Статті в журналах: "Photonic Neural Network"

1

Han, Yanan, Shuiying Xiang, Yuna Zhang, Shuang Gao, Aijun Wen, and Yue Hao. "An All-MRR-Based Photonic Spiking Neural Network for Spike Sequence Learning." Photonics 9, no. 2 (February 20, 2022): 120. http://dx.doi.org/10.3390/photonics9020120.

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Photonic spiking neural networks (SNN) have the advantages of high power efficiency, high bandwidth and low delay, but limitations are encountered in large-scale integration. The silicon photonics platform is a promising candidate for realizing large-scale photonic SNN because it is compatible with the current mature CMOS platforms. Here, we present an architecture of photonic SNN which consists of photonic neuron, photonic spike timing dependent plasticity (STDP) and weight configuration that are all based on silicon micro-ring resonators (MRRs), via taking advantage of the nonlinear effects in silicon. The photonic spiking neuron based on the add-drop MRR is proposed, and a system-level computational model of all-MRR-based photonic SNN is presented. The proposed architecture could exploit the properties of small area, high integration and flexible structure of MRR, but also faces challenges caused by the high sensitivity of MRR. The spike sequence learning problem is addressed based on the proposed all-MRR-based photonic SNN architecture via adopting supervised training algorithms. We show the importance of algorithms when hardware devices are limited.

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2

Marquez, Bicky A., Hugh Morison, Zhimu Guo, Matthew Filipovich, Paul R. Prucnal, and Bhavin J. Shastri. "Graphene-based photonic synapse for multi wavelength neural networks." MRS Advances 5, no. 37-38 (2020): 1909–17. http://dx.doi.org/10.1557/adv.2020.327.

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AbstractA synapse is a junction between two biological neurons, and the strength, or weight of the synapse, determines the communication strength between the neurons. Building a neuromorphic (i.e. neuron isomorphic) computing architecture, inspired by a biological network or brain, requires many engineered synapses. Furthermore, recent investigation in neuromorphic photonics, i.e. neuromorphic architectures on photonics platforms, have garnered much interest to enable high-bandwidth, low-latency, low-energy applications of neural networks in machine learning and neuromorphic computing. We propose a graphene-based synapse model as a core element to enable large-scale photonic neural networks based on on-chip multiwavelength techniques. This device consists of an electro-absorption modulator embedded in a microring resonator. We also introduce an encoding protocol that allows for the representation of synaptic weights on our photonic device with 15.7 bits of resolution using current control hardware. Recent work has suggested that graphene-based modulators could operate in excess of 100 GHz. Combined with our work, such a graphene-based synapse could enable applications for ultrafast and online learning.

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3

Wang, Ruiting, Pengfei Wang, Chen Lyu, Guangzhen Luo, Hongyan Yu, Xuliang Zhou, Yejin Zhang, and Jiaoqing Pan. "Multicore Photonic Complex-Valued Neural Network with Transformation Layer." Photonics 9, no. 6 (May 28, 2022): 384. http://dx.doi.org/10.3390/photonics9060384.

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Photonic neural network chips have been widely studied because of their low power consumption, high speed and large bandwidth. Using amplitude and phase to encode, photonic chips can accelerate complex-valued neural network computations. In this article, a photonic complex-valued neural network (PCNN) chip is designed. The scale of the single-core PCNN chip is limited because of optical losses, and the multicore architecture of the chip is used to improve computing capability. Further, for improving the performance of the PCNN, we propose the transformation layer, which can be implemented by the designed photonic chip to transform real-valued encoding to complex-valued encoding, which has richer information. Compared with real-valued input, the transformation layer can effectively improve the classification accuracy from 93.14% to 97.51% of a 64-dimensional input on the MNIST test set. Finally, we analyze the multicore computation of the PCNN. Compared with the single-core architecture, the multicore architecture can improve the classification accuracy by implementing larger neural networks and has better phase noise robustness. The proposed architecture and algorithms are beneficial to promote the accelerated computing of photonic chips for complex-valued neural networks and are promising for use in many applications, such as image recognition and signal processing.

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Ferreira de Lima, Thomas, Bhavin J. Shastri, Alexander N. Tait, Mitchell A. Nahmias, and Paul R. Prucnal. "Progress in neuromorphic photonics." Nanophotonics 6, no. 3 (March 11, 2017): 577–99. http://dx.doi.org/10.1515/nanoph-2016-0139.

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AbstractAs society’s appetite for information continues to grow, so does our need to process this information with increasing speed and versatility. Many believe that the one-size-fits-all solution of digital electronics is becoming a limiting factor in certain areas such as data links, cognitive radio, and ultrafast control. Analog photonic devices have found relatively simple signal processing niches where electronics can no longer provide sufficient speed and reconfigurability. Recently, the landscape for commercially manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. By bridging the mathematical prowess of artificial neural networks to the underlying physics of optoelectronic devices, neuromorphic photonics could breach new domains of information processing demanding significant complexity, low cost, and unmatched speed. In this article, we review the progress in neuromorphic photonics, focusing on photonic integrated devices. The challenges and design rules for optoelectronic instantiation of artificial neurons are presented. The proposed photonic architecture revolves around the processing network node composed of two parts: a nonlinear element and a network interface. We then survey excitable lasers in the recent literature as candidates for the nonlinear node and microring-resonator weight banks as the network interface. Finally, we compare metrics between neuromorphic electronics and neuromorphic photonics and discuss potential applications.

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5

Pai, Sunil, Zhanghao Sun, Tyler W. Hughes, Taewon Park, Ben Bartlett, Ian A. D. Williamson, Momchil Minkov, et al. "Experimentally realized in situ backpropagation for deep learning in photonic neural networks." Science 380, no. 6643 (April 28, 2023): 398–404. http://dx.doi.org/10.1126/science.ade8450.

