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Co‑packaged optics(CPO):status,challenges,and solutions

Frontiers of Optoelectronics 2023年第1期16卷 1-40页

作者： Min Tan Jiang Xu Siyang Liu Junbo Feng Hua Zhang Chaonan Yao Shixi Chen Hangyu Guo Gengshi Han Zhanhao Wen Bao Chen Yu He Xuqiang Zheng Da Ming Yaowen Tu Qiang Fu Nan Qi Dan Li Li Geng Song Wen Fenghe Yang Huimin He Fengman Liu Haiyun Xue Yuhang Wang Ciyuan Qiu Guangcan Mi Yanbo Li Tianhai Chang Mingche Lai Luo Zhang Qinfen Hao Mengyuan Qin School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong KongChina HKUST Fok Ying Tung Research Institute Guangzhou 511462China The Hong Kong University of Science and Technology(Guangzhou) Guangzhou 511462China Chongqing United Micro-Electronics Center(CUMEC) Chongqing 401332China Hisense Broadband Multimedia Technologies Co. Ltd.Qingdao 266000China Institute of Microelectronics Chinese Academy of SciencesBeijing 100029China State Key Laboratory of Superlattices and Microstructures Institute of SemiconductorsChinese Academy of SciencesBeijing 100083China School of Microelectronics Xi’an Jiaotong UniversityXi’an 710049China Zhangjiang Laboratory Shanghai 201210China The State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghai 200240China Huawei Technologies Co. Ltd.Shenzhen 440307China College of Computer National University of Defense TechnologyChangsha 410073China Institute of Computing Technology Chinese Academy of SciencesBeijing 100086China

Due to the rise of 5G,IoT,AI,and high-performance computing applications,datacenter trafc has grown at a compound annual growth rate of nearly 30%.Furthermore,nearly three-fourths of the datacenter trafc resides within *** conventional pluggable optics increases at a much slower rate than that of datacenter *** gap between application requirements and the capability of conventional pluggable optics keeps increasing,a trend that is *** optics(CPO)is a disruptive approach to increasing the interconnecting bandwidth density and energy efciency by dramatically shortening the electrical link length through advanced packaging and co-optimization of electronics and *** is widely regarded as a promising solution for future datacenter interconnections,and silicon platform is the most promising platform for large-scale *** international companies(e.g.,Intel,Broadcom and IBM)have heavily investigated in CPO technology,an inter-disciplinary research feld that involves photonic devices,integrated circuits design,packaging,photonic device modeling,electronic-photonic co-simulation,applications,and *** review aims to provide the readers a comprehensive overview of the state-of-the-art progress of CPO in silicon platform,identify the key challenges,and point out the potential solutions,hoping to encourage collaboration between diferent research felds to accelerate the development of CPO technology.

关键词： Co-packaged optics Silicon photonics high-performance computing Advanced packaging External laser Optical power delivery Co-simulation Standardization Transmitter Receiver

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Coordination of networking and computing: toward new information infrastructure and new services mode

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Frontiers of information Technology & Electronic engineering 2024年第5期25卷 629-632页

作者： Xiaoyun WANG Tao SUN Yong CUI Rajkumar BUYYA Deke GUO Qun HUANG Hassnaa MOUSTAFA Chen TIAN Shangguang WANG China Mobile Communications Corporation Beijing 100032China China Mobile Research Institute Beijing 100053China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China CLOUDS Lab School of Computing and Information SystemsUniversity of MelbourneMelbourneVIC 3010Australia National Key Laboratory of Information Systems Engineering National University of Defense TechnologyChangsha 410073China School of Computer Science Peking UniversityBeijing 100871China Intel Corporation Santa ClaraCA 95054USA State Key Laboratory for Novel Sofiware Technology Nanjing UniversityNanjing 210023China State Key Laboratory of Networking and Switching Technology Bejing University of Posts and TelecommunicationsBeijing 100876China

computing,serving as the cornerstone of information processing,plays a pivotal role in the digital service ***"network"and"computing,"responsible for information transmission and processing respectively,traditionally belong to different stakeholders and have evolved ***,the recent trend toward the coordination and integration of computing and networks has garnered significant attention from both industry and *** such as"computility network"and"computing force network"have emerged,and the International Telecommunication Union-Telecommunication Standardization(ITU-T)has initiated efforts to develop standards for the coordination of networking and computing(CNC),focusing on the architecture and framework.

