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Nonlinear absorption of 2D materials and their application in optical neural networks

JOURNAL OF THE optical SOCIETY OF AMERICA B-optical PHYSICS 2023年第8期40卷 2007-2012页

作者： Teng, Caihong He, Weijie Du, Wen Wu, Jiang Wang, Zhiming Univ Elect Sci & Technol China UESTC Inst Fundamental & Frontier Sci Chengdu 610054 Peoples R China

In recent years, optical neural network (ONN) research has blossomed due to the outstanding advantage of energy consumption and computing property. Regrettably, nonlinear processing in the optical domain remains a huge challenge. The optical characteristics of 2D material, particularly related to saturable absorption (SA), have enabled nonlinear operation. Here, we discuss the SA models with various categories and their application in ONNs. A feedforward artificial neural network was built for handwritten digit recognition to illustrate the feasibil-ity of SA features as nonlinear mapping. For comparison, ONNs without the assistance of the activation function were used as a benchmark to examine the capability of the nonlinear models. A simulation shows that the accuracy of digit classification ranged from 86% to 95%, depending on the nonlinearity of the mediums. This work offers an optical nonlinear unit selection guideline to explore ONNS.& COPY;2023 Optica Publishing Group

关键词： neural networks Nonlinear absorption optical materials optical networks optical neural systems Stochastic gradient descent

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All-optical perception based on partially coherent optical neural networks

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OPTICS EXPRESS 2025年第2期33卷 1609-1624页

作者： Chen, Rui Ma, Yijun Zhang, Chuang Xu, Wenjun Wang, Zhong Sun, Shengli Chinese Acad Sci Shanghai Inst Tech Phys Shanghai 200083 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Chinese Acad Sci Key Lab Intelligent Infrared Percept Shanghai 200083 Peoples R China

In the field of image processing, optical neural networks offer advantages such as high speed, high throughput, and low energy consumption. However, most existing coherent optical neural networks (CONN) rely on coherent light sources to establish transmission models. The use of laser inputs and electro-optic modulation devices at the front end of these neural networks diminishes their computational capability and energy efficiency, thereby limiting their practical applications in object detection tasks. This paper proposes a partially coherent optical neural network (PCONN) transmission model based on mutual intensity modulation. This model does not depend on coherent light source inputs or active electro-optic modulation devices, allowing it to directly compute and infer using natural light after simple filtering, thus achieving full optical perception from light signal acquisition to computation and inference. Simulation results indicate that the model achieves a highest classification accuracy of 96.80% and 86.77% on the MNIST and Fashion-MNIST datasets, respectively. In a binary classification simulation test based on the ISDD segmentation dataset, the model attained an accuracy of 94.69%. It is estimated that this system's computational inference speed for object detection tasks is 100 times faster than that of traditional CONN, with energy efficiency approximately 50 times greater. In summary, our proposed PCONN model addresses the limitations of conventional optical neural networks in coherent light environments and is anticipated to find applications in practical object detection scenarios. (c) 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： High throughput optics Huygens Fresnel principle Information processing optical computing optical neural systems Point spread function

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Multipeak Wavelength Detection of Spectrally Overlapped Fiber Bragg Grating Sensors Through a CNN-Based Autoencoder

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IEEE SENSORS JOURNAL 2024年第13期24卷 20674-20687页

作者： Rudloff, Gabriel Soto, Marcelo A. Univ Tecn Federico Santa Maria Dept Elect Engn Valparaiso 2390123 Chile

This article presents a machine learning solution to identify the peak wavelengths of fiber Bragg grating (FBG) sensors multiplexed in a network with high spectral overlapping. Machine learning solutions generally require high-quality, extensive datasets, which can be difficult to obtain. In contrast, the proposed model corresponds to a convolutional neural network (CNN) autoencoder that can be trained in an unsupervised manner using only FBG spectrum data, without the need for information about the spectral positions of the sensors. The model is specifically designed to encode the spectral positions of overlapping FBG sensors as Dirac deltas, facilitating the straightforward extraction of spectral positions by computing their center of mass. The effectiveness and precision of the model are validated using both simulation and experimental data of a two-sensor serial array. In simulations, the model demonstrates promising adaptation capability, outperforming methods reported in the literature by over an order of magnitude in terms of mean absolute error (MAE). Meanwhile, when evaluated with experimental sensor data, the proposed autoencoder matches the performance of one of the most effective existing methods, but employing a much more efficient computing approach, thereby offering the potential for real-time inference of the spectral position of highly overlapping FBG sensors.