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Integrated photonic neural networks provide a promising platform for energy-efficient, high-throughput machine learning with extensive scientific and commercial applications. Photonic neural networks efficiently transform optically encoded inputs using Mach-Zehnder interferometer mesh networks interleaved with nonlinearities. We experimentally trained a three-layer, four-port silicon photonic neural network with programmable phase shifters and optical power monitoring to solve classification tasks using “in situ backpropagation,” a photonic analog of the most popular method to train conventional neural networks. We measured backpropagated gradients for phase-shifter voltages by interfering forward- and backward-propagating light and simulated in situ backpropagation for 64-port photonic neural networks trained on MNIST image recognition given errors. All experiments performed comparably to digital simulations ( > 94% test accuracy), and energy scaling analysis indicated a route to scalable machine learning.

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6

Fu, Chentao, Shuiying Xiang, Yanan Han, Ziwei Song, and Yue Hao. "Multilayer Photonic Spiking Neural Networks: Generalized Supervised Learning Algorithm and Network Optimization." Photonics 9, no. 4 (March 25, 2022): 217. http://dx.doi.org/10.3390/photonics9040217.

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We propose a generalized supervised learning algorithm for multilayer photonic spiking neural networks (SNNs) by combining the spike-timing dependent plasticity (STDP) rule and the gradient descent mechanism. A vertical-cavity surface-emitting laser with an embedded saturable absorber (VCSEL-SA) is employed as a photonic leaky-integrate-and-fire (LIF) neuron. The temporal coding strategy is employed to transform information into the precise firing time. With the modified supervised learning algorithm, the trained multilayer photonic SNN successfully solves the XOR problem and performs well on the Iris and Wisconsin breast cancer datasets. This indicates that a generalized supervised learning algorithm is realized for multilayer photonic SNN. In addition, network optimization is performed by considering different network sizes.

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7

Xia, Chengpeng, Yawen Chen, Haibo Zhang, Hao Zhang, Fei Dai, and Jigang Wu. "Efficient neural network accelerators with optical computing and communication." Computer Science and Information Systems, no. 00 (2022): 66. http://dx.doi.org/10.2298/csis220131066x.

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Conventional electronic Artificial Neural Networks (ANNs) accelerators focus on architecture design and numerical computation optimization to improve the training efficiency. However, these approaches have recently encountered bottlenecks in terms of energy efficiency and computing performance, which leads to an increase interest in photonic accelerator. Photonic architectures with low energy consumption, high transmission speed and high bandwidth have been considered as an important role for generation of computing architectures. In this paper, to provide a better understanding of optical technology used in ANN acceleration, we present a comprehensive review for the efficient photonic computing and communication in ANN accelerators. The related photonic devices are investigated in terms of the application in ANNs acceleration, and a classification of existing solutions is proposed that are categorized into optical computing acceleration and optical communication acceleration according to photonic effects and photonic architectures. Moreover, we discuss the challenges for these photonic neural network acceleration approaches to highlight the most promising future research opportunities in this field.

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8

Christensen, Thomas, Charlotte Loh, Stjepan Picek, Domagoj Jakobović, Li Jing, Sophie Fisher, Vladimir Ceperic, John D. Joannopoulos, and Marin Soljačić. "Predictive and generative machine learning models for photonic crystals." Nanophotonics 9, no. 13 (June 29, 2020): 4183–92. http://dx.doi.org/10.1515/nanoph-2020-0197.

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AbstractThe prediction and design of photonic features have traditionally been guided by theory-driven computational methods, spanning a wide range of direct solvers and optimization techniques. Motivated by enormous advances in the field of machine learning, there has recently been a growing interest in developing complementary data-driven methods for photonics. Here, we demonstrate several predictive and generative data-driven approaches for the characterization and inverse design of photonic crystals. Concretely, we built a data set of 20,000 two-dimensional photonic crystal unit cells and their associated band structures, enabling the training of supervised learning models. Using these data set, we demonstrate a high-accuracy convolutional neural network for band structure prediction, with orders-of-magnitude speedup compared to conventional theory-driven solvers. Separately, we demonstrate an approach to high-throughput inverse design of photonic crystals via generative adversarial networks, with the design goal of substantial transverse-magnetic band gaps. Our work highlights photonic crystals as a natural application domain and test bed for the development of data-driven tools in photonics and the natural sciences.

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9

Zhang, Lulu, Yongzhi Zhang, Furong Liu, Qingyuan Chen, Yangbo Lian, and Quanlong Ma. "On-Chip Photonic Synapses with All-Optical Memory and Neural Network Computation." Micromachines 14, no. 1 (December 27, 2022): 74. http://dx.doi.org/10.3390/mi14010074.

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Inspired by the human brain, neural network computing was expected to break the bottleneck of traditional computing, but the integrated design still faces great challenges. Here, a readily integrated membrane-system photonic synapse was demonstrated. By pre-pulse training at 1064 nm (cutoff wavelength), the photonic synapse can be regulated both excitatory and inhibitory at tunable wavelengths (1200–2000 nm). Furthermore, more weights and memory functions were shown through the photonic synapse integrated network. Additionally, the digital recognition function of the single-layer perceptron neural network constructed by photonic synapses has been successfully demonstrated. Most of the biological synaptic functions were realized by the photonic synaptic network, and it had the advantages of compact structure, scalable, adjustable wavelength, and so on, which opens up a new idea for the study of the neural synaptic network.

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10

Quan, Zhiqiang, Yuanjian Wan, and Jian Wang. "On-chip ultra-compact nonvolatile photonic synapse." Applied Physics Letters 121, no. 17 (October 24, 2022): 171102. http://dx.doi.org/10.1063/5.0115564.

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The important research content of modern communication systems is to realize high-speed, stable, and intelligent information transmission and processing. All-optical neural networks based on the silicon integrated technology and phase change materials (PCMs) can realize picosecond-level modulation speed, faster processing speed, and lower energy consumption compared with the traditional electrical communication system. The photonic synapse is the core component of the all-optical neural network module, but the existing photonic synapses based on PCMs require a modulation distance (MD) of several micrometers or even ten micrometers to achieve a large output intensity range. In this paper, we propose an ultra-compact nonvolatile photonic synapse, in which MD can be shortened to 1 μm, breaking the record of the minimum signal MD of the silicon photonic synapse using the PCMs. At the same time, the output intensity range of our synapse is almost twice that of the existing research. Based on this photonic synapse, we analyze the relationship between the output response and incident wavelength, which can help to design an ultra-compact photonic convolutional neural network. This work has great potential in future photonic computing and photonic communication technologies.