关键词： computing services network

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Mixture Gaussian Distribution-Based Collaborative Reinforcement Learning for 3D UAV Localization Optimization Against Jamming Attacks

Mixture Gaussian Distribution-Based Collaborative Reinforcem...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yujiao Zhu Mingzhe Chen Sihua Wang Yuchen Liu Gaolei Li Changchuan Yin Tony Q.S. Quek Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Computer Science North Carolina State University Raleigh NC USA School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, the optimization of unmanned aerial vehicle (UAV) localization under jamming attacks is studied. In the considered network, a base station (BS) collaborates with an active UAV to localize a target UAV. During this positioning process, a jamming UAV transmits discontinuous signals to passive UAVs to interfere the distance information measurement. To localize the target UAV under jamming attacks, the BS jointly use two localization methods: 1) generative adversarial network (GAN)-based positioning method and 2) time difference of arrival (TDOA)-based positioning method. Since GAN-based positioning method cannot defense in a strong jamming signal while TDOA-based positioning method may consume more energy and sacrifice localization accuracy, the BS must select an appropriate positioning method (GAN-based or TDOA-based methods) and four distance measurement information of passive UAVs to estimate the position of the target UAV. This problem is formulated as an optimization problem whose goal is to minimize the positioning error between the estimated and the ground truth positions of the target UAV while considering jamming attacks and the trajectory of passive UAVs. To solve this problem, we propose a mixture Gaussian distribution model-based collaborative reinforcement learning (RL) method which enables the active UAV to determine its transmit power and trajectory, and enables the BS to select the most appropriate subsets of distance measurement information and the optimal positioning method according to the movement of passive UAVs and the unknown jamming attack pattern of the jamming UAV. Simulation results show the proposed method can reduce the positioning error of the target UAV by up to 36.5% compared to the method that does not consider the GAN-based positioning method.

关键词： Location awareness Simulation Collaboration Reinforcement learning Gaussian distribution Autonomous aerial vehicles Distance measurement Trajectory Jamming Optimization

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Self-Supervised Multi-LiDAR Object View Generation Using Single LiDAR

Self-Supervised Multi-LiDAR Object View Generation Using Sin...

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IEEE International Conference on Embedded and Real-Time computing Systems and Applications (RTCSA)

作者： Yi-Hung Kuo Chi-Sheng Shih Hsiang-Jui Lin Shih-Hao Hung Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan High Performance and Scientific Computing Center National Taiwan University Taipei Taiwan Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi

3D object detection using point clouds has been widely used for self-driving vehicles, roadside vehicle detection, and tracking. However, the detection accuracy using single-LiDAR-scanned point clouds suffers from occlusion to have accurate bounding boxes. Several previous works proposed compensating for the point cloud to improve 3D detection. This work designed a self-labeling method that exploits the characteristics of multiple LiDAR-scanned point clouds collected on roadside units. This labeling method allows the point completion model to be trained without human-labeled bounding boxes. The point completion model uses the single-LiDAR-scanned point cloud as input and uses the extracted object points from the fused point cloud as the training target. The voxel IoU and recall of the point-completed point clouds increased by 17% and 22% compared to that of the raw point clouds. Additionally, the detection performance of DBSCAN, i.e., 3D AP@IoU=0.25 increased by 20% after applying the point completion algorithm.

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AgileWatts: An Energy-Efficient CPU Core Idle-State Architecture for Latency-Sensitive Server Applications 22

AgileWatts: An Energy-Efficient CPU Core Idle-State Architec...

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Proceedings of the 55th Annual IEEE/ACM International Symposium on Microarchitecture

作者： Jawad Haj Yahya Haris Volos Davide B. Bartolini Georgia Antoniou Jeremie S. Kim Zhe Wang Kleovoulos Kalaitzidis Tom Rollet Zhirui Chen Ye Geng Onur Mutlu Yiannakis Sazeides Rivos Inc Mountain View California United States Computer Architecture Laboratory Department of Computer Science University of Cyprus Computing Systems Lab Huawei Zurich Research Center Department of Information Technology and Electrical Engineering ETH Zurich Switzerland