关键词： Sensors Fiber gratings optical fiber sensors Sensor phenomena and characterization Adaptation models Reflectivity Data models Autoencoders convolutional neural networks (CNNs) fiber Bragg gratings (FBGs) optical fiber sensors

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optical-electronic hybrid Fourier convolutional neural network based on super-pixel complex-valued modulation

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APPLIED OPTICS 2023年第5期62卷 1337-1344页

作者： Fan, L., I Long, X. I. L. I. N. Dai, J. U. N. LI, C. H. O. N. G. Dong, X. I. A. O. W. E. N. He, Jian-jun Zhejiang Univ Coll Opt Sci & Engn Ctr Integrated Optoelect State Key Lab Modern Opt Instrumentat Hangzhou 310027 Peoples R China Huawei Technol Co Ltd Bantian Shenzhen 518000 Guangdong Peoples R China

An optical-electronic hybrid convolutional neural network (CNN) system is proposed and investigated for its parallel processing capability and system design robustness. It is regarded as a practical way to implement real-time optical computing. In this paper, we propose a complex-valued modulation method based on an amplitude-only liquid-crystal-on-silicon spatial light modulator and a fixed four-level diffractive optical element. A comparison of computational results of convolutions between different modulation methods in the Fourier plane shows the feasibility of the proposed complex-valued modulation method. A hybrid CNN model with one convolutional layer of multiple channels is proposed and trained electrically for different classification tasks. Our simulation results show that this model has a classification accuracy of 97.55% for MNIST, 88.81% for Fashion MNIST, and 56.16% for Cifar10, which outperforms models using only amplitude or phase modulation and is comparable to the ideal complex-valued modulation method.(c) 2023 Optica Publishing Group

关键词： Diffractive optical elements Modulation techniques optical computing Parallel processing Phase modulation Systems design

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HPPI: A High-Performance Photonic Interconnect Design for Chiplet-Based DNN Accelerators

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2024年第3期43卷 812-825页

作者： Li, Guanglong Ye, Yaoyao Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Dept Micro Nano Elect Shanghai 200240 Peoples R China

In pursuit of higher inference accuracy, the complexity and parameter size of recent deep neural networks (DNNs) have increased significantly. Due to the increasing demand for computing power, the chiplet-based accelerator has been an important computing platform that can handle these DNN models more efficiently. In widely used DNN models, the feature sizes and the number of channels vary greatly among different convolutional layers. Existing chiplet-based accelerators typically adopt consistent optimization strategy for all of the convolutional layers regardless of their sizes, which would limit the inference performance. In this work, we carry out communication-aware customized optimization for convolutional layers with different sizes. First, we propose a reconfigurable high-performance photonic interconnect (HPPI) architecture to facilitate the communication in chiplet-based DNN accelerators. Second, we propose a customized dataflow as the mapping framework and provide four communication patterns of the photonic interconnect with different ways of spatial mapping. Third, we propose a lightweight back propagation neural network to efficiently select the optimal communication pattern for each convolutional layer. The proposed photonic interconnect can be switched between the four communication patterns to enable communication-aware customized optimization for each convolutional layer in the DNN model. As compared to Simba (a representative chiplet-based accelerator with electronic interconnect), HPPI reduces the execution time by 72.23% on average, while saving the energy consumption by 25.49% on average. As compared to ASCEND (a state-of-the-art chiplet-based accelerator with photonic interconnect), HPPI reduces the execution time by 34.04% on average, while saving the energy consumption by 10.34% on average.