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11

Zhang, Qi, Zhuangzhuang Xing, and Duan Huang. "Implementation of Pruned Backpropagation Neural Network Based on Photonic Integrated Circuits." Photonics 8, no. 9 (August 30, 2021): 363. http://dx.doi.org/10.3390/photonics8090363.

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We demonstrate a pruned high-speed and energy-efficient optical backpropagation (BP) neural network. The micro-ring resonator (MRR) banks, as the core of the weight matrix operation, are used for large-scale weighted summation. We find that tuning a pruned MRR weight banks model gives an equivalent performance in training with the model of random initialization. Results show that the overall accuracy of the optical neural network on the MNIST dataset is 93.49% after pruning six-layer MRR weight banks on the condition of low insertion loss. This work is scalable to much more complex networks, such as convolutional neural networks and recurrent neural networks, and provides a potential guide for truly large-scale optical neural networks.

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12

Jiang, Jiaqi, and Jonathan A. Fan. "Multiobjective and categorical global optimization of photonic structures based on ResNet generative neural networks." Nanophotonics 10, no. 1 (September 22, 2020): 361–69. http://dx.doi.org/10.1515/nanoph-2020-0407.

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AbstractWe show that deep generative neural networks, based on global optimization networks (GLOnets), can be configured to perform the multiobjective and categorical global optimization of photonic devices. A residual network scheme enables GLOnets to evolve from a deep architecture, which is required to properly search the full design space early in the optimization process, to a shallow network that generates a narrow distribution of globally optimal devices. As a proof-of-concept demonstration, we adapt our method to design thin-film stacks consisting of multiple material types. Benchmarks with known globally optimized antireflection structures indicate that GLOnets can find the global optimum with orders of magnitude faster speeds compared to conventional algorithms. We also demonstrate the utility of our method in complex design tasks with its application to incandescent light filters. These results indicate that advanced concepts in deep learning can push the capabilities of inverse design algorithms for photonics.

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13

Ferreira de Lima, Thomas, Alexander N. Tait, Armin Mehrabian, Mitchell A. Nahmias, Chaoran Huang, Hsuan-Tung Peng, Bicky A. Marquez, et al. "Primer on silicon neuromorphic photonic processors: architecture and compiler." Nanophotonics 9, no. 13 (August 10, 2020): 4055–73. http://dx.doi.org/10.1515/nanoph-2020-0172.

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AbstractMicroelectronic computers have encountered challenges in meeting all of today’s demands for information processing. Meeting these demands will require the development of unconventional computers employing alternative processing models and new device physics. Neural network models have come to dominate modern machine learning algorithms, and specialized electronic hardware has been developed to implement them more efficiently. A silicon photonic integration industry promises to bring manufacturing ecosystems normally reserved for microelectronics to photonics. Photonic devices have already found simple analog signal processing niches where electronics cannot provide sufficient bandwidth and reconfigurability. In order to solve more complex information processing problems, they will have to adopt a processing model that generalizes and scales. Neuromorphic photonics aims to map physical models of optoelectronic systems to abstract models of neural networks. It represents a new opportunity for machine information processing on sub-nanosecond timescales, with application to mathematical programming, intelligent radio frequency signal processing, and real-time control. The strategy of neuromorphic engineering is to externalize the risk of developing computational theory alongside hardware. The strategy of remaining compatible with silicon photonics externalizes the risk of platform development. In this perspective article, we provide a rationale for a neuromorphic photonics processor, envisioning its architecture and a compiler. We also discuss how it can be interfaced with a general purpose computer, i.e. a CPU, as a coprocessor to target specific applications. This paper is intended for a wide audience and provides a roadmap for expanding research in the direction of transforming neuromorphic photonics into a viable and useful candidate for accelerating neuromorphic computing.

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14

Miscuglio, Mario, Jiawei Meng, Armin Mehrabian, Volker Sorger, Omer Yesiliurt, Ludmila Prokopeva, Alexander Kildishev, Yifei Zhang, and Juejun Hu. "Artificial Synapse with Mnemonic Functionality using GSST-based Photonic Integrated Memory." Applied Computational Electromagnetics Society 35, no. 11 (February 5, 2021): 1447–49. http://dx.doi.org/10.47037/2020.aces.j.351192.

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Here we present a multi-level discrete-state nonvolatile photonic memory based on an ultra-compact (<4μm) hybrid phase change material GSST-silicon Mach Zehnder modulator, with low insertion losses (3dB), to serve as node in a photonic neural network. Emulating an opportunely trained 100 × 100 fully connected multilayered perceptron neural network with this weighting functionality embedded as photonic memory, shows up to 92% inference accuracy and robustness towards noise when performing predictions of unseen data.

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15

Tian, Ye, Yang Zhao, Shengping Liu, Qiang Li, Wei Wang, Junbo Feng, and Jin Guo. "Scalable and compact photonic neural chip with low learning-capability-loss." Nanophotonics 11, no. 2 (December 22, 2021): 329–44. http://dx.doi.org/10.1515/nanoph-2021-0521.

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Abstract Photonic computation has garnered huge attention due to its great potential to accelerate artificial neural network tasks at much higher clock rate to digital electronic alternatives. Especially, reconfigurable photonic processor consisting of Mach–Zehnder interferometer (MZI) mesh is promising for photonic matrix multiplier. It is desired to implement high-radix MZI mesh to boost the computation capability. Conventionally, three cascaded MZI meshes (two universal N × N unitary MZI mesh and one diagonal MZI mesh) are needed to express N × N weight matrix with O(N 2) MZIs requirements, which limits scalability seriously. Here, we propose a photonic matrix architecture using the real-part of one nonuniversal N × N unitary MZI mesh to represent the real-value matrix. In the applications like photonic neural network, it probable reduces the required MZIs to O(Nlog2 N) level while pay low cost on learning capability loss. Experimentally, we implement a 4 × 4 photonic neural chip and benchmark its performance in convolutional neural network for handwriting recognition task. Low learning-capability-loss is observed in our 4 × 4 chip compared to its counterpart based on conventional architecture using O(N 2) MZIs. While regarding the optical loss, chip size, power consumption, encoding error, our architecture exhibits all-round superiority.

