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Quantum optical Convolutional neural network: A Novel Image Recognition Framework for Quantum computing

IEEE ACCESS 2021年 9卷 103337-103346页

作者： Parthasarathy, Rishab Bhowmik, Rohan T. Harker Sch San Jose CA 95129 USA

Large machine learning models based on Convolutional neural networks (CNNs) with rapidly increasing numbers of parameters are being deployed in a wide array of computer vision tasks. However, the insatiable demand for computing resources required to train these models is fast outpacing the advancement of classical computing hardware, and new frameworks including optical neural networks (ONNs) and quantum computing are being explored as future alternatives. In this work, we report a novel quantum computing based deep learning model, the Quantum optical Convolutional neural network (QOCNN), to alleviate the computational bottleneck in future computer vision applications. Using the MNIST dataset, we have benchmarked this new architecture against a traditional CNN model based on the seminal LeNet architecture. We have also compared the performance against previously reported ONNs, the GridNet and ComplexNet, and a Quantum optical neural network (QONN) that we built by combining the ComplexNet with quantum-inspired sinusoidal nonlinearities. Our work extends the prior research on QONN by adding quantum convolution and pooling layers. We have evaluated the models using the metrics of accuracies, confusion matrices, Receiver Operating Characteristic curves, and Matthews Correlation Coefficients. The performance of the models were similar, and the metrics indicated that the new QOCNN model is robust. Finally, we estimated the gains in computational efficiencies from executing this novel framework on a quantum computer, concluding that switching to a quantum computing based approach to deep learning may result in comparable accuracies to classical models while simultaneously achieving unprecedented boosts in computational performances and power allocation.

关键词： Quantum computing Computational modeling Artificial intelligence optical imaging optical network units optical computing Convolutional neural networks Deep learning computer vision image recognition convolutional neural networks quantum computing quantum photonics

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Simulating an optical neural network for Deep Learning in Edge computing

Simulating an Optical Neural Network for Deep Learning in Ed...

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作者： Cochrane, Jared Massachusetts Institute of Technology

学位级别：硕士

Deep learning has risen to prominence in fields from medicine to autonomous vehicles. This rise has been driven by improvements in parallel computing from graphics processing units (GPUs) as well as large data sets. Applying deep learning to edge computing is challenging because deep neural network (DNN) hardware must not only possess the needed computational power but must also satisfy size, weight, and power (SWaP) constraints for practical deployment. Many DNNs require a GPU or data center to run, both of which are too large to fit onto edge devices. Here, an optical neural network (ONN) accelerator called netcast is simulated on two real-world machine vision applications: MNIST digit classification and scene recognition. The netcast ONN enables large DNNs to run on SWaP-limited edge devices with significantly less energy needed to run inference compared to digital models. Software simulations are used to assess netcast’s performance on MNIST classification and scene recognition relative to digital networks. Using an accuracy per energy consumption figure of merit (FOM), the simulations indicate that netcast is able to outperform digital electronics on average by over three orders of magnitude. Netcast’s strong performance relative to its digital counterparts indicates that it will enable the novel deployment of large DNNs to edge applications in a way that would be infeasible using current digital electronics. Netcast’s novel applications give rise to a host of policy challenges, one of which focuses on defining and applying acceptable performance metrics to optically enabled deep learning.

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optical computing for Deep neural network Acceleration: Foundations, Recent Developments, and Emerging Directions

arXiv

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arXiv 2024年

作者： Pasricha, Sudeep Department of Electrical and Computer Engineering Colorado State University United States

Emerging artificial intelligence applications across the domains of computer vision, natural language processing, graph processing, and sequence prediction increasingly rely on deep neural networks (DNNs). These DNNs require significant compute and memory resources for training and inference. Traditional computing platforms such as CPUs, GPUs, and TPUs are struggling to keep up with the demands of the increasingly complex and diverse DNNs. optical computing represents an exciting new paradigm for light-speed acceleration of DNN workloads. In this article, we discuss the fundamentals and state-of-the-art developments in optical computing, with an emphasis on DNN acceleration. Various promising approaches are described for engineering optical devices, enhancing optical circuits, and designing architectures that can adapt optical computing to a variety of DNN workloads. Novel techniques for hardware/software co-design that can intelligently tune and map DNN models to improve performance and energy-efficiency on optical computing platforms across high performance and resource constrained embedded, edge, and IoT platforms are also discussed. Lastly, several open problems and future directions for research in this domain are highlighted. © 2024, CC BY-NC-ND.