ISBN: (纸本)9781665462723

User-facing applications running in modern datacenters exhibit irregular request patterns and are implemented using a multitude of services with tight latency requirements (30--250μs). These characteristics render existing energy-conserving techniques ineffective when processors are idle due to the long transition time (order of 100μs) from a deep CPU core idle power state (C-state). While prior works propose management techniques to mitigate this inefficiency, we tackle it at its root with AgileWatts (AW): a new deep CPU core C-state architecture optimized for datacenter server processors targeting latency-sensitive *** drastically reduces the transition latency from deep CPU core idle power states while retaining most of their power savings based on three key ideas. First, AW eliminates the latency (several microseconds) of saving/restoring the core context when powering-off/-on the core in a deep idle state by i) implementing medium-grained power-gates, carefully distributed across the CPU core, and ii) retaining context in the power-ungated domain. Second, AW eliminates the flush latency (several tens of microseconds) of the L1/L2 caches when entering a deep idle state by keeping L1/L2 content power-ungated. A small control logic also remains ungated to serve cache coherence traffic. AW implements cache sleep-mode and leakage reduction for the power-ungated domain by lowering a core's voltage to the minimum operational level. Third, using a state-of-the-art power efficient all-digital phase-locked loop (ADPLL) clock generator, AW keeps the PLL active and locked during the idle state, cutting microseconds of wake-up latency at negligible power *** evaluation with an accurate industrial-grade simulator calibrated against an Intel Skylake server shows that AW reduces the energy consumption of Memcached by up to 71% (35% on average) with <1% end-to-end performance degradation. We observe similar trends for other evaluated services (MySQL and Kafka).

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Distributed Traffic engineering in Hybrid Software Defined Networks: A Multi-agent Reinforcement Learning Framework

arXiv

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arXiv 2023年

作者： Guo, Yingya Tang, Qi Ma, Yulong Tian, Han Chen, Kai The College of Computer and Data Science Fuzhou University China The Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China The Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fujian 350003 China The Department of Computer Science & Engineering Hong Kong University of Science & Technology Hong Kong

Traffic engineering (TE) is an efficient technique to balance network flows and thus improves the performance of a hybrid Software Defined Network (SDN). Previous TE solutions mainly leverage heuristic algorithms to centrally optimize link weight setting or traffic splitting ratios under the static traffic demand. Note that as the network scale becomes larger and network management gains more complexity, it is notably that the centralized TE methods suffer from a high computation overhead and a long reaction time to optimize routing of flows when the network traffic demand dynamically fluctuates or network failures happen. To enable adaptive and efficient routing in TE, we propose a Multi-agent Reinforcement Learning method CMRL that divides the routing optimization of a large network into multiple small-scale routing decision-making problems. To coordinate the multiple agents for achieving a global optimization goal, we construct an interactive environment for training the routing agents that own partial link utilization observations. To optimize credit assignment of multi-agent, we introduce the difference reward assignment mechanism for encouraging agents to take better action. Extensive simulations conducted on the real traffic traces demonstrate the superiority of CMRL in improving TE performance, especially when traffic demands change or network failures happen. Copyright © 2023, The Authors. All rights reserved.

关键词： Global optimization

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Joint Optimization Air Base Stations Deployment and User Association in Heterogeneous Space-Air-Ground Integrated Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Ao, Shaoyou Niu, Yong Han, Zhu Xiong, Lei Wang, Ning Ai, Bo Beijing Jiaotong University Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications School of electronic and Information Engineering Beijing100044 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China Zhengzhou University School of Information Engineering Zhengzhou450001 China

With the development of 6 G and beyond, space-air-ground integrated networks (SAGINs) have evolved into key elements to promote high-speed and seamless connections for users. This paper studies a heterogeneous SAGIN with satellite, multiple unmanned aerial vehicles (UAVs) and high-altitude platforms (HAPs) serve as aerial base stations (ABSs) in a scenario where ground base stations (GBSs) are overloaded. To optimize the number of ABSs within the system's power limit and maximize the system's downlink throughput, we optimize the 3D deployment of ABSs and user association in the millimeter wave (mmWave) frequency band. We formulate an optimization problem to satisfy the access quality of service (QoS) requirements. This problem is a mixed- integer nonlinear programming problem. Therefore, we divide it into two subproblems: the heterogeneous network ABS deployment problem and the user association problem. We propose a Broyden-Fletcher-Goldfarb-Shanno (BFGS) formula based sequential quadratic programming (SQP) algorithm for the ABS deployment problem and a coalition game algorithm for the user association problem. Finally, we employ a block coordinate descent (BCD) algorithm to iteratively solve the two subproblems until convergence. Extensive simulations with various parameters and user deployments demonstrate that our algorithm outperforms other selected schemes. © 1967-2012 IEEE.

