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High-speed photonic neuromorphic computing using recurrent optical spectrum slicing neural networks

Communications Engineering 2022年第1期1卷 1-10页

作者： Sozos, Kostas Bogris, Adonis Bienstman, Peter Sarantoglou, George Deligiannidis, Stavros Mesaritakis, Charis Dept. of Informatics and Computer Engineering University of West Attica Egaleo Greece Dept. of Information Technology Ghent University-imec Gent Belgium Dept. Information and Communication Systems Engineering Engineering School University of the Aegean Samos Greece

Neuromorphic computing using photonic hardware is a promising route towards ultrafast processing while maintaining low power consumption. Here we present and numerically evaluate a hardware concept for realizing photonic recurrent neural networks and reservoir computing architectures. Our method, called Recurrent optical Spectrum Slicing neural networks (ROSS-NNs), uses simple optical filters placed in a loop, where each filter processes a specific spectral slice of the incoming optical signal. The synaptic weights in our scheme are equivalent to the filters’ central frequencies and bandwidths. Numerical application to high baud rate optical signal equalization (>100 Gbaud) reveals that ROSS-NN extends optical signal transmission reach to > 60 km, more than four times that of two state-of-the-art digital equalizers. Furthermore, ROSS-NN relaxes complexity, requiring less than 100 multiplications/bit in the digital domain, offering tenfold reduction in power consumption with respect to these digital counterparts. ROSS-NNs hold promise for efficient photonic hardware accelerators tailored for processing high-bandwidth (>100 GHz) optical signals in optical communication and high-speed imaging applications. © The Author(s) 2022.

关键词： Computer science Electrical and electronic engineering Photonic devices

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Hardware/Software Co-Design With ADC-Less In-Memory computing Hardware for Spiking neural networks

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IEEE TRANSACTIONS ON EMERGING TOPICS IN computing 2024年第1期12卷 35-47页

作者： Apolinario, Marco Paul E. Kosta, Adarsh Kumar Saxena, Utkarsh Roy, Kaushik Purdue Univ Elmore Family Sch Elect & Comp Engn W Lafayette IN 47907 USA

Spiking neural networks (SNNs) are bio-plausible models that hold great potential for realizing energy-efficient implementations of sequential tasks on resource-constrained edge devices. However, commercial edge platforms based on standard GPUs are not optimized to deploy SNNs, resulting in high energy and latency. While analog In-Memory computing (IMC) platforms can serve as energy-efficient inference engines, they are accursed by the immense energy, latency, and area requirements of high-precision ADCs (HP-ADC), overshadowing the benefits of in-memory computations. We propose a hardware/software co-design methodology to deploy SNNs into an ADC-Less IMC architecture using sense-amplifiers as 1-bit ADCs replacing conventional HP-ADCs and alleviating the above issues. Our proposed framework incurs minimal accuracy degradation by performing hardware-aware training and is able to scale beyond simple image classification tasks to more complex sequential regression tasks. Experiments on complex tasks of optical flow estimation and gesture recognition show that progressively increasing the hardware awareness during SNN training allows the model to adapt and learn the errors due to the non-idealities associated with ADC-Less IMC. Also, the proposed ADC-Less IMC offers significant energy and latency improvements, 2-7 and8.9-24.6 chi, respectively, depending on the SNN model and the workload, compared to HP-ADC IMC.

关键词： ADC-Less HW/SW co-design in-memory computing spiking neural networks

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neural computing with coherent laser networks

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NANOPHOTONICS 2023年第5期12卷 883-892页

作者： Miri, Mohammad-Ali Menon, Vinod CUNY Queens Coll Dept Phys Queens NY 11367 USA CUNY Grad Ctr Phys Program New York NY 10016 USA CUNY City Coll Dept Phys New York NY 10031 USA

We show that coherent laser networks (CLNs) exhibit emergent neural computing capabilities. The proposed scheme is built on harnessing the collective behavior of laser networks for storing a number of phase patterns as stable fixed points of the governing dynamical equations and retrieving such patterns through proper excitation conditions, thus exhibiting an associative memory property. It is discussed that despite the large storage capacity of the network, the large overlap between fixed-point patterns effectively limits pattern retrieval to only two images. Next, we show that this restriction can be uplifted by using nonreciprocal coupling between lasers and this allows for utilizing a large storage capacity. This work opens new possibilities for neural computation with coherent laser networks as novel analog processors. In addition, the underlying dynamical model discussed here suggests a novel energy-based recurrent neural network that handles continuous data as opposed to Hopfield networks and Boltzmann machines that are intrinsically binary systems.

关键词： lasers machine learning neural networks nonlinear dynamics optical computing

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Design automation for optical computing: Boolean logic and neural networks

Design automation for optical computing: Boolean logic and n...

