使用高度非线性光纤的极限学习机的原理和度量

IF 6.6 2区 物理与天体物理 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Mathilde Hary, Daniel Brunner, Lev Leybov, Piotr Ryczkowski, John M. Dudley, Goëry Genty
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引用次数: 0

摘要

光计算利用光的固有特性,如并行性、线性和非线性超高带宽信号转换,为超高速和低延迟计算提供了潜力。在这里,我们探索使用高度非线性光纤(HNLFs)作为基于极限学习机(elm)概念的光计算平台。为了评估系统的信息处理潜力,我们考虑了任务独立和任务依赖的性能指标。前者侧重于内在属性,如有效维数,通过主成分分析(PCA)量化系统对随机输入的响应。后者评估了MNIST数字数据集上分类任务的准确性,突出了系统在不同压缩级别和非线性传播机制下的表现。我们表明,输入功率和光纤特性显著影响计算系统的维度,在输入功率水平为30 mW时,更长的光纤和更高的色散产生高达100个主成分(PC),其中PC对应于系统的线性独立维度。PC特征向量的光谱分布表明,通过维数扩展促进计算的高维动力学位于泵浦波长1,560 nm的40 nm范围内,为非线性Schrödinger方程系统的计算提供了一般的见解。任务相关的结果证明了HNLFs对MNIST数据集图像分类的有效性。通过PC分析对输入数据进行压缩,将不同输入维数的MNIST图像注入系统,研究输入功率对分类精度的影响。在优化的功率水平下,我们实现了87%±1.3%的分类测试准确率,大大超过了线性系统83.7%的基线。值得注意的是,我们发现最佳性能不是在最大输入功率下获得的,即最大系统维数,而是在低一个数量级以上的情况下获得的。对于MNIST图像的压缩也证实了这一点,当将图像强烈压缩到少于50个pc时,精度大大提高。这些发现与未来的超快光学计算系统的维度高度相关,该系统可以在飞秒时间尺度上捕获和处理顺序输入信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Principles and metrics of extreme learning machines using a highly nonlinear fiber
Optical computing offers potential for ultra high-speed and low-latency computation by leveraging the intrinsic properties of light, such as parallelism and linear as well as nonlinear ultra-high bandwidth signal transformations. Here, we explore the use of highly nonlinear optical fibers (HNLFs) as platforms for optical computing based on the concept of extreme learning machines (ELMs). To evaluate the information processing potential of the system, we consider both task-independent and task-dependent performance metrics. The former focuses on intrinsic properties such as effective dimensionality, quantified via principal component analysis (PCA) on the system response to random inputs. The latter evaluates classification task accuracy on the MNIST digit dataset, highlighting how the system performs under different compression levels and nonlinear propagation regimes. We show that input power and fiber characteristics significantly influence the dimensionality of the computational system, with longer fibers and higher dispersion producing up to 100 principal components (PCs) at input power levels of 30 mW, where the PC corresponds to the linearly independent dimensions of the system. The spectral distribution of the PC’s eigenvectors reveals that the high-dimensional dynamics facilitating computing through dimensionality expansion are located within 40 nm of the pump wavelength at 1,560 nm, providing general insight for computing with nonlinear Schrödinger equation systems. Task-dependent results demonstrate the effectiveness of HNLFs in classifying MNIST dataset images. Using input data compression through PC analysis, we inject MNIST images of various input dimensionality into the system and study the impact of input power upon classification accuracy. At optimized power levels, we achieve a classification test accuracy of 87 % ± 1.3 %, significantly surpassing the baseline of 83.7 % from linear systems. Noteworthy, we find that the best performance is not obtained at maximal input power, i.e., maximal system dimensionality, but at more than one order of magnitude lower. The same is confirmed regarding the MNIST image’s compression, where accuracy is substantially improved when strongly compressing the image to less than 50 PCs. These are highly relevant findings for the dimensioning of future, ultrafast optical computing systems that can capture and process sequential input information on femtosecond timescales.
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来源期刊
Nanophotonics
Nanophotonics NANOSCIENCE & NANOTECHNOLOGY-MATERIALS SCIENCE, MULTIDISCIPLINARY
CiteScore
13.50
自引率
6.70%
发文量
358
审稿时长
7 weeks
期刊介绍: Nanophotonics, published in collaboration with Sciencewise, is a prestigious journal that showcases recent international research results, notable advancements in the field, and innovative applications. It is regarded as one of the leading publications in the realm of nanophotonics and encompasses a range of article types including research articles, selectively invited reviews, letters, and perspectives. The journal specifically delves into the study of photon interaction with nano-structures, such as carbon nano-tubes, nano metal particles, nano crystals, semiconductor nano dots, photonic crystals, tissue, and DNA. It offers comprehensive coverage of the most up-to-date discoveries, making it an essential resource for physicists, engineers, and material scientists.
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