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Efficient on-chip training of large-scale optical neural network through block adjoint training algorithm

OPTICS EXPRESS 2024年第26期32卷 46633-46648页

作者： Yang, Zhiwei Zhang, Tian Dai, Jian Xu, Kun Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China

MZI-based block optical neural networks (BONNs), which utilize block matrix multiplication to achieve large-scale network models, have garnered significant attention but still lack efficient training algorithms. In this article, by calculating the original field and adjoint field for the block matrices in BONNs and directly updating the phase values of all phase shifters within the optical mesh, we propose an on-chip block adjoint training (BAT) algorithm for large-scale BONNs. To demonstrate the effectiveness of our proposed algorithm, the trained BONNs are applied in image classification tasks for MNIST and SVHN datasets. The calculated results demonstrate that the performance of the BAT algorithm (95.915% for the MNIST dataset and 82.64% for the SVHN dataset) is competitive with the traditional gradient algorithm based on artificial neural networks (96.238% and 84.182%), but the BONNs can infer 1.5 times and 1.3 times faster than artificial neural networks, respectively. By studying the influence of the block size and the inputted position of the padded zero signals, we demonstrate that the BAT algorithm based on the BONNs with 12 block sizes can achieve higher performance by adding the padded zero signals to the same side beside the normal inputted signals. Additionally, we demonstrate that substituting the complete weight matrices with unitary matrices to construct BONNs is an efficient way to reduce both the system area and the required trainable parameters. Finally, we demonstrate the relatively good robustness of the BAT algorithm and the imprecision alleviation method by using on-chip retraining. Notably, our proposed BAT algorithm shows excellent potential for more complex tasks and network models.

关键词： Genetic algorithms Machine vision neural networks optical neural systems Phase shift Stochastic gradient descent

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Reliable adaptive edge-cloud collaborative DNN inference acceleration scheme combining computing and communication resources in optical networks

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JOURNAL OF optical COMMUNICATIONS AND NETWORKING 2023年第10期15卷 750-764页

作者： Yin, Shan Jiao, Yurong You, Chenyu Cai, Mengru Jin, Tianyu Huang, Shanguo Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China

With the continuous development of the Artificial Intelligence of Things, deep neural network (DNN) models require a larger amount of computing capacity. The emerging edge-cloud collaboration architecture in optical networks is proposed as an effective solution, which combines edge computing with cloud computing to provide faster response and reduce the cloud load for compute-intensive tasks. The multi-layered DNN model can be divided into subtasks that are offloaded to edge and cloud servers for computation in this architecture. In addition, as bearer networks for computing capacity, once a server or link in optical networks fails, a large amount of data can be lost, so the robust reliability of the edge-cloud collaborative optical networks is very important. To solve the above problems, we design a reliable adaptive edge-cloud collaborative DNN inference acceleration scheme (RACAI) combining computing and communication resources. We formulate the RACAI into a mixed integer linear programming model and develop a multi-agent deep reinforcement learning algorithm (MADRL-RACIA) to jointly optimize DNN task partitioning, offloading, and protection. The simulation results show that compared with the benchmark schemes, the proposed MADRL-RACIA can provide a guarantee of reliability for more tasks under latency constraints and reduce the blocking probability.

关键词： Task analysis Servers Collaboration optical fiber networks Computational modeling Reliability Cloud computing

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On-Chip Photonic Convolutional Processing Lights Up Fourier neural Operator

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PHOTONICS 2025年第3期12卷 253-253页

作者： Tao, Zilong Ouyang, Hao Yan, Qiuquan Du, Shiyin Hao, Hao Zhang, Jun You, Jie Natl Univ Def Technol Coll Comp Sci & Technol Changsha 410073 Peoples R China Natl Univ Def Technol Inst Quantum Sci & Technol Coll Sci Changsha 410073 Peoples R China Acad Mil Sci PLA China Natl Innovat Inst Def Technol Beijing 100071 Peoples R China