关键词： Photonics Artificial neural networks Optimization Integrated circuit interconnections Bandwidth optical filters Unicast Chiplet deep neural network (DNN) accelerator interconnect network mapping

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Application of machine learning for signal recognition in distributed fibre optic acoustic sensing technology

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IET OPTOELECTRONICS 2024年第4期18卷 81-95页

作者： Zhan, Yage Liu, Lirui Li, Kehan Donghua Univ Coll Sci Shanghai Peoples R China

Coherent Rayleigh scattering-based distributed fibre optic sensing technology enables real-time acquisition of vibration and acoustic information along the optical fibres. However, the complexity of monitoring environments often leads to false alarms and missed detections during the process of information source identification with distributed acoustic sensing (DAS). Therefore, it becomes crucial to effectively extract meaningful signal features and perform accurate pattern recognition in the presence of external noise disturbance. The authors provide a comprehensive review of signal feature extraction and pattern recognition techniques applied in DAS technology. After introducing the fundamentals of DAS, specific applications are considered, and the following techniques have been analysed and compared: feature extraction algorithms based on wavelet decomposition, feature extraction schemes utilising other decomposition models, traditional recognition classifiers, and neural network-based recognition classifiers using deep learning. The advantages and limitations of each scheme are discussed, along with their potential applications in various scenarios. The aim is to provide insights into the latest technologies in signal processing and pattern recognition for DAS, fostering further advancements in this field. The authors provide a comprehensive review of signal feature extraction and pattern recognition techniques applied in distributed acoustic sensing (DAS) technology. After introducing the fundamentals of DAS, specific applications are considered, and the following techniques have been analysed and compared: feature extraction algorithms based on wavelet decomposition, feature extraction schemes utilising other decomposition models, traditional recognition classifiers, and neural network-based recognition classifiers using deep learning. image

关键词： distributed sensors object detection optical engineering computing optical fibre communication

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Variational Methods in optical Quantum Machine Learning

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IEEE ACCESS 2023年 11卷 131394-131408页

作者： Simonetti, Marco Perri, Damiano Gervasi, Osvaldo Univ Florence Dept Math & Comp Sci I-50134 Florence Italy Univ Perugia Dept Math & Comp Sci I-06123 Perugia Italy

The computing world is rapidly evolving and advancing, with new ground-breaking technologies emerging. Quantum computing and Quantum Machine Learning have opened up new possibilities, providing unprecedented computational power and problem-solving capabilities while offering a deeper understanding of complex systems. Our research proposes new variational methods based on a deep learning system based on an optical quantum neural network applied to Machine Learning models for point classification. As a case study, we considered the binary classification of points belonging to a certain geometric pattern (the Two-Moons Classification problem) on a plane. We think it is reasonable to expect benefits from using hybrid deep learning systems (classical + quantum), not just in terms of accelerating computation but also in understanding the underlying phenomena and mechanisms. This will result in the development of new machine-learning paradigms and a significant advancement in the field of quantum computation. The selected dataset is a set of 2D points creating two interleaved semicircles and is based on a 2D binary classification generator, which aids in evaluating the performance of particular methods. The two coordinates of each unique point, x(1) and x(2), serve as the features since they present two disparate data sets in a two-dimensional representation space. The goal was to create a quantum deep neural network that could recognise and categorise points accurately with the fewest trainable parameters possible.

关键词： Quantum computing variational methods deep learning quantum feed-forward neural networks optical quantum computing

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Detection and recognition of images under interference conditions

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JOURNAL OF optical TECHNOLOGY 2024年第8期91卷 547-555页

作者： Malakhova, Ekaterina yu. Shelepin, Konstantin yu. Shelepin, Yuriy E. Russian Acad Sci Pavlov Inst Physiol St Petersburg Russia

Subject of study. This study investigates human visual perception under noise conditions and models these mechanisms using generative neural networks. Aim of study. We aimed to understand how humans recognize visual signals under interference conditions and internal noise limitations and to develop effective computing technologies that implement heuristic principles of the visual system. Method. We analyzed psychophysical and neurophysiological data to understand perception efficiency, internal noise, and image reconstruction. We then modeled these perception mechanisms using generative neural networks. Main results. Our study examined visual perception thresholds in the presence of noise and characterized the properties of equivalent internal noise. We found that both internal noise and external uncertainty limit visual perception efficiency. Decreased efficiency serves as an indicator of information-processing disorders in sensory and cognitive pathology. Our work demonstrates that generative neural network models can effectively model key perceptual phenomena in noisy environments. Practical significance. Our computational models advance the understanding of noise-resistant perception mechanisms and facilitate differential diagnosis of visual processing disorders. (c) 2025 Optica Publishing Group

