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On-chip spiking neural networks based on add-drop ring microresonators and electrically reconfigurable phase-change material photonic switches

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Photonics Research 2024年第4期12卷 755-766页

作者： QIANG ZHANG NING JIANG YIQUN ZHANG ANRAN LI HUANHUAN XIONG GANG HU YONGSHENG CAO KUN QIU School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

We propose and numerically demonstrate a photonic computing primitive designed for integrated spiking neural networks (SNNs) based on add-drop ring microresonators (ADRMRs) and electrically reconfigurable phasechange material (PCM) photonic switches. In this neuromorphic system, the passive silicon-based ADRMR,equipped with a power-tunable auxiliary light, effectively demonstrates nonlinearity-induced dual neural dynamics encompassing spiking response and synaptic plasticity that can generate single-wavelength optical neural spikes with synaptic weight. By cascading these ADRMRs with different resonant wavelengths, weighted multiple-wavelength spikes can be feasibly output from the ADRMR-based hardware arrays when external wavelengthaddressable optical pulses are injected;subsequently, the cumulative power of these weighted output spikes is utilized to ascertain the activation status of the reconfigurable PCM photonic switches. Moreover, the reconfigurable mechanism driving the interconversion of the PCMs between the resonant-bonded crystalline states and the covalent-bonded amorphous states is achieved through precise thermal modulation. Drawing from the thermal properties, an innovative thermodynamic leaky integrate-and-firing (TLIF) neuron system is proposed. With the TLIF neuron system as the fundamental unit, a fully connected SNN is constructed to complete a classic deep learning task:the recognition of handwritten digit patterns. The simulation results reveal that the exemplary SNN can effectively recognize 10 numbers directly in the optical domain by employing the surrogate gradient algorithm. The theoretical verification of our architecture paves a whole new path for integrated photonic SNNs, with the potential to advance the field of neuromorphic photonic systems and enable more efficient spiking information processing.

关键词： neural networks precise

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Structure-Preserving Recurrent Neural networks for a Class of Birkhoffian Systems

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Journal of Systems Science & Complexity 2024年第2期37卷 441-462页

作者： XIAO Shanshan CHEN Mengyi ZHANG Ruili TANG Yifa LSEC ICMSECAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China School of Mathematics and Statistics Beijing Jiaotong UniversityBeijing 100044China

In this paper,the authors propose a neural network architecture designed specifically for a class of Birkhoffian systems—The Newtonian system.The proposed model utilizes recurrent neural networks(RNNs)and is based on a mathematical framework that ensures the preservation of the Birkhoffian structure.The authors demonstrate the effectiveness of the proposed model on a variety of problems for which preserving the Birkhoffian structure is important,including the linear damped oscillator,the Van der Pol equation,and a high-dimensional example.Compared with the unstructured baseline models,the Newtonian neural network(NNN)is more data efficient,and exhibits superior generalization ability.

关键词： Birkhoffian system k(z,t)-symplectic neural networks recurrent neural network

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Genetic association analysis-based networks and classification of human UCP1 polymorphisms

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Genes & Diseases 2024年第3期11卷 63-65页

作者： Donghai Li State Key Laboratory of Pharmaceutical Biotechnology School of Life SciencesNanjing UniversityJiangsu 210023China

Uncoupling protein 1(UCP1,also known as thermogenin or SLC25A7)plays an important role in the uncoupling of oxidative phosphorylation and adaptive non-shivering thermogenesis(NST).The genomic location for UCP1 is chromosome 4 q31.1:140,555,770--140,568,961(GRCh38/hg38)and its size is 13,192 bases split into 6 exons.In dbSNP,3650 short genetic variations of human UCP1 are documented.In this study,UCP1 serves as an example to construct polymorphism-trait networks and enable a func-tional classification.

关键词： classification. UCP1 networks

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On the use of deep learning for phase recovery

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Light(Science & Applications) 2024年第2期13卷 190-235页

作者： Kaiqiang Wang Li Song Chutian Wang Zhenbo Ren Guangyuan Zhao Jiazhen Dou Jianglei Di George Barbastathis Renjie Zhou Jianlin Zhao Edmund Y.Lam Department of Electrical and Electronic Engineering The University of Hong KongHong Kong SARChina School of Physical Science and Technology Northwestern Polytechnical UniversityXi’anChina Department of Biomedical Engineering The Chinese University of Hong KongHong Kong SARChina School of Information Engineering Guangdong University of TechnologyGuangzhouChina Department of Mechanical Engineering Massachusetts Institute of TechnologyCambridgeMAUSA

Phase recovery(PR)refers to calculating the phase of the light field from its intensity measurements.As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics,PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system.In recent years,deep learning(DL),often implemented through deep neural networks,has provided unprecedented support for computational imaging,leading to more efficient solutions for various PR problems.In this review,we first briefly introduce conventional methods for PR.Then,we review how DL provides support for PR from the following three stages,namely,pre-processing,in-processing,and post-processing.We also review how DL is used in phase image processing.Finally,we summarize the work in DL for PR and provide an outlook on how to better use DL to improve the reliability and efficiency of PR.Furthermore,we present a live-updating resource(https://***/kqwang/phase-recovery)for readers to learn more about PR.