Fourier neural Operators (FNOs) have gained increasing attention for their effectiveness in extracting frequencydomain features and efficiently approximating functions, making them wellsuited for classification tasks. However, the absence of specialized photonic hardware has limited the acceleration of FNO inference. In this study, we introduce what we believe is the first photonic hardware framework dedicated to speeding up the Fourier layer of an FNO. Our approach employs a frequency domain convolutional photonic chip and a micro-ring array chip, achieving 5-bit quantization precision in the inference process. On the Radio ML 2016.10b dataset, our Fourier convolutional neural network achieves a peak identification accuracy of 95.50%, outperforming standard convolution-based networks. These findings highlight the transformative potential of co-designing software and hardware, demonstrating how photonic computing can deliver specialized acceleration for critical AI components and substantially improve inference efficiency. Ultimately, this work lays a foundation for integrating photonic technologies into next-generation AI accelerators, pointing to a promising direction for further research and development in optoelectronic hybrid computing.

关键词： optical neural networks photonic convolutional processing Fourier neural Operator

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Multi-Visual-GRU-Based Survivable computing Power Scheduling in Metro optical networks

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第1期21卷 1302-1315页

作者： Yu, Tiankuo Yang, Hui Yao, Qiuyan Yu, Ao Zhao, Yang Liu, Sheng Li, Yunbo Zhang, Jie Cheriet, Mohamed Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China Kuaishou Technol Co Ltd Network Technol Ctr Beijing 100085 Peoples R China China Mobile Res Inst Dept Fundamental Network Technol Beijing 100053 Peoples R China Univ Quebec Ecole Technol Super Lab Imagery Vis & Artificial Intelligence Montreal PQ H2L 2C4 Canada

The computing power network (CPN) has emerged as a promising networking paradigm in recent times. Since the characteristics of high bandwidth, low delay and high reliable communication, optical networks have been identified as potential frameworks for establishing the CPN infrastructure across metropolitan areas. In CPN of metropolitan areas, owing to the low delay demands of computing power requests, the traffic of computing power requests is more likely to be burst than others. The burst traffic leads to the exponential increase of the traffic loads instantly, which leads to soft failure in the form of overloading and breaks the tradeoff between resource utilization and load balance, which all decline the survivability severely. To solve the problems above, this article proposes an architecture named metro optical computing power network (MO-CPN) to achieve collaborative scheduling in MO-CPN. And proposed a survivable computing power scheduling scheme during burst traffic. Where a multi visual gate recurrent unit (MV-GRU) neural network based on error feedback is constructed to achieve high-precision of burst traffic prediction. According to the burst traffic prediction, a protection threshold to avoid the overloading of nodes is set. And aiming at multi-objectives of low delay and load balancing, the computing power, spectrum resources, burst traffic and protection threshold are used as constraints in the scheduling scheme. The experimental results reveal that our approach can significantly enhance the survivability during burst traffic and improve the utilization of resources. The proposed scheme can also lower the blocking probability and average processing delay, which has strong robustness and reliability.

关键词： Processor scheduling Delays optical fiber networks Urban areas Resource management Computer architecture Scheduling MO-CPN burst traffic prediction multi-visual-GRU computing power scheduling

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optical matrix computing for optical forward error correction encoding

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OPTICS EXPRESS 2024年第17期32卷 29149-29158页

作者： Guo, Tianyan Du, Jiangbing Zhang, Wenjia He, Zuyuan Shanghai Jiao Tong Univ State Key Lab Adv Opt Commun Syst & Networks Shanghai 200240 Peoples R China

Forward error correction (FEC) has been widely used in today's communication systems, implemented by electronics. For optical communications, on-the-fly optical processing is highly desired for reduced optical-electrical-optical (OEO) conversions, so as to reduce the latency and cost. In this work, we present a proof-of-concept study of an optical FEC encoding method based on optical matrix computing by wavelength-division multiplexing (WDM) with time-wavelength interleaved manipulation. The optical FEC encoder realized by optical fiber devices features nearly equivalent net encoding gain compared with the theoretical electrical encoder. The experimental results indicate effective error correction, with 2-dB net gain in on-off keying (OOK) transmission at 20 Gb/s, supporting the realization of transmission-computingmerged systems. Meanwhile, the proposed method also supports the whole realization of FEC with both encoding and decoding.