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16

Aloafi, Tahani A., Azhari A. Elhag, Taghreed M. Jawa, Neveen Sayed-Ahmed, Fatimah S. Bayones, Jamel Bouslimi, and Marin Marin. "Predication and Photon Statistics of a Three-Level System in the Photon Added Negative Binomial Distribution." Symmetry 14, no. 2 (January 31, 2022): 284. http://dx.doi.org/10.3390/sym14020284.

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Statistical and artificial neural network models are applied to forecast the quantum scheme of a three-level atomic system (3LAS) and field, initially following a photon added negative binomial distribution (PANBD). The Mandel parameter is used to detect the photon statistics of a radiation field. Explicit forms of the PANBD are given. The prediction of the Mandel parameter, atomic probability of the 3LAS in the upper state, and von Neumann entropy are obtained using time series and artificial neural network methods. The influence of probability success photons and the number of added photons to the NBD are examined. The total density matrix is used to compute and analyze the time evolution of the initial photonic negative binomial probability distribution that governs the 3LAS–field photon entanglement behavior. It is shown that the statistical quantities are strongly affected by probability success photons and the number of added photons to the NBD. Also, the prediction of quantum entropy is achieved by the time series and neural network.

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17

Wang, Yan, Wei Cheng, Junbo Feng, Shengyin Zang, Hao Cheng, Zheng Peng, Xiaodong Ren, et al. "Silicon photonic secure communication using artificial neural network." Chaos, Solitons & Fractals 163 (October 2022): 112524. http://dx.doi.org/10.1016/j.chaos.2022.112524.

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18

Zarei, Sanaz, Mahmood-reza Marzban, and Amin Khavasi. "Integrated photonic neural network based on silicon metalines." Optics Express 28, no. 24 (November 18, 2020): 36668. http://dx.doi.org/10.1364/oe.404386.

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Sunny, Febin P., Asif Mirza, Mahdi Nikdast, and Sudeep Pasricha. "ROBIN: A Robust Optical Binary Neural Network Accelerator." ACM Transactions on Embedded Computing Systems 20, no. 5s (October 31, 2021): 1–24. http://dx.doi.org/10.1145/3476988.

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Domain specific neural network accelerators have garnered attention because of their improved energy efficiency and inference performance compared to CPUs and GPUs. Such accelerators are thus well suited for resource-constrained embedded systems. However, mapping sophisticated neural network models on these accelerators still entails significant energy and memory consumption, along with high inference time overhead. Binarized neural networks (BNNs), which utilize single-bit weights, represent an efficient way to implement and deploy neural network models on accelerators. In this paper, we present a novel optical-domain BNN accelerator, named ROBIN , which intelligently integrates heterogeneous microring resonator optical devices with complementary capabilities to efficiently implement the key functionalities in BNNs. We perform detailed fabrication-process variation analyses at the optical device level, explore efficient corrective tuning for these devices, and integrate circuit-level optimization to counter thermal variations. As a result, our proposed ROBIN architecture possesses the desirable traits of being robust, energy-efficient, low latency, and high throughput, when executing BNN models. Our analysis shows that ROBIN can outperform the best-known optical BNN accelerators and many electronic accelerators. Specifically, our energy-efficient ROBIN design exhibits energy-per-bit values that are ∼4 × lower than electronic BNN accelerators and ∼933 × lower than a recently proposed photonic BNN accelerator, while a performance-efficient ROBIN design shows ∼3 × and ∼25 × better performance than electronic and photonic BNN accelerators, respectively.

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Argyris, Apostolos. "Photonic neuromorphic technologies in optical communications." Nanophotonics 11, no. 5 (January 19, 2022): 897–916. http://dx.doi.org/10.1515/nanoph-2021-0578.

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Abstract Machine learning (ML) and neuromorphic computing have been enforcing problem-solving in many applications. Such approaches found fertile ground in optical communications, a technological field that is very demanding in terms of computational speed and complexity. The latest breakthroughs are strongly supported by advanced signal processing, implemented in the digital domain. Algorithms of different levels of complexity aim at improving data recovery, expanding the reach of transmission, validating the integrity of the optical network operation, and monitoring data transfer faults. Lately, the concept of reservoir computing (RC) inspired hardware implementations in photonics that may offer revolutionary solutions in this field. In a brief introduction, I discuss some of the established digital signal processing (DSP) techniques and some new approaches based on ML and neural network (NN) architectures. In the main part, I review the latest neuromorphic computing proposals that specifically apply to photonic hardware and give new perspectives on addressing signal processing in optical communications. I discuss the fundamental topologies in photonic feed-forward and recurrent network implementations. Finally, I review the photonic topologies that were initially tested for channel equalization benchmark tasks, and then in fiber transmission systems, for optical header recognition, data recovery, and modulation format identification.

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21

Sheng, Huayi. "Review of Integrated Diffractive Deep Neural Networks." Highlights in Science, Engineering and Technology 24 (December 27, 2022): 264–78. http://dx.doi.org/10.54097/hset.v24i.3957.

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An integrated photonic diffractive deep neural network ( ID^2 NN) is one of the most exciting cross-discipline fields of artificial intelligence and optical computing, combining deep learning with the power of light-speed processing on an integrated platform. We know that neural network in a digital computer is based on transistors, which have significant challenges in keeping pace with Moore's law and limited real-time processing applications due to the increased computational costs associated with them. However, with remarkable progress and advancement in silicon photonic integrated circuits over the last few decades, ID^2 NN hold the promise of on-chip miniaturisation and high-speed performance with low power consumption. This paper covers the essential theoretical background for constructing the ID^2 NN and reviews the research status of optical diffractive neural networks in the field of neuromorphic computing. Problems of narrowing down current ID^2 NN applications are also included in this review. Finally, future research directions for ID^2 NN are discussed, and conclusions are delivered.

