Efficient Neuromorphic Reservoir Computing Using Optoelectronic Memristors for Multivariate Time Series Classification

Jing Su, Jiale Lu, Fan Sun, G. Zhou, Shukai Duan, Xiaofang Hu
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Abstract

Reservoir computing (RC) has attracted much attention as a brain-like neuromorphic computing algorithm for time series processing. In addition, the hardware implementation of the RC system can significantly reduce the computing time and effectively apply it to edge computing, showing a wide range of applications. However, many hardware implementations of RC use different hardware to implement standard RC without further expanding the RC architecture, which makes it challenging to deal with relatively complex time series tasks. Therefore, we propose a bidirectional hierarchical light reservoir computing method using optoelectronic memristors as the basis for the hardware implementation. The approach improves the performance of hardware-implemented RC by allowing the memristor to capture multilevel temporal information and generate a variety of reservoir states. Ag[Formula: see text]GQDs[Formula: see text]TiOx[Formula: see text]FTO memristors with negative photoconductivity effects can map temporal inputs nonlinearly to reservoir states and are used to build physical reservoirs to accomplish higher-speed operations. The method’s effectiveness is demonstrated in multivariate time series classification tasks: a predicted accuracy of 98.44[Formula: see text] is achieved in voiceprint recognition and 99.70[Formula: see text] in the mobile state recognition task. Our study offers a strategy for dealing with multivariate time series classification issues and paves the way to developing efficient neuromorphic computing.
基于光电忆阻器的多元时间序列分类高效神经形态储层计算
水库计算(RC)作为一种用于时间序列处理的类脑神经形态计算算法受到了广泛的关注。此外,RC系统的硬件实现可以显著减少计算时间,并有效地应用于边缘计算,显示出广泛的应用范围。然而,许多RC的硬件实现使用不同的硬件来实现标准RC,而没有进一步扩展RC体系结构,这使得处理相对复杂的时间序列任务具有挑战性。因此,我们提出了一种利用光电忆阻器作为硬件实现基础的双向分层光库计算方法。该方法通过允许忆阻器捕获多层时间信息并生成各种储层状态,提高了硬件实现RC的性能。Ag[公式:见文本]GQDs[公式:见文本]TiOx[公式:见文本]具有负光导效应的FTO忆阻器可以将时间输入非线性地映射到储层状态,并用于构建物理储层以实现更高的运行速度。该方法的有效性在多变量时间序列分类任务中得到了验证:声纹识别的预测准确率为98.44[公式:见文],移动状态识别的预测准确率为99.70[公式:见文]。我们的研究为处理多变量时间序列分类问题提供了一种策略,并为开发高效的神经形态计算铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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