关键词： Silicon photonics

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Predictive Modelling of optical Beams From Grating Structure Using Deep neural network

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2024年第2期42卷 696-703页

作者： Lim, Yu Dian Zhao, Peng Guidoni, Luca Likforman, Jean-Pierre Tan, Chuan Seng Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore ASTAR Inst Microelect Singapore 117685 Singapore Univ Paris Lab Mat & Phenomenes Quant MPQ F-75205 Paris France

Integrated grating structure has been widely used in the optical addressing of trapped ion qubits in quantum computing. For accurate optical addressing, the optical properties of light beam coupled out from the grating should be thoroughly understood. In this study, deep neural network (DNN) modeling is used to predict the optical properties of light from silicon nitride (SiN) grating. DNN models with various number of layers (L) and nodes per layer (N) are attempted and optimized. Both overfitted and well-fitted L/N combinations are addressed. The APE values of the overfitted DNNs can reach as low as 5.2%, while the APE values of the well-fitted DNN reaches as low as 7.2%.

关键词： Gratings Laser beams optical beams Artificial neural networks Particle beam optics optical computing Three-dimensional displays Deep learning deep neural networks photonics integrated circuits quantum computing silicon photonics

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High-Efficiency Reconfigurable optical Convolutional neural network

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2025年第9期43卷 4076-4085页

作者： Li, Junshen Meng, Xiangyan Zhang, Jingyun Li, Wei Shi, Nuannuan Li, Ming Chinese Acad Sci Inst Semicond State Key Lab Optoelect Mat & Devices Beijing 100083 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Univ Chinese Acad Sci Sch Mat Sci & Optoelect Technol Beijing 100190 Peoples R China Tencent WeChat Pay Lab33 Shenzhen 518054 Peoples R China

As the intricacy of network models escalates and the volume of data swells exponentially, the demands on the processing capabilities of neural networks have become increasingly rigorous. optical computing, with the excellent performance of extremely large bandwidth and ultra-low loss, is poised to be a disruptive technology that supports the needs of the next generation high performance artificial intelligence. In this paper, an optical convolutional neural network, combining a novel architectural design with a compatible data encoding technique, is introduced to achieve superior data efficiency by harnessing the dimensions of wavelength, temporal and parallel space. Utilizing 3x3 convolution kernel on 28x28-pixel images, the proportion of valid data in the output temporal waveform achieves as high as 92.86%. Furthermore, by employing multi-kernel parallel for processing larger size image, the data efficiency is ulteriorly enhanced to an astonishing 99%, and the potential computing power is increased to 10.51 TOPS, showcasing a remarkable leap in performance. During the experiment, five reconfigurable kernels including two positive and three real-value kernels, in conjunction with an electrical fully connected layer, successfully classified handwritten digital images from "0" to "9" in Modified National Institute of Standards and Technology (MNIST) database, achieving an accuracy of 92.81%. The defining feature of the proposed structure is that high-efficiency reconfigurable optical convolutional neural networks streamline the preprocessing and extraction of the data, markedly reducing the meaninglessdata ratio. This innovation, offering a new architecture for optical convolutional neural networks, not only conserves computing power but also improve processing speed, which is expected to alleviate the expanding demand for computing performance in deep learning.