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Yang Lingyan, 杨凌雁, та 张林 Zhang Lin. "光蓄水池神经网络研究进展". Chinese Journal of Lasers 48, № 19 (2021): 1906001. http://dx.doi.org/10.3788/cjl202148.1906001.

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De Marinis, Lorenzo, Marco Cococcioni, Odile Liboiron-Ladouceur, Giampiero Contestabile, Piero Castoldi, and Nicola Andriolli. "Photonic Integrated Reconfigurable Linear Processors as Neural Network Accelerators." Applied Sciences 11, no. 13 (July 5, 2021): 6232. http://dx.doi.org/10.3390/app11136232.

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Reconfigurable linear optical processors can be used to perform linear transformations and are instrumental in effectively computing matrix–vector multiplications required in each neural network layer. In this paper, we characterize and compare two thermally tuned photonic integrated processors realized in silicon-on-insulator and silicon nitride platforms suited for extracting feature maps in convolutional neural networks. The reduction in bit resolution when crossing the processor is mainly due to optical losses, in the range 2.3–3.3 for the silicon-on-insulator chip and in the range 1.3–2.4 for the silicon nitride chip. However, the lower extinction ratio of Mach–Zehnder elements in the latter platform limits their expressivity (i.e., the capacity to implement any transformation) to 75%, compared to 97% of the former. Finally, the silicon-on-insulator processor outperforms the silicon nitride one in terms of footprint and energy efficiency.

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Roques-Carmes, Charles. "Learning photons go backward." Science 380, no. 6643 (April 28, 2023): 341–42. http://dx.doi.org/10.1126/science.adh0724.

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Sun, Yichen, Mingli Dong, Mingxin Yu, Jiabin Xia, Xu Zhang, Yuchen Bai, Lidan Lu та Lianqing Zhu. "Nonlinear All-Optical Diffractive Deep Neural Network with 10.6 μm Wavelength for Image Classification". International Journal of Optics 2021 (27 лютого 2021): 1–16. http://dx.doi.org/10.1155/2021/6667495.

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A photonic artificial intelligence chip is based on an optical neural network (ONN), low power consumption, low delay, and strong antiinterference ability. The all-optical diffractive deep neural network has recently demonstrated its inference capabilities on the image classification task. However, the size of the physical model does not have miniaturization and integration, and the optical nonlinearity is not incorporated into the diffraction neural network. By introducing the nonlinear characteristics of the network, complex tasks can be completed with high accuracy. In this study, a nonlinear all-optical diffraction deep neural network (N-D2NN) model based on 10.6 μm wavelength is constructed by combining the ONN and complex-valued neural networks with the nonlinear activation function introduced into the structure. To be specific, the improved activation function of the rectified linear unit (ReLU), i.e., Leaky-ReLU, parametric ReLU (PReLU), and randomized ReLU (RReLU), is selected as the activation function of the N-D2NN model. Through numerical simulation, it is proved that the N-D2NN model based on 10.6 μm wavelength has excellent representation ability, which enables them to perform classification learning tasks of the MNIST handwritten digital dataset and Fashion-MNIST dataset well, respectively. The results show that the N-D2NN model with the RReLU activation function has the highest classification accuracy of 97.86% and 89.28%, respectively. These results provide a theoretical basis for the preparation of miniaturized and integrated N-D2NN model photonic artificial intelligence chips.

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Gao, Shuang, Shuiying Xiang, Ziwei Song, Yanan Han, and Yue Hao. "All-optical Sudoku solver with photonic spiking neural network." Optics Communications 495 (September 2021): 127068. http://dx.doi.org/10.1016/j.optcom.2021.127068.

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27

El-Mosalmy, Dalia D., M. F. O. Hameed, Nihal F. F. Areed, and S. S. A. Obayya. "Novel neural network based optimization approach for photonic devices." Optical and Quantum Electronics 46, no. 3 (January 16, 2014): 439–53. http://dx.doi.org/10.1007/s11082-013-9869-8.

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28

Hsiao, Fu-Li, Hsin-Feng Lee, Su-Chao Wang, Yu-Ming Weng, and Ying-Pin Tsai. "Artificial Neural Network for Photonic Crystal Band Structure Prediction in Different Geometric Parameters and Refractive Indexes." Electronics 12, no. 8 (April 9, 2023): 1777. http://dx.doi.org/10.3390/electronics12081777.

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In this study, an artificial neural network that can predict the band structure of 2-D photonic crystals is developed. Three kinds of photonic crystals in a square lattice, triangular lattice, and honeycomb lattice and two kinds of materials with different refractive indices are investigated. Using the length of the wave vectors in the reduced Brillouin zone, band number, r/a ratio, and the refractive indices as the dataset, the desired ANN is trained to predict the eigenfrequencies of the photonic modes and depict the photonic band structures with a correlation coefficient greater than 0.99. By increasing the number of neurons in the hidden layer, the correlation coefficient can be further increased over 0.999.

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29

Dang, Dharanidhar, Sai Vineel Reddy Chittamuru, Sudeep Pasricha, Rabi Mahapatra, and Debashis Sahoo. "BPLight-CNN: A Photonics-Based Backpropagation Accelerator for Deep Learning." ACM Journal on Emerging Technologies in Computing Systems 17, no. 4 (October 31, 2021): 1–26. http://dx.doi.org/10.1145/3446212.

