All-optical computing towards 100-GHz clock rates

IF 24 Q1 OPTICS
Gordon H. Y. Li, Midya Parto, Jinhao Ge, Qing-Xin Ji, Maodong Gao, Yan Yu, James Williams, Robert M. Gray, Christian R. Leefmans, Nicolas Englebert, Kerry J. Vahala, Alireza Marandi
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引用次数: 0

Abstract

A computer’s clock rate ultimately determines the minimum time between sequential operations or instructions. Despite exponential advances in electronic computer performance due to Moore’s Law and increasingly parallel system architectures, clock rates of conventional processors have remained stagnant at ~5 GHz for nearly two decades. This creates a significant challenge for applications requiring real-time processing or control of ultrafast information systems. Here, we break this barrier by proposing and experimentally demonstrating computing based on an all-optical recurrent neural network leveraging the ultrafast characteristics of linear and nonlinear optical operations while circumventing electronic bottlenecks. The all-optical computer realizes linear operations, nonlinear functions, and memory entirely in the optical domain with accuracy surpassing a purely linear model up to 80 GHz clock rates depending on the task. We experimentally demonstrate a prototypical task of noisy waveform classification as well as perform ultrafast in-situ analysis of the soliton states from integrated optical microresonators. We further illustrate the application of the architecture for generative artificial intelligence based on quantum fluctuations to generate images even in the absence of input optical signals. Our results highlight the potential of all-optical computing beyond what can be achieved with digital electronics by utilizing ultrafast linear, nonlinear, and memory functions and quantum fluctuations.
向着100ghz时钟速率的全光计算
计算机的时钟速率最终决定了顺序操作或指令之间的最短时间。尽管由于摩尔定律和越来越多的并行系统架构,电子计算机性能呈指数级增长,但传统处理器的时钟频率在近二十年来一直停滞在~5 GHz。这给需要实时处理或控制超快信息系统的应用带来了重大挑战。在这里,我们提出并通过实验证明了基于全光递归神经网络的计算,利用线性和非线性光学操作的超快特性,同时绕过了电子瓶颈。全光计算机完全在光域中实现线性操作、非线性功能和内存,根据任务的不同,其精度超过纯线性模型,最高可达80 GHz时钟速率。我们通过实验演示了噪声波形分类的原型任务,并对集成光学微谐振器的孤子状态进行了超快的原位分析。我们进一步说明了基于量子涨落的生成式人工智能架构的应用,即使在没有输入光信号的情况下也能生成图像。我们的研究结果突出了全光计算的潜力,超越了利用超快线性、非线性和存储功能以及量子涨落所能实现的数字电子学。
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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来源期刊
Light-Science & Applications
Light-Science & Applications 数理科学, 物理学I, 光学, 凝聚态物性 II :电子结构、电学、磁学和光学性质, 无机非金属材料, 无机非金属类光电信息与功能材料, 工程与材料, 信息科学, 光学和光电子学, 光学和光电子材料, 非线性光学与量子光学
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803
审稿时长
2.1 months
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