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Are You Moving? 您要搬家吗?
IF 6.9 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-03-05 DOI: 10.1109/mcas.2024.3361109
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引用次数: 0
IEEE CASS Tour Manaus 2023: Sailing on the Waters of Innovation IEEE CASS 2023 年马瑙斯之旅:在创新之路上扬帆起航
IF 6.9 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-03-05 DOI: 10.1109/mcas.2024.3349730
Ricardo Reis, Fávero Guilherme Santos
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引用次数: 0
We Want to Hear From You! 我们希望收到您的来信
IF 6.9 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-03-05 DOI: 10.1109/mcas.2024.3361095
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引用次数: 0
TechRxiv: Share Your Preprint Research With the World! TechRxiv:与世界分享您的预印本研究成果!
IF 6.9 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-03-05 DOI: 10.1109/mcas.2024.3361098
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引用次数: 0
2023 Index IEEE Circuits and Systems Magazine Vol. 23 2023 索引 IEEE 电路与系统杂志第 23 卷
IF 6.9 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-01-05 DOI: 10.1109/mcas.2024.3350570
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引用次数: 0
SBCCI 2024 SBCCI 2024
IF 6.9 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-01-01 DOI: 10.1109/mcas.2023.3334440
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引用次数: 0
An Interview With Professor Jason Cong [Interview] 专访 Jason Cong 教授 [访谈]
IF 6.9 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-01-01 DOI: 10.1109/mcas.2023.3331327
Yiran Chen
In September 2023, the EiC of the IEEE Circuits and Systems Magazine, Yiran Chen, and Associate Editor Fan Chen had a great chance to interview Professor Jason Cong, a world-renowned distinguished scholar in electronic design automation (EDA) and next-generation computing. This interview comprises six questions that explore various facets of his distinguished career. We trust that our CAS readers will find this conversation enlightening and enjoyable!
2023 年 9 月,《IEEE 电路与系统》杂志的 EiC 陈怡然和副主编陈凡有幸采访了在电子设计自动化(EDA)和下一代计算领域享誉全球的杰出学者 Jason Cong 教授。本访谈包括六个问题,探讨了他杰出职业生涯的方方面面。我们相信,中科院的读者一定会从中获得启迪和乐趣!
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引用次数: 0
Takeshi Yanagisawa—A Pioneer of Analog Signal Processing [Pioneers in CAS] 柳泽武--模拟信号处理的先驱 [CAS 先驱]
IF 6.9 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-01-01 DOI: 10.1109/mcas.2023.3325592
Nobuo Fujii
Professor Takeshi Yanagisawa, who passed away in June 2022, was one of the most outstanding leaders in the field of analog signal processing. Professor Yanagisawa graduated from Tokyo Institute of Technology (TokyoTech) in 1953, and received the M.S. and Ph.D. degrees from TokyoTech in 1955 and 1958, respectively. He has greatly contributed to the development of analog circuits, especially RC active filters. Originally, stable analog filters were realized using inductors, capacitors, and resistors. In order to install filters in a monolithic integrated circuit, the inductors must be avoided because of the difficulty of micro miniaturization. This led to the development of analog filter using resistor, capacaitors, and active elements such as amplifiers.
柳泽武教授于 2022 年 6 月去世,他是模拟信号处理领域最杰出的领导人之一。柳泽教授 1953 年毕业于东京工业大学(TokyoTech),1955 年和 1958 年分别获得东京工业大学硕士和博士学位。他对模拟电路,特别是 RC 有源滤波器的发展做出了巨大贡献。最初,稳定的模拟滤波器是通过电感器、电容器和电阻器实现的。为了在单片集成电路中安装滤波器,必须避免使用电感器,因为电感器难以实现微型化。因此,人们开发了使用电阻器、电容器和放大器等有源元件的模拟滤波器。
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引用次数: 0
Design Strategies and Applications of Reservoir Computing: Recent Trends and Prospects [Feature] 储层计算的设计策略与应用:最新趋势与前景 [特写]
IF 6.9 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-01-01 DOI: 10.1109/mcas.2023.3325496
Kang Jun Bai, Clare Thiem, Jack Lombardi, Yibin Liang, Yang Yi
Reservoir computing (RC) is a neural computing paradigm especially well-suited for learning dynamical systems by leveraging an untrained reservoir layer, providing high-dimensional input encoding with fading memory property. Since only the readout weights are trained under RC, linear regression learning algorithms are sufficient, leading to significant improvements in computational complexity and energy efficiency as compared to other deep neural networks (DNNs). RC offers an alternative solution to sidestep the shortcomings of data scarcity and the vanishing gradient problem. More importantly, such a network structure is amenable to hardware implementation using a variety of devices, circuits, and systems, making RC a good candidate to replace sophisticated DNNs as a lightweight classifier at the edge for internet of things (IoT) applications. In this article, we provide an overview of recent advances in RC hardware and their applications for mobile edge intelligence. Specifically, we will demonstrate the design strategies of RC in opto-electronic configuration, fully digital system, and silicon with the mixed-signal integrated circuit approach. Moreover, we will expose a novel implementation approach using emerging materials, designing the way for RC to be used in the next-generation neuromorphic computing systems. Building upon these efficient RC models, their applicability and effectiveness against the state-of-the-art are then demonstrated through diverse machine learning benchmarks spanning the area of IoT, communication networks, and healthcare.
储层计算(RC)是一种神经计算范式,通过利用未经训练的储层,提供具有消退记忆特性的高维输入编码,特别适合学习动态系统。由于在 RC 下只训练读出权重,因此线性回归学习算法就足够了,从而与其他深度神经网络(DNN)相比,显著提高了计算复杂性和能效。RC 为避免数据稀缺和梯度消失问题提供了另一种解决方案。更重要的是,这种网络结构可以使用各种设备、电路和系统进行硬件实现,因此 RC 是替代复杂的 DNN,成为物联网(IoT)应用边缘轻量级分类器的理想候选方案。在本文中,我们将概述 RC 硬件的最新进展及其在移动边缘智能领域的应用。具体而言,我们将展示光电子配置、全数字系统和硅混合信号集成电路方法中的 RC 设计策略。此外,我们还将揭示一种使用新兴材料的新型实施方法,为将 RC 应用于下一代神经形态计算系统指明方向。在这些高效 RC 模型的基础上,我们将通过横跨物联网、通信网络和医疗保健领域的各种机器学习基准来展示这些模型的适用性和有效性。
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引用次数: 0
IEEE Circuits and Systems Magazine Publication Information IEEE 电路与系统杂志出版信息
IF 6.9 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-01-01 DOI: 10.1109/mcas.2023.3332579
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引用次数: 0
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IEEE Circuits and Systems Magazine
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