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Training deep learning networks involves continuous weight updates across the various layers of the deep network while using a backpropagation (BP) algorithm. This results in expensive computation overheads during training. Consequently, most deep learning accelerators today employ pretrained weights and focus only on improving the design of the inference phase. The recent trend is to build a complete deep learning accelerator by incorporating the training module. Such efforts require an ultra-fast chip architecture for executing the BP algorithm. In this article, we propose a novel photonics-based backpropagation accelerator for high-performance deep learning training. We present the design for a convolutional neural network (CNN), BPLight-CNN , which incorporates the silicon photonics-based backpropagation accelerator. BPLight-CNN is a first-of-its-kind photonic and memristor-based CNN architecture for end-to-end training and prediction. We evaluate BPLight-CNN using a photonic CAD framework (IPKISS) on deep learning benchmark models, including LeNet and VGG-Net. The proposed design achieves (i) at least 34× speedup, 34× improvement in computational efficiency, and 38.5× energy savings during training; and (ii) 29× speedup, 31× improvement in computational efficiency, and 38.7× improvement in energy savings during inference compared with the state-of-the-art designs. All of these comparisons are done at a 16-bit resolution, and BPLight-CNN achieves these improvements at a cost of approximately 6% lower accuracy compared with the state-of-the-art.

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30

Jiang, Yue, Wenjia Zhang, Fan Yang, and Zuyuan He. "Photonic Convolution Neural Network Based on Interleaved Time-Wavelength Modulation." Journal of Lightwave Technology 39, no. 14 (July 2021): 4592–600. http://dx.doi.org/10.1109/jlt.2021.3076070.

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31

Huang, Chaoran, Shinsuke Fujisawa, Thomas Ferreira de Lima, Alexander N. Tait, Eric C. Blow, Yue Tian, Simon Bilodeau, et al. "A silicon photonic–electronic neural network for fibre nonlinearity compensation." Nature Electronics 4, no. 11 (November 2021): 837–44. http://dx.doi.org/10.1038/s41928-021-00661-2.

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32

Zhang, Tian, Jia Wang, Yihang Dan, Yuxiang Lanqiu, Jian Dai, Xu Han, Xiaojuan Sun, and Kun Xu. "Efficient training and design of photonic neural network through neuroevolution." Optics Express 27, no. 26 (December 9, 2019): 37150. http://dx.doi.org/10.1364/oe.27.037150.

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33

Wang, Suhong, Shuiying Xiang, Genquan Han, Ziwei Song, Zhenxing Ren, Aijun Wen, and Yue Hao. "Photonic Associative Learning Neural Network Based on VCSELs and STDP." Journal of Lightwave Technology 38, no. 17 (September 1, 2020): 4691–98. http://dx.doi.org/10.1109/jlt.2020.2995083.

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34

Bueno, J., S. Maktoobi, L. Froehly, I. Fischer, M. Jacquot, L. Larger, and D. Brunner. "Reinforcement learning in a large-scale photonic recurrent neural network." Optica 5, no. 6 (June 20, 2018): 756. http://dx.doi.org/10.1364/optica.5.000756.

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35

Skontranis, Menelaos, George Sarantoglou, Stavros Deligiannidis, Adonis Bogris, and Charis Mesaritakis. "Time-Multiplexed Spiking Convolutional Neural Network Based on VCSELs for Unsupervised Image Classification." Applied Sciences 11, no. 4 (February 3, 2021): 1383. http://dx.doi.org/10.3390/app11041383.

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In this work, we present numerical results concerning a multilayer “deep” photonic spiking convolutional neural network, arranged so as to tackle a 2D image classification task. The spiking neurons used are typical two-section quantum-well vertical-cavity surface-emitting lasers that exhibit isomorphic behavior to biological neurons, such as integrate-and-fire excitability and timing encoding. The isomorphism of the proposed scheme to biological networks is extended by replicating the retina ganglion cell for contrast detection in the photonic domain and by utilizing unsupervised spike dependent plasticity as the main training technique. Finally, in this work we also investigate the possibility of exploiting the fast carrier dynamics of lasers so as to time-multiplex spatial information and reduce the number of physical neurons used in the convolutional layers by orders of magnitude. This last feature unlocks new possibilities, where neuron count and processing speed can be interchanged so as to meet the constraints of different applications.

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36

Xu, Xiaofeng, Lianqing Zhu, Wei Zhuang, Lidan Lu, and Pei Yuan. "A Convolution Neural Network Implemented by Three 3 × 3 Photonic Integrated Reconfigurable Linear Processors." Photonics 9, no. 2 (January 29, 2022): 80. http://dx.doi.org/10.3390/photonics9020080.

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The convolution neural network (CNN) is a classical neural network with advantages in image processing. The use of multiport optical interferometric linear structures in neural networks has recently attracted a great deal of attention. Here, we use three 3 × 3 reconfigurable optical processors, based on Mach-Zehnder interferometers (MZIs), to implement a two-layer CNN. To circumvent the random phase errors originating from the fabrication process, MZIs are calibrated before the classification experiment. The MNIST datasets and Fashion-MNIST datasets are used to verify the classification accuracy. The optical processor achieves 86.9% accuracy on the MNIST datasets and 79.3% accuracy on the Fashion-MNIST datasets. Experiments show that we can improve the classification accuracy by reducing phase errors of MZIs and photodetector (PD) noises. In the future, our work provides a way to embed the optical processor in CNN to compute matrix multiplication.

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37

Li, Renjie, Xiaozhe Gu, Yuanwen Shen, Ke Li, Zhen Li, and Zhaoyu Zhang. "Smart and Rapid Design of Nanophotonic Structures by an Adaptive and Regularized Deep Neural Network." Nanomaterials 12, no. 8 (April 16, 2022): 1372. http://dx.doi.org/10.3390/nano12081372.

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The design of nanophotonic structures based on deep learning is emerging rapidly in the research community. Design methods using Deep Neural Networks (DNN) are outperforming conventional physics-based simulations performed iteratively by human experts. Here, a self-adaptive and regularized DNN based on Convolutional Neural Networks (CNNs) for the smart and fast characterization of nanophotonic structures in high-dimensional design parameter space is presented. This proposed CNN model, named LRS-RCNN, utilizes dynamic learning rate scheduling and L2 regularization techniques to overcome overfitting and speed up training convergence and is shown to surpass the performance of all previous algorithms, with the exception of two metrics where it achieves a comparable level relative to prior works. We applied the model to two challenging types of photonic structures: 2D photonic crystals (e.g., L3 nanocavity) and 1D photonic crystals (e.g., nanobeam) and results show that LRS-RCNN achieves record-high prediction accuracies, strong generalizibility, and substantially faster convergence speed compared to prior works. Although still a proof-of-concept model, the proposed smart LRS-RCNN has been proven to greatly accelerate the design of photonic crystal structures as a state-of-the-art predictor for both Q-factor and V. It can also be modified and generalized to predict any type of optical properties for designing a wide range of different nanophotonic structures. The complete dataset and code will be released to aid the development of related research endeavors.