关键词： networks phase optics

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Lensless opto-electronic neural network with quantum dot nonlinear activation

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Photonics Research 2024年第4期12卷 682-690页

作者： WANXIN SHI XI JIANG ZHENG HUANG XUE LI YUYANG HAN SIGANG YANG HAIZHENG ZHONG HONGWEI CHEN Beijing National Research Center for Information Science and Technology(BNRist) Department of Electronic EngineeringTsinghua UniversityBeijing 100084China China Mobile Research Institute Beijing 100053China MIIT Key Laboratory for Low-dimensional Quantum Structure and Devices School of Materials Sciences&EngineeringBeijing Institute of TechnologyBeijing 100081China

With the swift advancement of neural networks and their expanding applications in many fields, optical neural networks have gradually become a feasible alternative to electrical neural networks due to their parallelism, high speed, low latency, and power consumption. Nonetheless, optical nonlinearity is hard to realize in free-space optics, which restricts the potential of the architecture.

关键词： network neural networks

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Three-Soliton Interactions and the Implementation of Their All-Optical Switching Function

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Chinese Physics Letters 2024年第4期41卷 51-58页

作者：伊厚会张鑫石领先姚延立王树斌马国利 School of Intelligent Manufacturing Weifang University of Science and TechnologyWeifang 262700China Institute of Aeronautical Engineering Shandong University of AeronauticsBinzhou 256603China Flight College Shandong University of AeronauticsBinzhou 256603China Tianjin Sino-German University of Applied Sciences Tianjin 300350China

As a key component in all-optical networks,all-optical switches play a role in constructing all-optical switching.Due to the absence of photoelectric conversion,all-optical networks can overcome the constraints of electronic bottlenecks,thereby improving communication speed and expanding their communication bandwidth.We study all-optical switches based on the interactions among three optical solitons.By analytically solving the coupled nonlinear Schr¨odinger equation,we obtain the three-soliton solution to the equation.We discuss the nonlinear dynamic characteristics of various optical solitons under different initial conditions.Meanwhile,we analyze the influence of relevant physical parameters on the realization of all-optical switching function during the process of three-soliton interactions.The relevant conclusions will be beneficial for expanding network bandwidth and reducing power consumption to meet the growing demand for bandwidth and traffic.

关键词： soliton expanding networks

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Multi-scale detector optimized for small target

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Optoelectronics Letters 2024年第4期20卷 243-248页

作者： ZHU Yongchang YANG Sen TONG Jigang WANG Zenghui School of Electrical Engineering and Automation Tianjin University of TechnologyTianjin 300384China Center of Artificial Intelligence and Data Science University of South AfricaFlorida 1709South Africa Department of Electrical Engineering University of South AfricaFlorida 1709South Africa

The effectiveness of deep learning networks in detecting small objects is limited,thereby posing challenges in addressing practical object detection tasks.In this research,we propose a small object detection model that operates at multiple scales.The model incorporates a multi-level bidirectional pyramid structure,which integrates deep and shallow networks to simultaneously preserve intricate local details and augment global features.Moreover,a dedicated multi-scale detection head is integrated into the model,specifically designed to capture crucial information pertaining to small objects.Through comprehensive experimentation,we have achieved promising results,wherein our proposed model exhibits a mean average precision(mAP)that surpasses that of the well-established you only look once version 7(YOLOv7)model by 1.1%.These findings validate the improved performance of our model in both conventional and small object detection scenarios.

关键词： networks details thereby

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Object Helps U-Net Based Change Detectors

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 548-550页

作者： Lan Yan Qiang Li Kenli Li College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Dear Editor,This letter focuses on leveraging the object information in images to improve the performance of the U-Net based change detector.Change detection is fundamental to many computer vision tasks.Although exist... 详细信息

关键词： Net computer networks

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Extended Lorenz majorization and frequencies of distances in an undirected network

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Journal of Data and Information Science 2024年第1期9卷 1-10页

作者： Leo Egghe Hasselt University Diepenbeek 3590Belgium

Purpose:To contribute to the study of networks and graphs.Design/methodology/approach:We apply standard mathematical thinking.Findings:We show that the distance distribution in an undirected network Lorenz majorizes the one of a chain.As a consequence,the average and median distances in any such network are smaller than or equal to those of a chain.Research limitations:We restricted our investigations to undirected,unweighted networks.Practical implications:We are convinced that these results are useful in the study of small worlds and the so-called six degrees of separation property.Originality/value:To the best of our knowledge our research contains new network results,especially those related to frequencies of distances.

关键词： majorization Lorenz curves networks shortest path distance graphs

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Journal of Communications and Information networks

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Journal of Communications and Information networks 2024年第1期9卷 F0003-F0003页

Introduction(background and motivation)Journal of Communications and Information networks(JCIN)is an English languageacademic journal established in 2016,sponsored by Posts&Telecom Press Co.,Ltd.,publishing peer-reviewed and high quality original articles and authoritative reviews that focus on communications and information networks.The mission of the journal is leading the academic frontier with an international view,promoting global communications and serving for the national cyberdevelopment strategy.

关键词： journal frontier networks

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