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作者： Zhao, Zheng University of Texas Austin

学位级别：博士

As a promising alternative to traditional CMOS circuits, optics has demonstrated the ability to realize ultra-high speed and low-power information processing and communications. For optical computing tasks including Boolean logic and neural networks, however, there still exist challenges such as optical power efficiency, bulkiness and noise-robustness. To address the aforementioned issues, this dissertation proposes a set of algorithms, methodology, and architectures for optical computing tasks, which include: a synthesis flow that significantly reduces the optical power loss; a set of synthesis algorithms that exploits wavelength-division multiplexing (WDM) for area-efficient optical logic construction; a hardware-software codesign methodology that generates more area-efficient and robust ONNs; and an on-chip, integratable photonic Elman RNN architecture that empowers photonic RNNs the capability of training and tuning the state transformation for the first time. For the first work, we study the long-neglected optical power depletion problem in previous optical boolean logic synthesis, and propose graph transform techniques along with the exploitation of better optical devices to address this problem. The experiments where various sources of optical power depletion are considered, show the efficacy of our method of generating optical power efficient optical circuits, which also helps to build a much more robust and scalable integrated photonic system. In the second work, we exploit a special property of light in optical logic synthesis to reduce the number of optical components. The great potential of adopting WDM for efficient optical logic construction, is pinpointed and a systematic synthesis flow is designed considering the practical capacity constraint. Mathematically, we demonstrate the affinity of the capacity-constrained synthesis problem to the hypergraph partitioning and the min-cost max-flow problem. The experiments show the efficiency and efficacy of ou

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Robustness of Neuromorphic computing with RRAM-based Crossbars and optical neural networks

Robustness of Neuromorphic Computing with RRAM-based Crossba...

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Asia and South Pacific Design Automation Conference

作者： Grace Li Zhang Bing Li Ying Zhu Tianchen Wang Yiyu Shi Xunzhao Yin Cheng Zhuo Huaxi Gu Tsung-Yi Ho Ulf Schlichtmann Chair of Electronic Design Automation Technical University of Munich (TUM) Germany University of Notre Dame United States College of Information Science and Electronic Engineering Zhejiang Unversity China School of Telecommunication Engineering Xidian University China National Tsing Hua University Taiwan

ISBN: (数字)9781450379991

ISBN: (纸本)9781728180571

RRAM-based crossbars and optical neural networks are attractive platforms to accelerate neuromorphic computing. However, both accelerators suffer from hardware uncertainties such as process variations. These uncertainty issues left unaddressed, the inference accuracy of these computing platforms can degrade significantly. In this paper, a statistical training method where weights under process variations and noise are modeled as statistical random variables is presented. To incorporate these statistical weights into training, the computations in neural networks are modified accordingly. For optical neural networks, we modify the cost function during software training to reduce the effects of process variations and thermal imbalance. In addition, the residual effects of process variations are extracted and calibrated in hardware test, and thermal variations on devices are also compensated in advance. Simulation results demonstrate that the inference accuracy can be improved significantly under hardware uncertainties for both platforms.

关键词： Training Uncertainty neural networks Hardware Software Random variables optical noise

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Photonic Bayesian neural networks: Leveraging Programmable Noise for Robust and Uncertainty-Aware computing

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ADVANCED SCIENCE 2025年

作者： Zhuge, Yangyang Ren, Zhihao Xiao, Zian Zhang, Zixuan Liu, Xinmiao Liu, Weixin Xu, Siyu Ho, Chong Pei Li, Nanxi Lee, Chengkuo Natl Univ Singapore Dept Elect & Comp Engn 4 Engn Dr 3 Singapore 117583 Singapore Natl Univ Singapore Ctr Intelligent Sensors & MEMS 4 Engn Dr 3 Singapore 117583 Singapore ASTAR Inst Microelect IME 2 Fusionopolis Way Innovis 08-02 Singapore 138634 Singapore Natl Univ Singapore Natl Ctr Adv Integrated Photon NCAIP Singapore 119077 Singapore NUS Suzhou Res Inst NUSRI Suzhou 215123 Jiangsu Peoples R China Natl Univ Singapore NUS Grad Sch Integrat Sci & Engn Programme ISEP Singapore 119077 Singapore

Photonic neural networks (PNNs) based on silicon photonic integrated circuits (Si-PICs) offer significant advantages over microelectronic counterparts, including lower energy consumption, higher bandwidth, and faster computing speeds. However, the analog nature of optical signal in PNNs makes Si-PIC solutions highly sensitive to device noise, especially when using fixed-value deterministic models, which are not robust to hardware fluctuation. Furthermore, current PNNs are unable to handle data uncertainty, a critical factor in applications such as autonomous driving, medical diagnostics, and financial forecasting. Herein, a photonic Bayesian neural network (PBNN) architecture that incorporates Bayesian principles to enhance robustness and address uncertainty is proposed. In the PBNN, device noise is leveraged through photonic-noise-based random number generators, which combine Mach-Zehnder interferometers and micro-ring resonators to independently control output mean and standard deviation. Based on modelling with experimentally extracted data, the PBNN achieves a classification accuracy of up to 98% for handwritten digit recognition, matching full-precision models on conventional computers. Beyond classification, the PBNN excels in multimodal data processing, regression, and outlier detection. This scalable, energy-efficient architecture transforms photonic noise into computational value, addressing the limitations of deterministic PNNs and enabling uncertainty-aware computing for real-world applications.