关键词： optical imaging optical computing optical fibers Convolutional neural networks Convolution optical fiber networks Kernel optical network units optical crosstalk Encoding deep learning optical computing optical neural network

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Intelligent Photonics:A Disruptive Technology to Shape the Present and Redefine the Future

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Engineering 2025年第3期46卷 186-213页

作者： Danlin Xu Yuchen Ma Guofan Jin Liangcai Cao State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision InstrumentsTsinghua UniversityBeijing 100084China

Artificial intelligence(AI)has taken breathtaking leaps forward in recent years,evolving into a strategic technology for pioneering the *** growing demand for computing power—especially in demanding inference tasks,exemplified by generative AI models such as ChatGPT—poses challenges for conventional electronic computing *** in photonics technology have ignited interest in investigating photonic computing as a promising AI computing *** the profound fusion of AI and photonics technologies,intelligent photonics is developing as an emerging interdisciplinary field with significant potential to revolutionize practical *** learning,as a subset of AI,presents efficient avenues for optimizing photonic design,developing intelligent optical systems,and performing optical data processing and *** AI in photonics can empower applications such as smartphone cameras,biomedical microscopy,and virtual and augmented reality ***,leveraging photonics-based devices and systems for the physical implementation of neural networks enables high speed and low energy *** photonics technology in AI computing is expected to have a transformative impact on diverse fields,including optical communications,automatic driving,and astronomical ***,recent advances in intelligent photonics are presented from the perspective of the synergy between deep learning and metaphotonics,holography,and quantum *** review also spotlights relevant applications and offers insights into challenges and prospects.

关键词： Artificial intelligence optical neural network Deep learning Metaphotonics Holography Quantum photonics

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Efficient O-type mapping and routing of large-scale neural networks to torus-based ONoCs

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JOURNAL OF optical COMMUNICATIONS AND networkING 2024年第9期16卷 918-928页

作者： Yao, Qiuyan Meng, Daqing Yang, Hui Feng, Nan Zhang, Jie Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China 54th Res Inst CETC Hebei Key Lab Photon Informat Technol & Applicat Shijiazhuang 050081 Peoples R China

The rapid development of artificial intelligence has accelerated the arrival of the era of large models. Artificial-neural-network-based large models typically have millions to billions of parameters, and their training and reasoning processes put strict requirements on hardware, especially at the chip level, in terms of interconnection bandwidth, processing speed, latency, etc. The optical network-on-chip (ONoC) is a new interconnection technology that connects IP cores through a network of optical waveguides. Due to its incomparable advantages such as low loss, high throughput, and low delay, this communication mode has gradually become the key technology to improve the efficiency of large models. At present, the ONoC has been used to reduce the interconnection complexity of neural network accelerators, where neural network models are reshaped to map into the process elements of the ONoC and communicate at high speed on chip. In this paper, we first propose a torus-based O-type mapping strategy to realize efficient mapping of neuron groups to the chip. Additionally, an array congestion information-based low-congestion arbitrator is designed and then a multi-path low-congestion routing algorithm named TMLA is presented to alleviate array congestion and disperse the routing pressure of each path. Results demonstrate that the proposed mapping and routing scheme can reduce the average network delay without additional loss when the injection rate is relatively large, which provides a valuable reference for the research of neural network acceleration.

关键词： Biological neural networks Neurons Routing optical computing network topology Topology optical fiber networks

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Multiplexable all-optical nonlinear activator for optical computing

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OPTICS EXPRESS 2024年第10期32卷 18161-18174页

作者： Teng, Caihong Zhang, Xuhao Tang, Jindao Ren, Aobo Deng, Guangwei Wu, Jiang Wang, Zhiming Univ Elect Sci & Technol China UESTC Inst Fundamental & Frontier Sci Chengdu 610054 Peoples R China

