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Annual Conference of the IEEE-Photonics-Society (IPC)

作者： Sumon, Md Saiful Islam Crisan, Mihai You, Weicheng Sankar, Shrivatch Faruque, Imad I. Dwivedi, Sarvagya Arafin, Shamsul Ohio State Univ Dept Elect & Comp Engn Columbus OH 43201 USA Univ Bristol Quantum Engn Technol Labs QET Labs Bristol BS8 1TL Avon England Rockley Photon Pasadena CA 91101 USA

ISBN: (纸本)9798350347227

We propose an all-optical neural network where signal processing requires no optical-to- electrical conversion. Weight multiplication, addition, and nonlinear activation of artificial neurons are performed in the photonic domain. Successful implementation will advance photonic neuromorphic computing, enabling practical solutions in artificial intelligence-driven tasks.

关键词： neuromorphic computing optical neural networks (ONN) photonic neuron

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Research on real-time image processing system combining convolutional neural network and edge computing

Research on real-time image processing system combining conv...

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2024 International Conference on Physics, Photonics, and optical Engineering, ICPPOE 2024

作者： Liang, Lu Hebei University of Architecture Hebei Zhangjiakou China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

With the rise of the Internet of Things (IoT) and edge computing technologies, traditional cloud-dependent convolutional neural network (CNN) image processing methods are facing the challenges of latency and bandwidth bottlenecks. In this study, we propose a real-time image processing system that combines CNN and edge computing, which enables the CNN model to run efficiently on resource-constrained edge devices through model pruning and quantisation optimisation. Experimental results show that the system significantly outperforms traditional methods in terms of inference speed, processing frame rate, and energy-efficiency ratio, especially in application scenarios requiring high real-time performance, such as intelligent surveillance, autonomous driving, and industrial inspection. In addition, the system shows good robustness and stability under different network conditions. This study provides an efficient and low-latency solution for image processing in edge computing environments and lays the foundation for future intelligent applications. © 2025 SPIE.

关键词： Convolutional neural networks

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Review of OFC 2022 optical networks and Communications Conference Hybrid (Virtual/In-Person) Conference: 6-10 March 2022, San Diego, CA

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FIBER AND INTEGRATED OPTICS 2022年第3-4期41卷 63-82页

作者： Hodara, Henri Mock, Patrick Skaljo, Edvin Symbioptix Inc Dana Point CA USA RAM Photon LLC San Diego CA USA Univ Sarajevo Dept Phys Sarajevo Bosnia & Herceg Univ Sarajevo BH Telecom Sarajevo Bosnia & Herceg

We review the latest developments in Fiber-optic Telecommunications and networks presented at OFC 2022 and compare the progress accomplished since the previous year conference, OFC 2021. In particular, we note this year's emphasis on the application of neural networks (NN) as part of Artificial Intelligence (AI) and Machine Learning (ML), as well as continued research in quantum communications and key distribution. We also expect future conferences to include papers about optical variants of quantum computers, a field of great interest that can exploit many quantum systems, as well as papers on continuing developments in High-Speed Communications beyond 800 Gb/s, and increased use of coherent modulation and detection in Data Centers Interconnect, Cloud and Edge computing, the latter in support of 5 G Internet Of Things (IOT).

关键词： Artificial Intelligence coherent communications Cloud and Edge computing Data Center Interconnects fiber optics neural networks Machine Learning quantum communications quantum computing

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Light in AI: Toward Efficient Neurocomputing With optical neural networks-A Tutorial

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第6期69卷 2581-2585页

作者： Gu, Jiaqi Feng, Chenghao Zhu, Hanqing Chen, Ray T. Pan, David Z. Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA

In the post Moore's era, conventional electronic digital computing platforms have encountered escalating challenges to support massively parallel and energy-hungry artificial intelligence (AI) workloads. Intelligent applications in data centers, edge devices, and autonomous vehicles have restricted requirements in throughput, power, and latency, which raises a high demand for a revolutionary neurocomputing solution. optical neural network (ONN) is a promising hardware platform that could represent a paradigm shift in efficient neurocomputing with its ultra-fast speed, high parallelism, and low energy consumption. In recent years, efforts have been made to facilitate the ONN design stack and push forward the practical application of optical neural accelerators. In this tutorial, we give an overview of state-of-the-art cross-layer co-design methodologies for scalable, robust, and self-learnable ONN designs across the circuit, architecture, and algorithm levels. Besides, we analyze challenges and highlight emerging directions targeting next-generation optics for AI.