关键词： Bayesian neural network multimodal data processing photonic probabilistic computing photonic random number generator silicon photonics

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All optical neural networks for Low Power Edge computing

All Optical Neural Networks for Low Power Edge Computing

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作者： Sarma, Raktim Briscoe, Jayson L. Goldflam, Michael D. Sandia National Lab. (SNL-NM) Albuquerque NM (United States)

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Closed-Loop Physical Reservoir computing with Optogenetic Control in Biological neural networks

Closed-Loop Physical Reservoir Computing with Optogenetic Co...

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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on Information Photonics and optical Communications, IPOC 2024

作者： Li, Jie Deng, Yin Lin, Yarong Lu, Zeying Sun, Xiaojuan Lan, Yueheng Sha, Longze Gui, Lili Xu, Kun School of Electronic Engineering Beijing University of Posts and Telecommunications State Key Laboratory of Information Photonics and Optical Communications Beijing China Department of Biochemistry and Molecular Biology Chinese Academy of Medical Sciences Peking Union Medical College State Key Laboratory of Common Mechanism Research for Major Diseases Beijing China School of Science Beijing University of Posts and Telecommunications State Key Laboratory of Information Photonics and Optical Communications Beijing China

ISBN: (纸本)9798350379266

This study introduces physical reservoir computing (PRC) using in vitro biological neural networks for efficient signal processing and *** system employs optogenetic control and First-Order Reduced and Controlled Error (FORCE) learning to enable a virtual car to avoid *** neuronal activity is captured with a CMOS camera based on calcium imaging and optical stimulation is performed via a digital micromirror device (DMD).Neuronal action potentials, induced by light, guide the movement of the *** adaptive learning system demonstrates effective error minimization and robust obstacle avoidance, advancing neuro-engineering applications and intelligent biohybrid systems. © 2024 IEEE.

关键词： biological neural network FORCE learning optogenetics Physical reservoir computing

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Advancing brain-inspired computing with hybrid neural networks

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National Science Review 2024年第5期11卷 56-71页

作者： Faqiang Liu Hao Zheng Songchen Ma Weihao Zhang Xue Liu Yansong Chua Luping Shi Rong Zhao Center for Brain-Inspired Computing Research Optical Memory National Engineering Research CenterTsinghua University-China Electronics Technology HIK Group Co.Joint Research Center for Brain-inspired ComputingIDGMc Govern Institute for Brain Research Department of Precision Instrument Tsinghua University Neuromorphic Computing Laboratory China Nanhu Academy of Electronics and Information Technology

Brain-inspired computing, drawing inspiration from the fundamental structure and information-processing mechanisms of the human brain, has gained significant momentum in recent years. It has emerged as a research paradigm centered on brain–computer dual-driven and multi-network integration. One noteworthy instance of this paradigm is the hybrid neural network(HNN), which integrates computer-science-oriented artificial neural networks(ANNs) with neuroscience-oriented spiking neural networks(SNNs). HNNs exhibit distinct advantages in various intelligent tasks, including perception, cognition and learning. This paper presents a comprehensive review of HNNs with an emphasis on their origin, concepts, biological perspective, construction framework and supporting systems. Furthermore, insights and suggestions for potential research directions are provided aiming to propel the advancement of the HNN paradigm.

关键词： brain-inspired computing hybrid neural network dual-brain driven multi-network integration neuromorphic system

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optical neural networks:progress and challenges

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Light(Science & Applications) 2024年第11期13卷 2511-2535页

作者： Tingzhao Fu Jianfa Zhang Run Sun Yuyao Huang Wei Xu Sigang Yang Zhihong Zhu Hongwei Chen College of Advanced Interdisciplinary Studies National University of Defense TechnologyChangshaChina Hunan Provincial Key Laboratory of Novel Nano-Optoelectronic Information Materials and Devices National University of Defense TechnologyChangshaChina Nanhu Laser Laboratory National University of Defense TechnologyChangshaChina Department of Electronic Engineering Tsinghua UniversityBeijingChina Beijing National Research Center for Information Science and Technology(BNRist) BeijingChina

Artificial intelligence has prevailed in all trades and professions due to the assistance of big data resources,advanced algorithms,and high-performance electronic ***,conventional computing hardware is inefficient at implementing complex tasks,in large part because the memory and processor in its computing architecture are separated,performing insufficiently in computing speed and energy *** recent years,optical neural networks(ONNs)have made a range of research progress in optical computing due to advantages such as subnanosecond latency,low heat dissipation,and high *** are in prospect to provide support regarding computing speed and energy consumption for the further development of artificial intelligence with a novel computing ***,we first introduce the design method and principle of ONNs based on various optical ***,we successively review the non-integrated ONNs consisting of volume optical components and the integrated ONNs composed of on-chip ***,we summarize and discuss the computational density,nonlinearity,scalability,and practical applications of ONNs,and comment on the challenges and perspectives of the ONNs in the future development trends.

关键词： networks neural hardware

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