关键词： Quadratic programming

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A Survey on Federated Learning Poisoning Attacks and Defenses

arXiv

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arXiv 2023年

作者： Liang, Junchuan Wang, Rong Feng, Chaosheng Chang, Chin-Chen School of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Sichuan Normal University Chengdu China Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan

As one kind of distributed machine learning technique, federated learning enables multiple clients to build a model across decentralized data collaboratively without explicitly aggregating the data. Due to its ability to break data silos, federated learning has received increasing attention in many fields, including finance, healthcare, and education. However, the invisibility of clients’ training data and the local training process result in some security issues. Recently, many works have been proposed to research the security attacks and defenses in federated learning, but there has been no special survey on poisoning attacks on federated learning and the corresponding defenses. In this paper, we investigate the most advanced schemes of federated learning poisoning attacks and defenses and point out the future directions in these areas. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning systems

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Harnessing Large Vision and Language Models in Agriculture: A Review

arXiv

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arXiv 2024年

作者： Zhu, Hongyan Qin, Shuai Su, Min Lin, Chengzhi Li, Anjie Gao, Junfeng Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin China Education Department of Guangxi Zhuang Autonomous Region Guilin China Department of Computer Science University of Aberdeen United Kingdom

Large models can play important roles in many domains. Agriculture is another key factor affecting the lives of people around the world. It provides food, fabric, and coal for humanity. However, facing many challenges such as pests and diseases, soil degradation, global warming, and food security, how to steadily increase the yield in the agricultural sector is a problem that humans still need to solve. Large models can help farmers improve production efficiency and harvest by detecting a series of agricultural production tasks such as pests and diseases, soil quality, and seed quality. It can also help farmers make wise decisions through a variety of information, such as images, text, etc. Herein, we delve into the potential applications of large models in agriculture, from large language model (LLM) and large vision model (LVM) to large vision-language models (LVLM). After gaining a deeper understanding of multimodal large language models (MLLM), it can be recognized that problems such as agricultural image processing, agricultural question answering systems, and agricultural machine automation can all be solved by large models. Large models have great potential in the field of agriculture. We outline the current applications of agricultural large models, and aims to emphasize the importance of large models in the domain of agriculture. In the end, we envisage a future in which famers use MLLM to accomplish many tasks in agriculture, which can greatly improve agricultural production efficiency and yield. © 2024, CC BY.

关键词： Fertilizers

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GaRField++: Reinforced Gaussian Radiance Fields for Large-Scale 3D Scene Reconstruction

arXiv

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arXiv 2024年

作者： Zhang, Hanyue Yang, Zhiliu Zuo, Xinhe Tong, Yuxin Long, Ying Liu, Chen School of Information Science and Engineering Yunnan University Yunnan Kunming650500 China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Yunnan Kunming650500 China Department of Electrical and Computer Engineering Clarkson University PotsdamNY13699 United States

This paper proposes a novel framework for large-scale scene reconstruction based on 3D Gaussian splatting (3DGS) and aims to address the scalability and accuracy challenges faced by existing methods. For tackling the scalability issue, we split the large scene into multiple cells, and the candidate point-cloud and camera views of each cell are correlated through a visibility-based camera selection and a progressive point-cloud extension. To reinforce the rendering quality, three highlighted improvements are made in comparison with vanilla 3DGS, which are a strategy of the ray-Gaussian intersection and the novel Gaussians density control for learning efficiency, an appearance decoupling module based on ConvKAN network to solve uneven lighting conditions in large-scale scenes, and a refined final loss with the color loss, the depth distortion loss, and the normal consistency loss. Finally, the seamless stitching procedure is executed to merge the individual Gaussian radiance field for novel view synthesis across different cells. Evaluation of Mill19, Urban3D, and MatrixCity datasets shows that our method consistently generates more high-fidelity rendering results than state-of-the-art methods of large-scale scene reconstruction. We further validate the generalizability of the proposed approach by rendering on self-collected video clips recorded by a commercial drone. © 2024, CC BY.

关键词： 3D reconstruction

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