关键词： Photonics Training Robustness Computer architecture optical network units Artificial intelligence Integrated optics optical neural network optical computing scalability robustness trainability software-hardware co-design

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optical neural networks based on perovskite solar cells

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Photonics Research 2025年第2期13卷 382-386页

作者： KAICHENG ZHANG JONATHON HARWELL DAVIDE PIERANGELI CLAUDIO CONTI ANDREA DI FALCO School of Physics and Astronomy University of St AndrewsSt Andrews KY169SSScotland Institute for Complex Systems National Research CouncilRome 00185Italy Department of Physics Sapienza Universitàdi RomaRome 00185Italy

optical neural networks(ONNs)are a class of emerging computing platforms that leverage the properties of light to perform ultra-fast computations with ultra-low energy *** often use CCD cameras as the output *** this work,we propose the use of perovskite solar cells as a promising alternative to imaging cameras in ONN *** cells are ubiquitous,versatile,highly customizable,and can be fabricated quickly in *** large acquisition area and outstanding efficiency enable them to generate output signals with a large dynamic range without the need for *** we have experimentally demonstrated the feasibility of using perovskite solar cells for capturing ONN output states,as well as the capability of single-layer random ONNs to achieve excellent performance even with a very limited number of *** results show that the solar-cell-based ONN setup consistently outperforms the same setup with CCD cameras of the same *** findings highlight the potential of solar-cell-based ONNs as an ideal choice for automated and battery-free edge-computing applications.

关键词： imaging cameras class emerging computing platforms edge computing ccd cameras optical neural networks onns optical neural networks CCD cameras perovskite solar cells

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Parameterization of optical properties for liquid cloud droplets containing black carbon based on neural network

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OPTICS EXPRESS 2023年第24期31卷 40124-40141页

作者： Li, Jun Hang, Feng Liu, Jia Li, Wenwen Wu, Kun Hu, Shuai Lin, Han Shanghai Qi Zhi Inst Shanghai Peoples R China Fudan Univ Dept Atmospher & Ocean Sci Shanghai Peoples R China Fudan Univ Inst Atmospher Sci Shanghai Peoples R China Hebei Univ Sch Qual & Tech Supervis Baoding Peoples R China Nanjing Univ Informat Sci & Technol Collaborat Innovat Ctr Forecast & Evaluat Meteorol Key Lab Meteorol Disaster Minist Educ Nanjing Peoples R China Natl Univ Def Technol Sch Meteorol & Oceanog Changsha Peoples R China Fuzhou Univ Key Lab Spatial Data Min & Informat Sharing Minist Educ Natl & Local Joint Engn Res Ctr Satellite Geospati Fuzhou Peoples R China

This paper introduces a novel back propagation (BP) neural network method to accurately characterize optical properties of liquid cloud droplets, including black carbon. The model establishes relationships between black carbon volume fraction, wavelength, cloud effective radius, and optical properties. Evaluated on a test set, the value of the root mean square error (RMSE) of the asymmetry factor, extinction coefficient, single-scattering albedo, and the first 4 moments of the Legendre expansion of the phase function are less than 0.003, with the maximum mean relative error (MRE) reaching 0.2%, which are all better than the traditional method that only uses polynomials to fit the relationship between the effective radius and optical properties. Notably, the BP neural network significantly compresses the optical property database size by 37,800 times. Radiative transfer simulations indicate that mixing black carbon particles in water clouds reduces the top-of-atmosphere (TOA) reflectance and heats the atmosphere. However, if the volume fraction of black carbon is less than 10-6, the black carbon mixed in the water cloud has a tiny effect on the simulated TOA reflectance.

关键词： Machine learning neural networks optical computing optical neural systems optical properties Remote sensing

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Compact, efficient, and scalable nanobeam core for photonic matrix-vector multiplication

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OPTICA 2024年第2期11卷 190-196页

作者： Zhang, Jiahui Wu, Bo Cheng, Junwei Dong, Jianji Zhang, Xinliang Huazhong Univ Sci & Technol Wuhan Natl Lab Optoelect Wuhan 430074 Peoples R China Opt Valley Lab Wuhan 430074 Peoples R China

optical neural networks have emerged as a promising avenue for implementing artificial intelligence applications, with matrix computations being a crucial component. However, the existing implementations based on microring resonators (MRRs) face bottlenecks in integration, power efficiency, and scalability, hindering the practical applications of wavelength division multiplexing (WDM)-based matrix -vector multiplications at the hardware level. Here we present a photonic crystal nanobeam cavity (PCNC) matrix core. Remarkably compact with dimensions reduced to 20 mu m x 0.5 mu m, the PCNC unit exhibits a thermal tuning efficiency more than three times that of MRRs. Crucially, it is immune to the free spectral range constraint, thus able to harness the wealth of independent wavelength channels provided by WDM. A 3 x 3 PCNC core chip is demonstrated for animal face recognition and a six -channel chip is employed for handwritten digit classification to demonstrate the scalability. The PCNC solution holds immense promise, offering a versatile platform for next -generation photonic artificial intelligence chips. (c) 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： Bend loss Effective refractive index optical computing optical neural systems Photonic crystal cavities Wavelength division multiplexing

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Experimental Demonstration of Delay-Weight Learning and Pattern Classification With a FP-SA-Based Photonic Spiking Neuron Chip

