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The Cavity Magnetron Developments Which Enabled the Rapid Deployment of Airborne Radar Systems in World War II 腔磁控管的发展使第二次世界大战中机载雷达系统的快速部署成为可能
IF 25.9 1区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-11-18 DOI: 10.1109/JPROC.2025.3627402
Peter M. Grant;John S. Thompson;Simon Watts
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引用次数: 0
A Taxonomy and Review of Algorithms for Modeling and Predicting Human Driver Behavior 人类驾驶行为建模与预测算法的分类与综述
IF 20.6 1区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-11-05 DOI: 10.1109/jproc.2025.3617487
Raunak Bhattacharyya, Kyle J. Brown, Juanran Wang, Katherine Driggs-Campbell, Mykel J. Kochenderfer
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引用次数: 0
Field-Programmable Gate Array Architecture for Deep Learning: Survey and Future Directions 用于深度学习的现场可编程门阵列架构:调查和未来方向
IF 25.9 1区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-10-30 DOI: 10.1109/JPROC.2025.3623023
Andrew Boutros;Aman Arora;Vaughn Betz
Deep learning (DL) is becoming the cornerstone of numerous applications both in large-scale datacenters and at the edge. Specialized hardware is often necessary to meet the performance requirements of state-of-the-art DL models, but the rapid pace of change in DL models and the wide variety of systems integrating DL make it impossible to create custom computer chips for all but the largest markets. Field-programmable gate arrays (FPGAs) present a unique blend of reprogrammability and direct hardware execution that make them suitable for accelerating DL inference. They offer the ability to customize processing pipelines and memory hierarchies to achieve lower latency and higher energy efficiency compared to general-purpose central processing units (CPUs) and graphics processing units (GPUs), at a fraction of the development time and cost of custom chips. Their diverse and high-speed inputs/outputs (IOs) also enable directly interfacing the FPGA to the network and/or a variety of external sensors, making them suitable for both datacenter and edge use cases. As DL has become an ever more important workload, FPGA architectures are evolving to enable higher DL performance. In this article, we survey both academic and industrial FPGA chip architecture enhancements for DL. First, we give a brief introduction on the basics of FPGA architecture and how its components lead to strengths and weaknesses for DL applications. Next, we discuss different design styles of DL inference accelerators implemented on FPGAs that achieve state-of-the-art performance and productive development flows, ranging from model-specific dataflow styles to software-programmable overlay styles. We survey DL-specific enhancements to traditional FPGA building blocks including the logic blocks (LBs), arithmetic circuitry, and on-chip memories, as well as new DL-specialized blocks that integrate into the FPGA fabric to accelerate tensor computations. Finally, we discuss hybrid devices that combine processors and coarse-grained accelerator blocks with FPGA-like interconnect and networks-on-chip (NoCs), and highlight promising future research directions.
深度学习(DL)正在成为大规模数据中心和边缘应用程序的基石。为了满足最先进的深度学习模型的性能要求,通常需要专门的硬件,但是深度学习模型的快速变化和集成深度学习的各种系统使得除了最大的市场之外,不可能为所有市场创建定制的计算机芯片。现场可编程门阵列(fpga)呈现出可重新编程性和直接硬件执行的独特混合,使它们适合加速DL推理。与通用中央处理单元(cpu)和图形处理单元(gpu)相比,它们提供了定制处理管道和内存层次结构的能力,以实现更低的延迟和更高的能效,而开发时间和成本仅为定制芯片的一小部分。其多样化和高速输入/输出(IOs)还可以将FPGA直接连接到网络和/或各种外部传感器,使其适用于数据中心和边缘用例。随着深度学习成为越来越重要的工作负载,FPGA架构也在不断发展,以实现更高的深度学习性能。在本文中,我们调查了学术和工业FPGA芯片架构对DL的增强。首先,我们简要介绍了FPGA架构的基础知识以及其组件如何导致DL应用的优势和劣势。接下来,我们讨论在fpga上实现的DL推理加速器的不同设计风格,这些设计风格实现了最先进的性能和高效的开发流程,范围从特定于模型的数据流风格到软件可编程的覆盖风格。我们研究了对传统FPGA构建块的dl特定增强,包括逻辑块(LBs)、算术电路和片上存储器,以及集成到FPGA结构中以加速张量计算的新的dl专用块。最后,我们讨论了将处理器和粗粒度加速器块与类似fpga的互连和片上网络(noc)相结合的混合器件,并强调了未来有希望的研究方向。
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引用次数: 0
TechRxiv TechRxiv
IF 20.6 1区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-10-17 DOI: 10.1109/jproc.2025.3616531
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引用次数: 0
Future Special Issues/Special Sections of the Proceedings 未来的特刊/会议记录的特别部分
IF 20.6 1区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-10-17 DOI: 10.1109/jproc.2025.3610964
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引用次数: 0
Proceedings of the IEEE Publication Information IEEE出版信息学报
IF 25.9 1区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-10-17 DOI: 10.1109/JPROC.2025.3610940
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引用次数: 0
IEEE Foundation IEEE基金会
IF 25.9 1区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-10-17 DOI: 10.1109/JPROC.2025.3616533
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引用次数: 0
Scanning the Issue 扫描问题
IF 25.9 1区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-10-17 DOI: 10.1109/JPROC.2025.3614274
Summary form only: Abstracts of articles presented in this issue of the publication.
仅以摘要形式提供:本刊发表的文章摘要。
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引用次数: 0
IEEE Connects You to a Universe of Information IEEE将你连接到信息的宇宙
IF 25.9 1区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-10-17 DOI: 10.1109/JPROC.2025.3616529
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引用次数: 0
Proceedings of the IEEE: Stay Informed. Become Inspired. IEEE会刊:保持信息灵通。成为灵感。
IF 25.9 1区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-10-17 DOI: 10.1109/JPROC.2025.3610968
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引用次数: 0
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