关键词： Image reconstruction Machine vision neural networks Pattern recognition Physiology Visual perception

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Utilization of big data classification models in digitally enhanced optical coherence tomography for medical diagnostics

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neural computing & APPLICATIONS 2024年第1期36卷 225-239页

作者： Bansal, Priti Harjai, Nipun Saif, Mohammad Mugloo, Saahil Hussain Kaur, Preeti Netaji Subhas Univ Technol Dept Informat Technol New Delhi India Netaji Subhas Univ Technol Dept Comp Engn New Delhi India

With the advancement in modern imaging techniques like CT scan, MRI, PET scan etc., a vast amount of data is generated every day in the field of healthcare. Big data contains hidden information, which necessitates the development of intelligent systems to analyze it and extract relevant information, allowing for accurate and cost-effective decisions in the medical field. By utilizing the untapped potential of the big data available in the medical field, very precise models can be developed for the medical diagnosis of retinal diseases. optical coherence tomography (OCT) is a non-invasive imaging test that captures different, distinctive layers of the retina and optic nerve in a living eye to map and measure their thickness, that helps diagnose various retinal disorders. With the advancement of the application of deep learning-based techniques in the field of medical sciences, the use of convolutional neural network (CNN) based approaches for disease detection is gaining popularity. While the manual examination of 3D OCT images for the diagnosis of retinal disorders requires extensive time and expert intervention, the use of CNNs provides an effective automated option that provides results with higher accuracy while also reducing the time involved in the overall process. In this paper, we have implemented the aforementioned idea by proposing OCT-CNN, a CNN architecture, that automatically classifies retinal OCT images and identifies potential disorders in a living eye. Several techniques have been employed to enhance the performance of the proposed approach, including digital enhancement of the images, dropout regularization, adaptive learning rates, and early stopping of training to attain optimal performance. The performance of the proposed OCT-CNN is evaluated on the UCSD dataset against several popular deep CNN architectures and existing state-of-the-art approaches to automatic retinal OCT classification. The proposed OCT-CNN attains the best performance on all e

关键词： Convolutional neural network Digital enhancement Retina Macular degeneration optical coherence tomography OCT-CNN

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STADIA: Photonic Stochastic Gradient Descent for neural network Accelerators

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ACM TRANSACTIONS ON EMBEDDED computing SYSTEMS 2023年第5期22卷 1-23页

作者： Xia, Chengpeng Chen, Yawen Zhang, Haibo Wu, Jigang Univ Otago Otago 9016 New Zealand Guangdong Univ Technol Guangzhou 510006 Guangdong Peoples R China

Deep neural networks (DNNs) have demonstrated great success in many fields such as image recognition and text analysis. However, the ever-increasing sizes of both DNN models and training datasets make deep leaning extremely computation- and memory-intensive. Recently, photonic computing has emerged as a promising technology for accelerating DNNs. While the design of photonic accelerators for DNN inference and forward propagation of DNN training has been widely investigated, the architectural acceleration for equally important backpropagation of DNN training has not been well studied. In this paper, we propose a novel silicon photonic-based backpropagation accelerator for high performance DNN training. Specifically, a general-purpose photonic gradient descent unit named STADIA is designed to implement the multiplication, accumulation, and subtraction operations required for computing gradients using mature optical devices including Mach-Zehnder Interferometer (MZI) and Mircoring Resonator (MRR), which can significantly reduce the training latency and improve the energy efficiency of backpropagation. To demonstrate efficient parallel computing, we propose a STADIA-based backpropagation acceleration architecture and design a dataflow by using wavelength-division multiplexing (WDM). We analyze the precision of STADIA by quantifying the precision limitations imposed by losses and noises. Furthermore, we evaluate STADIA with different element sizes by analyzing the power, area and time delay for photonic accelerators based on DNN models such as AlexNet, VGG19 and ResNet. Simulation results show that the proposed architecture STADIA can achieve significant improvement by 9.7x in time efficiency and 147.2x in energy efficiency, compared with the most advanced optical-memristor based backpropagation accelerator.

关键词： Stochastic gradient descent neural networks accelerator optical computing

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