Atomic Switch Networks，简称AgI）被用作物理储备池计算（RC）的神经形态衬底，AgI结是一种以前未被开发为高度互连的纳米线网络内的功能忆阻元件的材料。这类新的基于ASN的设备已被物理表征并用于对语音数字音频数据进行分类，证明了基于衬底的设备架构的实用性，其中可以利用固有的材料特性来执行材料内计算。这项工作表明，在时间分析的自由口语数字数据（FSDD）的分类精度高。这些结果扩展了可用于生产功能性纳米线网络的可行忆阻材料的类别，并支持基于ASN的设备作为涉及记忆、适应和学习的类脑计算应用的独特硬件平台的实用性。
英文摘要：
Atomic Switch Networks (ASN) comprising silver iodide (AgI) junctions, a material previously unexplored as functional memristive elements within highly-interconnected nanowire networks, were employed as a neuromorphic substrate for physical Reservoir Computing (RC). This new class of ASN-based devices has been physically characterized and utilized to classify spoken digit audio data, demonstrating the utility of substrate-based device architectures where intrinsic material properties can be exploited to perform computation in-materio. This work demonstrates high accuracy in the classification of temporally analyzed Free-Spoken Digit Data (FSDD). These results expand upon the class of viable memristive materials available for the production of functional nanowire networks and bolster the utility of ASN-based devices as unique hardware platforms for neuromorphic computing applications involving memory, adaptation and learning.
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