关键词： Bit error rate Fiber optic communications optical computing optical neural systems optical signals Signal processing

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Unconventional computing Based on Four Wave Mixing in Highly Nonlinear Waveguides

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IEEE JOURNAL OF QUANTUM ELECTRONICS 2024年第4期60卷 1页

作者： Sozos, Kostas Deligiannidis, Stavros Mesaritakis, Charis Bogris, Adonis Univ West Attica Dept Informat & Comp Engn Athens 12243 Greece Univ Aegean Dept Informat & Commun Syst Engn Karlovassi 83200 Samos Greece

In this work we numerically analyze a photonic unconventional accelerator based on the four-wave mixing effect in highly nonlinear waveguides. The proposed scheme can act as a fully analogue system for nonlinear signal processing directly in the optical domain. By exploiting the rich Kerr-induced nonlinearities, multiple nonlinear transformations of an input signal can be generated and used for solving complex nonlinear tasks. We first evaluate the performance of our scheme in the Santa-Fe chaotic time-series prediction. The true power of this processor is revealed in the all-optical nonlinearity compensation in an optical communication scenario where we provide results superior to those offered by strong machine learning algorithms with reduced power consumption and computational complexity. Finally, we showcase how the FWM module can be used as a reconfigurable nonlinear activation module being capable of reproducing characteristic functions such as sigmoid or rectified linear unit.

关键词： optical communications optical signal processing four-wave mixing optical neural networks nonlinear optics optical communications optical signal processing four-wave mixing optical neural networks nonlinear optics

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Performance Enhancement of Reservoir computing Based on Fabry-Perot Laser as All-optical Reconfigurable Nonlinear Node

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2024年第22期42卷 7785-7794页

作者： Banovic, Mladen Z. Crnjanski, Jasna V. Krstic, Marko M. Gvozdic, Dejan M. Univ Belgrade Sch Elect Engn Belgrade 11120 Serbia

The paper explores the performance enhancement of a photonic reservoir computer through the reconfigurability of all-optical nonlinear node based on Fabry-Perot laser diode. By benchmarking various tasks with differing computational demands, we demonstrate how reconfigurability offers flexible computational capabilities, enabling the reservoir computer to adapt to specific tasks while striking an optimal balance between nonlinear transformation and linear memory. We achieve the state-of-the-art results using a basic delay-line reservoir computer concept with a small number of virtual nodes (similar to 30 nodes).

关键词： Reservoirs Artificial neural networks optical feedback optical bistability Task analysis Photonics Benchmark testing All-optical delay-based reservoir computing reconfigurable activation function semiconductor laser bistability time-series prediction

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Artificial neural networks for photonic applications-from algorithms to implementation: tutorial

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ADVANCES IN OPTICS AND PHOTONICS 2023年第3期15卷 739-834页

作者： Freire, Pedro Manuylovich, Egor Prilepsky, Jaroslaw E. Turitsyn, Sergei K. Aston Univ Aston Inst Photon Technol Birmingham B4 7ET England

This tutorial-review on applications of artificial neural networks in photonics targets a broad audience, ranging from optical research and engineering communities to computer science and applied mathematics. We focus here on the research areas at the interface between these disciplines, attempting to find the right balance between technical details specific to each domain and overall clarity. First, we briefly recall key properties and peculiarities of some core neural network types, which we believe are the most relevant to photonics, also linking the layer's theoretical design to some photonics hardware realizations. After that, we elucidate the question of how to fine-tune the selected model's design to perform the required task with optimized accuracy. Then, in the review part, we discuss recent developments and progress for several selected applications of neural networks in photonics, including multiple aspects relevant to optical communications, imaging, sensing, and the design of new materials and lasers. In the following section, we put a special emphasis on how to accurately evaluate the complexity of neural networks in the context of the transition from algorithms to hardware implementation. The introduced complexity characteristics are used to analyze the applications of neural networks in optical communications, as a specific, albeit highly important example, comparing those with some benchmark signal-processing methods. We combine the description of the well-known model compression strategies used in machine learning, with some novel techniques introduced recently in optical applications of neural networks. It is important to stress that although our focus in this tutorial-review is on photonics, we believe that the methods and techniques presented here can be handy in a much wider range of scientific and engineering applications. (c) 2023 Optica Publishing Group

