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Proceedings of the IEEE Publication Information 电气和电子工程师学会论文集》出版信息

IF 20.6 1区计算机科学

Proceedings of the IEEE Pub Date : 2023-12-18 DOI: 10.1109/JPROC.2023.3339897

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Proceedings of the IEEE: Stay Informed. Become Inspired. 电气和电子工程师学会论文集》：保持信息灵通。激发灵感。

IF 20.6 1区计算机科学

Proceedings of the IEEE Pub Date : 2023-12-18 DOI: 10.1109/JPROC.2023.3339905

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Future Special Issues/Special Sections of the Proceedings 论文集》未来的特刊/专栏

IF 20.6 1区计算机科学

Proceedings of the IEEE Pub Date : 2023-12-18 DOI: 10.1109/JPROC.2023.3339901

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Proceedings of the IEEE Pub Date : 2023-12-18 DOI: 10.1109/JPROC.2023.3339903

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Proceedings of the IEEE Pub Date : 2023-12-18 DOI: 10.1109/JPROC.2023.3339908

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Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision 计算成像与人工智能：移动视觉的下一次革命

IF 20.6 1区计算机科学

Proceedings of the IEEE Pub Date : 2023-12-12 DOI: 10.1109/JPROC.2023.3338272

Jinli Suo;Weihang Zhang;Jin Gong;Xin Yuan;David J. Brady;Qionghai Dai

{"title":"Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision","authors":"Jinli Suo;Weihang Zhang;Jin Gong;Xin Yuan;David J. Brady;Qionghai Dai","doi":"10.1109/JPROC.2023.3338272","DOIUrl":"https://doi.org/10.1109/JPROC.2023.3338272","url":null,"abstract":"Signal capture is at the forefront of perceiving and understanding the environment; thus, imaging plays a pivotal role in mobile vision. Recent unprecedented progress in artificial intelligence (AI) has shown great potential in the development of advanced mobile platforms with new imaging devices. Traditional imaging systems based on the “capturing images first and processing afterward” mechanism cannot meet this explosive demand. On the other hand, computational imaging (CI) systems are designed to capture high-dimensional data in an encoded manner to provide more information for mobile vision systems. Thanks to AI, CI can now be used in real-life systems by integrating deep learning algorithms into the mobile vision platform to achieve a closed loop of intelligent acquisition, processing, and decision-making, thus leading to the next revolution of mobile vision. Starting from the history of mobile vision using digital cameras, this work first introduces the advancement of CI in diverse applications and then conducts a comprehensive review of current research topics combining CI and AI. Although new-generation mobile platforms, represented by smart mobile phones, have deeply integrated CI and AI for better image acquisition and processing, most mobile vision platforms, such as self-driving cars and drones only loosely connect CI and AI, and are calling for a closer integration. Motivated by this fact, at the end of this work, we propose some potential technologies and disciplines that aid the deep integration of CI and AI and shed light on new directions in the future generation of mobile vision platforms.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":null,"pages":null},"PeriodicalIF":20.6,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138739398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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A Comprehensive Survey on Distributed Training of Graph Neural Networks 图神经网络分布式训练综合调查

IF 20.6 1区计算机科学

Proceedings of the IEEE Pub Date : 2023-12-08 DOI: 10.1109/JPROC.2023.3337442

Haiyang Lin;Mingyu Yan;Xiaochun Ye;Dongrui Fan;Shirui Pan;Wenguang Chen;Yuan Xie

{"title":"A Comprehensive Survey on Distributed Training of Graph Neural Networks","authors":"Haiyang Lin;Mingyu Yan;Xiaochun Ye;Dongrui Fan;Shirui Pan;Wenguang Chen;Yuan Xie","doi":"10.1109/JPROC.2023.3337442","DOIUrl":"https://doi.org/10.1109/JPROC.2023.3337442","url":null,"abstract":"Graph neural networks (GNNs) have been demonstrated to be a powerful algorithmic model in broad application fields for their effectiveness in learning over graphs. To scale GNN training up for large-scale and ever-growing graphs, the most promising solution is distributed training that distributes the workload of training across multiple computing nodes. At present, the volume of related research on distributed GNN training is exceptionally vast, accompanied by an extraordinarily rapid pace of publication. Moreover, the approaches reported in these studies exhibit significant divergence. This situation poses a considerable challenge for newcomers, hindering their ability to grasp a comprehensive understanding of the workflows, computational patterns, communication strategies, and optimization techniques employed in distributed GNN training. As a result, there is a pressing need for a survey to provide correct recognition, analysis, and comparisons in this field. In this article, we provide a comprehensive survey of distributed GNN training by investigating various optimization techniques used in distributed GNN training. First, distributed GNN training is classified into several categories according to their workflows. In addition, their computational patterns and communication patterns, as well as the optimization techniques proposed by recent work, are introduced. Second, the software frameworks and hardware platforms of distributed GNN training are also introduced for a deeper understanding. Third, distributed GNN training is compared with distributed training of deep neural networks (DNNs), emphasizing the uniqueness of distributed GNN training. Finally, interesting issues and opportunities in this field are discussed.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":null,"pages":null},"PeriodicalIF":20.6,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138739494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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A Visionary Look at the Security of Reconfigurable Cloud Computing 对可重构云计算安全性的远见卓识

IF 20.6 1区计算机科学

Proceedings of the IEEE Pub Date : 2023-11-21 DOI: 10.1109/JPROC.2023.3330729

Mirjana Stojilović;Kasper Rasmussen;Francesco Regazzoni;Mehdi B. Tahoori;Russell Tessier

{"title":"A Visionary Look at the Security of Reconfigurable Cloud Computing","authors":"Mirjana Stojilović;Kasper Rasmussen;Francesco Regazzoni;Mehdi B. Tahoori;Russell Tessier","doi":"10.1109/JPROC.2023.3330729","DOIUrl":"https://doi.org/10.1109/JPROC.2023.3330729","url":null,"abstract":"Field-programmable gate arrays (FPGAs) have become critical components in many cloud computing platforms. These devices possess the fine-grained parallelism and specialization needed to accelerate applications ranging from machine learning to networking and signal processing, among many others. Unfortunately, fine-grained programmability also makes FPGAs a security risk. Here, we review the current scope of attacks on cloud FPGAs and their remediation. Many of the FPGA security limitations are enabled by the shared power distribution network in FPGA devices. The simultaneous sharing of FPGAs is a particular concern. Other attacks on the memory, host microprocessor, and input/output channels are also possible. After examining current attacks, we describe trends in cloud architecture and how they are likely to impact possible future attacks. FPGA integration into cloud hypervisors and system software will provide extensive computing opportunities but invite new avenues of attack. We identify a series of system, software, and FPGA architectural changes that will facilitate improved security for cloud FPGAs and the overall systems in which they are located.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":null,"pages":null},"PeriodicalIF":20.6,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138739520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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IF 20.6 1区计算机科学

Proceedings of the IEEE Pub Date : 2023-11-20 DOI: 10.1109/JPROC.2023.3328675

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IF 20.6 1区计算机科学

Proceedings of the IEEE Pub Date : 2023-11-20 DOI: 10.1109/JPROC.2023.3328660

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