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38

Van der Sande, Guy, Daniel Brunner, and Miguel C. Soriano. "Advances in photonic reservoir computing." Nanophotonics 6, no. 3 (May 12, 2017): 561–76. http://dx.doi.org/10.1515/nanoph-2016-0132.

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AbstractWe review a novel paradigm that has emerged in analogue neuromorphic optical computing. The goal is to implement a reservoir computer in optics, where information is encoded in the intensity and phase of the optical field. Reservoir computing is a bio-inspired approach especially suited for processing time-dependent information. The reservoir’s complex and high-dimensional transient response to the input signal is capable of universal computation. The reservoir does not need to be trained, which makes it very well suited for optics. As such, much of the promise of photonic reservoirs lies in their minimal hardware requirements, a tremendous advantage over other hardware-intensive neural network models. We review the two main approaches to optical reservoir computing: networks implemented with multiple discrete optical nodes and the continuous system of a single nonlinear device coupled to delayed feedback.

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39

Fornarelli, G., L. Mescia, F. Prudenzano, M. De Sario, and F. Vacca. "A neural network model of erbium-doped photonic crystal fibre amplifiers." Optics & Laser Technology 41, no. 5 (July 2009): 580–85. http://dx.doi.org/10.1016/j.optlastec.2008.10.010.

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40

Chang, Wei-Shan, Ying-Pin Tsai, Chi-Tsung Chiang, and Fu-Li Hsiao. "Using Autoencoder Artificial Neural Network to Predict Photonic Crystal Band Structure." Sensors and Materials 35, no. 8 (August 31, 2023): 3045. http://dx.doi.org/10.18494/sam4514.

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41

Guo Pengxing, 郭鹏星, 刘志远 Liu Zhiyuan, 侯维刚 Hou Weigang та 郭磊 Guo Lei. "相变材料辅助的光子卷积神经网络加速器". Acta Optica Sinica 43, № 4 (2023): 0415001. http://dx.doi.org/10.3788/aos221329.

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42

Chen, Ying, Teng Liu, Wenyue Wang, Qiguang Zhu, and Weihong Bi. "Refractive index sensing performance analysis of photonic crystal Mach–Zehnder interferometer based on BP neural network optimization." Modern Physics Letters B 29, no. 10 (April 20, 2015): 1550040. http://dx.doi.org/10.1142/s0217984915500402.

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According to the band gap and photon localization characteristics, the single-arm notching and the double-arm notching Mach–Zehnder interferometer (MZI) structures based on 2D triangular lattice air hole-typed photonic crystal waveguide are proposed. The back-propagation (BP) neural network is introduced to optimize the structural parameters of the photonic crystal MZI structure, which results in the normalized transmission peak increasing from 85.3% to 97.1%. The sensitivity performances of the two structures are compared and analyzed using the Salmonella solution samples with different concentrations in the numerical simulation. The results show that the sensitivity of the double-arm notching structure is 4583 nm/RIU, which is about 6.4 times of the single-arm notching structure, which can provide some references for the optimization of the photonic devices and the design of high-sensitivity biosensors.

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43

Bile, Alessandro, Hamed Tari, and Eugenio Fazio. "Episodic Memory and Information Recognition Using Solitonic Neural Networks Based on Photorefractive Plasticity." Applied Sciences 12, no. 11 (May 31, 2022): 5585. http://dx.doi.org/10.3390/app12115585.

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Neuromorphic models are proving capable of performing complex machine learning tasks, overcoming the structural limitations imposed by software algorithms and electronic architectures. Recently, both supervised and unsupervised learnings were obtained in photonic neurons by means of spatial-soliton-waveguide X-junctions. This paper investigates the behavior of networks based on these solitonic neurons, which are capable of performing complex tasks such as bit-to-bit information memorization and recognition. By exploiting photorefractive nonlinearity as if it were a biological neuroplasticity, the network modifies and adapts to the incoming signals, memorizing and recognizing them (photorefractive plasticity). The information processing and storage result in a plastic modification of the network interconnections. Theoretical description and numerical simulation of solitonic networks are reported and applied to the processing of 4-bit information.

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44

Hooten, Sean, Raymond G. Beausoleil, and Thomas Van Vaerenbergh. "Inverse design of grating couplers using the policy gradient method from reinforcement learning." Nanophotonics 10, no. 15 (October 7, 2021): 3843–56. http://dx.doi.org/10.1515/nanoph-2021-0332.

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Abstract We present a proof-of-concept technique for the inverse design of electromagnetic devices motivated by the policy gradient method in reinforcement learning, named PHORCED (PHotonic Optimization using REINFORCE Criteria for Enhanced Design). This technique uses a probabilistic generative neural network interfaced with an electromagnetic solver to assist in the design of photonic devices, such as grating couplers. We show that PHORCED obtains better performing grating coupler designs than local gradient-based inverse design via the adjoint method, while potentially providing faster convergence over competing state-of-the-art generative methods. As a further example of the benefits of this method, we implement transfer learning with PHORCED, demonstrating that a neural network trained to optimize 8° grating couplers can then be re-trained on grating couplers with alternate scattering angles while requiring >10× fewer simulations than control cases.

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45

Shi, Bin, Nicola Calabretta, and Ripalta Stabile. "Numerical Simulation of an InP Photonic Integrated Cross-Connect for Deep Neural Networks on Chip." Applied Sciences 10, no. 2 (January 9, 2020): 474. http://dx.doi.org/10.3390/app10020474.

