储层计算的设计策略与应用:最新趋势与前景 [特写]

IF 5.6 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Circuits and Systems Magazine Pub Date : 2024-01-01 DOI:10.1109/mcas.2023.3325496
Kang Jun Bai, Clare Thiem, Jack Lombardi, Yibin Liang, Yang Yi
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引用次数: 0

摘要

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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Design Strategies and Applications of Reservoir Computing: Recent Trends and Prospects [Feature]
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.
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来源期刊
IEEE Circuits and Systems Magazine
IEEE Circuits and Systems Magazine 工程技术-工程:电子与电气
CiteScore
9.30
自引率
1.40%
发文量
34
审稿时长
>12 weeks
期刊介绍: The IEEE Circuits and Systems Magazine covers the subject areas represented by the Society's transactions, including: analog, passive, switch capacitor, and digital filters; electronic circuits, networks, graph theory, and RF communication circuits; system theory; discrete, IC, and VLSI circuit design; multidimensional circuits and systems; large-scale systems and power networks; nonlinear circuits and systems, wavelets, filter banks, and applications; neural networks; and signal processing. Content also covers the areas represented by the Society technical committees: analog signal processing, cellular neural networks and array computing, circuits and systems for communications, computer-aided network design, digital signal processing, multimedia systems and applications, neural systems and applications, nonlinear circuits and systems, power systems and power electronics and circuits, sensors and micromaching, visual signal processing and communication, and VLSI systems and applications. Lastly, the magazine covers the interests represented by the widespread conference activity of the IEEE Circuits and Systems Society. In addition to the technical articles, the magazine also covers Society administrative activities, as for instance the meetings of the Board of Governors, Society People, as for instance the stories of award winners-fellows, medalists, and so forth, and Places reached by the Society, including readable reports from the Society's conferences around the world.
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