As an alternative solution to surpass electronic neural networks, optical neural networks (ONNs) offer significant advantages in terms of energy consumption and computing speed. Despite the optical hardware platform could provide an efficient approach to realizing neural network algorithms than traditional hardware, the lack of optical nonlinearity limits the development of ONNs. Here, we proposed and experimentally demonstrated an all -optical nonlinear activator based on the stimulated Brillouin scattering (SBS). Utilizing the exceptional carrier dynamics of SBS, our activator supports two types of nonlinear functions, saturable absorption and rectified linear unit (Relu) models. Moreover, the proposed activator exhibits large dynamic response bandwidth ( similar to 11.24 GHz), low nonlinear threshold ( similar to 2.29 mW), high stability, and wavelength division multiplexing identities. These features have potential advantages for the physical realization of optical nonlinearities. As a proof of concept, we verify the performance of the proposed activator as an ONN nonlinear mapping unit via numerical simulations. Simulation shows that our approach achieves comparable performance to the activation functions commonly used in computers. The proposed approach provides support for the realization of all -optical neural networks.

关键词： Machine learning neural networks Numerical simulation optical computing optical limiting optical neural systems

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Incremental Distillation Physics-Informed neural network (IDPINN) Accurately models the Evolution of optical Solitons

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JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY 2024年 1-15页

作者： Zhang, Zhiyang Liu, Muwei Liu, Wenjun Beijing Univ Posts & Telecommun Sch Sci State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China North China Elect Power Univ Hebei Key Lab Phys & Energy Technol Baoding 071000 Peoples R China

optical solitons play an important role in long-distance, high-capacity communications. To enhance the precision of soliton dynamics modeling, the authors combine incremental learning techniques with physics-informed neural network. The novel model employs a process of knowledge distillation and fine-tuning to continually integrate fresh physical information into the neural network. This iterative approach leads to a constant improvement in the network's ability to extract features. The authors conduct experiments on three solitons, and the new method significantly reduces the error compared to the general physics-informed neural network. The modeling approach put forward in this research is anticipated to contribute to the advancement of all-optical computing research and facilitate the development of novel fiber optic communication systems.

关键词： Deep learning incremental learning knowledge distillation optical soliton physics-informed neural network

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Multi-Physical Intelligent Analysis of the Thermal-Structural-optical Coupling for Mirror Based on Surrogate Model

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IEEE ACCESS 2025年 13卷 14113-14121页

作者： Li, Wenxiong Shen, Junli Lu, Zhenyu Men, Shudong Wu, Qingwen Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Jilin Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China CAS Key Lab Onorbit Mfg & Integrat Space Opt Syst Changchun 130033 Jilin Peoples R China

The thermal-structural-optical multi-physical coupling analysis (TSO-MCA) of infrared optical systems and their mirrors during the establishment of a cryogenic environment is a complex multi-physical coupling problem involving mechanics, thermal, optics and other factors. Currently, the finite element method's workflow is complex and cumbersome, and it cannot simulate the continuous process of establishing a cryogenic environment. Therefore, a surrogate modeling approach based on a neural network for the TSO-MCA of mirrors has been proposed to analyze the surface shape of optical system mirrors under the action of multi-physical fields. Firstly, a thermal-structural-optical multi-physical integrated chain(TSO-IC) is established by integrating various disciplinary simulation and analysis tools, through which computational data necessary for the surrogate model are acquired. Secondly, a hybrid network combining Transformer and Long Short-Term Memory (LSTM) networks is proposed. The high-precision TSO-MCA surrogate model is obtained through training and K-fold cross validation. Compared with traditional methods, the hybrid network has the smallest symmetric mean absolute percentage error (SMAPE) of 2.674% on the same test set, which indicates that the hybrid network obtains a higher prediction accuracy of the surrogate model. Finally, through the thermal structure-optical coupling analysis of a mirror of an optical system, it is shown that the high precision mirror TSO-MCA based on the surrogate model is 3 orders of magnitude faster than the traditional finite element analysis in terms of computation time, indicating that the surrogate model is more efficient and convenient.

关键词： Mirrors Computational modeling neural networks Analytical models Couplings Thermal analysis optical computing Numerical models Adaptive optics Temperature distribution Thermal-structural-optical coupled mirror surrogate model multi-physics neural network

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