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We propose a novel photonic accelerator architecture based on a broadcast-and-weight approach for a deep neural network through a photonic integrated cross-connect. The single neuron and the complete neural network operation are numerically simulated. The weight calibration and weighted addition are reproduced and demonstrated to behave as in the experimental measurements. A dynamic range higher than 25 dB is predicted, in line with the measurements. The weighted addition operation is also simulated and analyzed as a function of the optical crosstalk and the number of input colors involved. In particular, while an increase in optical crosstalk negatively influences the simulated error, a greater number of channels results in better performance. The iris flower classification problem is solved by implementing the weight matrix of a trained three-layer deep neural network. The performance of the corresponding photonic implementation is numerically investigated by tuning the optical crosstalk and waveguide loss, in order to anticipate energy consumption per operation. The analysis of the prediction error as a function of the optical crosstalk per layer suggests that the first layer is essential to the final accuracy. The ultimate accuracy shows a quasi-linear dependence between the prediction accuracy and the errors per layer for a normalized root mean square error lower than 0.09, suggesting that there is a maximum level of error permitted at the first layer for guaranteeing a final accuracy higher than 89%. However, it is still possible to find good local minima even for an error higher than 0.09, due to the stochastic nature of the network we are analyzing. Lower levels of path losses allow for half the power consumption at the matrix multiplication unit, for the same error level, offering opportunities for further improved performance. The good agreement between the simulations and the experiments offers a solid base for studying the scalability of this kind of network.

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46

Sharma, Sunil, and Lokesh Tharani. "Use of AI Techniques on Photonic Crystal Sensing for the Detection of Tumor." Journal of Electronics, Electromedical Engineering, and Medical Informatics 4, no. 2 (April 29, 2022): 62–69. http://dx.doi.org/10.35882/jeeemi.v4i2.2.

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Tumors can cause severe problem to human beings. Sometime it can be a cause of death. Earlier there were lack of treatment and technological deficiency, due to which it was unable to detect tumor cells and even unable to offer proper treatment for these diseases. This study aims to use Photonic crystal (PhC) due to their ample choice of structures and litheness to endure with every sphere of influence has been utilized them twenty decade back to now a day and have extremely huge prospects in imminent future also. They have revealed their incidence in the field of imaging, sensing, fabricating industries, automation, medical, mechatronics, computronics, mechanochromic, underwater acoustic detection, pharma industries and nanoimprinting etc. If we are discussing about current and impending applications of PhC then it comprises smart sensing and detection of disunite diseases, anonymous viruses and a range of tumors. Artificial intelligence (AI) is also playing incredibly essential role in analyzing and creating entities equivalent to the change in human behavior. AI tools and techniques are utilizing to create intelligent entities through which it is accomplishing countless feats. The PhC along with the artificial intelligence are utilizing as Optical Neural Network (ONN), Artificial Neural Network (ANN), Cellular Computing, Plasma Technology, Parallel Processing, Image Processing etc. Here in this study designated photonic crystal has been used for the detection of infected cell in human body. Sometimes these infected cells are unable to trace by normal pathological investigations and slowly they take a shape of Tumors. But thanks to Photonics crystal sensors that they have made it true not only for detection but we can say for early detection of such tumors in human body. These early detection and proper investigation is possible only because of AI impacts on photonics crystal. This study focuses on detection and observation of bio molecules for selectivity, sensitivity, reflectivity and concentration. By change in wavelength i.e. from 1.5 μm to 4 μm the refractive index (RI) of tumor cell can be measured which is observed by measuring sensitivity between 11258 nm/RIU to 32358 nm/RIU. Tumors have refractive indices varies between 1.3342 to 1.4251. It is observed that sarcoma level is directly proportional to the RI of tumor. Various AI algorithms like support vector machine (SVM) obtained accuracy as 96%, K- nearest neighbor (KNN) shows as 70%, logistic regression (LR) shows as 88%, random forest (RF) show it as 90%, fuzzy logic (FL) and artificial neural network (ANN) observed accuracy as 93% and 95% respectively.

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47

Kumar, Hardik, Tanya Jain, Mritunjay Sharma, and Kamal Kishor. "Neural network approach for faster optical properties predictions for different PCF designs." Journal of Physics: Conference Series 2070, no. 1 (November 1, 2021): 012001. http://dx.doi.org/10.1088/1742-6596/2070/1/012001.

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Abstract Photonic Crystal Fibres (PCFs) are emerging as an alternative to standard fibres for applications in many disciplines like fibre lasers & amplifiers, imaging, spectroscopy and telecommunications. They have superior light guiding properties compared to ordinary Optical Fibres (OFs). This paper illustrates the potential of neural networks to efficiently and accurately compute the optical properties of PCFs including solid-core, hollow-core and multi-core designs. The proposed method takes a range of design parameters and wavelengths as input to predict PCF optical properties like effective index, effective mode area, confinement loss and dispersion desired for optimal specifications. The neural network approach is significantly better in terms of the low computational runtimes (~5 milli-sec) required for predicting the properties against the longer runtimes (~18 sec) required for similar calculations by traditional numerical methods.

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48

Xiang, Shuiying, Zhenxing Ren, Yahui Zhang, Ziwei Song, Xingxing Guo, Genquan Han, and Yue Hao. "Training a Multi-Layer Photonic Spiking Neural Network With Modified Supervised Learning Algorithm Based on Photonic STDP." IEEE Journal of Selected Topics in Quantum Electronics 27, no. 2 (March 2021): 1–9. http://dx.doi.org/10.1109/jstqe.2020.3005589.

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49

Shi, Bin, Nicola Calabretta, and Ripalta Stabile. "Deep Neural Network Through an InP SOA-Based Photonic Integrated Cross-Connect." IEEE Journal of Selected Topics in Quantum Electronics 26, no. 1 (January 2020): 1–11. http://dx.doi.org/10.1109/jstqe.2019.2945548.

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50

Wei, Pu, Cheng Cheng, and Tingting Liu. "A Photonic Transducer-Based Optical Current Sensor Using Back-Propagation Neural Network." IEEE Photonics Technology Letters 28, no. 14 (July 15, 2016): 1513–16. http://dx.doi.org/10.1109/lpt.2016.2557339.

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