关键词： Fiber optic communications Laser materials optical communications optical computing optical networks Polarization mode dispersion

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optical neural networks:Analysis and Prospects for 5G Applications

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Computers, Materials & Continua 2023年第12期77卷 3723-3740页

作者： Doaa Sami Khafaga Zongming Lv Imran Khan Shebnam M.Sefat Amel Ali Alhussan Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia School of Information and Finance Xuancheng Vocational&Technical CollegeXuancheng242000China Department of Electrical Engineering University of Engineering and TechnologyP.O.Box 814PeshawarPakistan Islamic University Centre for Scientific Research The Islamic UniversityNajafIraq Department of Computer Science Independent UniversityDhaka1208Bangladesh

With the capacities of self-learning,acquainted capacities,high-speed looking for ideal arrangements,solid nonlin-ear fitting,and mapping self-assertively complex nonlinear relations,neural systems have made incredible advances and accomplished broad application over the final *** one of the foremost conspicuous methods for fake insights,neural systems are growing toward high computational speed and moo control *** to the inborn impediments of electronic gadgets,it may be troublesome for electronic-implemented neural systems to make the strides these two exhibitions *** neural systems can combine optoelectronic procedures and neural organization models to provide ways to break the *** paper outlines optical neural networks of feedforward repetitive and spiking models to give a clearer picture of history,wildernesses,and future optical neural *** framework demonstrates neural systems in optic communication with the serial and parallel *** graphene-based laser structure for fiber optic communication is *** comparison of different balance plans for photonic neural systems is made within the setting of hereditary calculation and molecule swarm *** expansion,the execution comparison of routine photonic neural,time-domain with and without extending commotion is additionally *** challenges and future patterns of optical neural systems on the growing scale and applications of in situ preparing nonlinear computing will hence be uncovered.

关键词： Optoelectronics 5G networks free-space optical transducer neural networks

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High-speed multifunctional photonic memory on a foundry-processed photonic platform

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OPTICA 2025年第1期12卷 31-38页

作者： Kari, Sadra rahimi Tamura, Marcus Guo, Zhimu Huang, Yi-siou Sun, Hongyi Lian, Chuanyu Nobile, Nicholas Erickson, John Moridsadat, Maryam Ocampo, C. A. R. L. O. S. A. RiOS Shastri, Bhavin J. Youngblood, Nathan Univ Pittsburgh Dept Elect & Comp Engn Pittsburgh PA 15261 USA Queens Univ Ctr Nanophoton Dept Phys Engn Phys & Astron Kingston ON K7L 3N6 Canada Univ Maryland Dept Mat Sci & Engn College Pk MD 20742 USA Univ Maryland Inst Res Elect & Appl Phys College Pk MD 20742 USA

The integration of computing with memory is essential for distributed, massively parallel, and adaptive architectures such as neural networks in artificial intelligence (AI). Accelerating AI can be achieved through photonic computing, but it requires nonvolatile photonic memory capable of rapid updates during on-chip training sessions or when new information becomes available during deployment. Phase-change materials (PCMs) are promising for providing compact, nonvolatile optical weighting;however, they face limitations in terms of bit precision, programming speed, and cycling endurance. Here, we propose a novel photonic memory cell that merges nonvolatile photonic weighting using PCMs with high-speed, volatile tuning enabled by an integrated PN junction. Our experiments demonstrate that the same PN modulator, fabricated via a foundry-compatible process, can achieve dual functionality. It supports coarse programmability for setting initial optical weights and facilitates high-speed fine-tuning to adjust these weights dynamically. The result shows a 400-fold increase in volatile tuning speed and a 10,000-fold enhancement in efficiency. This multifunctional photonic memory with volatile and nonvolatile capabilities could significantly advance the performance and versatility of photonic memory cells, providing robust solutions for dynamic computing environments. (c) 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： Electron beam lithography Laser sources neural networks optical computing optical confinement Quantum computation

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