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2024年第5期42卷 1497-1503页

作者： Han, Yanan Xiang, Shuiying Gao, Shuang Zhang, Yuna Shi, Yuechun Chen, Xiangfei Hao, Yue Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Xidian Univ Sch Microelect State Key Discipline Lab Wide Bandgap Semicond Tec Xian 710071 Peoples R China Yongjiang Lab Ningbo 315202 Peoples R China Nanjing Univ Inst Opt Commun Engn Coll Engn & Appl Sci Key Lab Intelligent Opt Sensing & ManipulatMinis Nanjing 210023 Peoples R China Xidian Univ Sch Microelect State Key Discipline Lab Wide Bandgap Semicond Tec Xian 710071 Peoples R China

optical acceleration of neuromorphic computing has emerged as a platform for achieving low-latency and energy-efficient computation. Due to the rich dynamics, the integrated photonics architecture provides a promising way for implementations of nonlinear computing. In this article, we demonstrate pattern classification of 2 benchmark datasets, the Iris dataset and the Wisconsin Breast Cancer (WBC) dataset, based on the nonlinear neuron-like dynamics of an integrated Fabry-Perot laser chip with a saturable absorption region (FP-SA). The effectiveness and robustness of the algorithm are firstly verified by numerical simulations. With the help of an optimized delay learning method, efficient learning can be achieved based on a single neuron, achieving 96% and 92% in classification accuracy of Iris and WBC dataset respectively. Then, the hardware-algorithm collaborative computing is demonstrated based on a single FP-SA laser chip. The classification accuracy of Iris and WBC dataset could reach 94.67% and 88%, respectively, having a relative low loss compared to the simulation results. This work provides an efficient solution for classification tasks based on optical SNN.

关键词： Delay learning FP-SA laser optical spiking neural network photonic spiking neuron

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Orbital angular momentum beams multiplexing and demultiplexing using hybrid optical-electronic neural network

Orbital angular momentum beams multiplexing and demultiplexi...

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Conference on Photonic computing - From Materials and Devices to Systems and Applications

作者： Ye, Jiachi Kang, Haoyan Altaleb, Salem Wang, Hao Patil, Chandraman Madadi-Kandjani, Ehsan Heidari, Elham Asadizanjani, Navid Dalir, Hamed Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA Calif State Univ Long Beach Dept Mech & Aerosp Engn Long Beach CA 90840 USA

ISBN: (纸本)9781510678866;9781510678873

Recent advancements in optical communications have explored the use of spatially structured beams, especially orbital angular momentum (OAM) beams, to achieve high-capacity data transmission. Traditional electronic convolutional neural networks (CNNs), while effective, face significant challenges in demultiplexing OAM beams efficiently, notably their high power consumption and substantial computational time, which can limit real-time processing capabilities in high-speed optical communication systems. In this study, we propose a hybrid optical-electronic CNN that integrates Fourier optics convolution for intensity recognition-based demultiplexing of multiplexed OAM beams under simulated atmospheric turbulence. Experimental results showed that the proposed hybrid neural network system achieves a 69% demultiplexing accuracy under strong turbulence conditions while exhibiting a three times reduction in training time compared to all-electronic CNNs. This study underscores the potential of a hybrid optical-electronic neural network to enhance both performance and efficiency in OAM-based optical communication systems.

关键词： OAM demultiplexing hybrid CNN Fourier optics atmospheric turbulence

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Multi-functional broadband diffractive neural network with a single spatial light modulator

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APL PHOTONICS 2025年第1期10卷

作者： Li, Bolin Zhu, Yinfei Fei, Jinlei Zheng, Runshi Gu, Min Lin, Jian Univ Shanghai Sci & Technol Sch Artificial Intelligence Sci & Technol Shanghai Peoples R China Univ Shanghai Sci & Technol Inst Photon Chips Shanghai Peoples R China

Diffractive neural networks (DNNs) are emerging as a novel optical computing architecture that combines wave optics with deep-learning methods for high-speed parallel information processing. Herein, we report a reflection type, multi-functional, broadband DNN design. It consists of two phase-modulation layers based on a single spatial light modulator and a mirror facing it. The power efficiency of this design is more than 16 times higher than that of the cascaded structure utilizing beam splitters. It can function either as a two-layer DNN or a one-layer DNN with the other serving as an information input layer. Single- and dual-wavelength filtering and focusing, as well as spatial wavelength demultiplexing of supercontinuum, are experimentally demonstrated using the two-layer DNN, whereas the one-layer DNN is experimentally demonstrated by the classification of hand-written digits, which are input by the first layer via holographic imaging. The designed DNN could operate independently or be readily integrated with other optical systems and may find applications in spectroscopy, microscopy, and information technology. (c) 2025 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC) license (https://***/licenses/by-nc/4.0/).

关键词： Light modulation

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