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Large circuit models: opportunities and challenges

Science China(Information Sciences) 2024年第10期67卷 25-66页

作者： Lei CHEN Yiqi CHEN Zhufei CHU Wenji FANG Tsung-Yi HO Ru HUANG Yu HUANG Sadaf KHAN Min LI Xingquan LI Yu LI Yun LIANG Jinwei LIU Yi LIU Yibo LIN Guojie LUO Hongyang PAN Zhengyuan SHI Guangyu SUN Dimitrios TSARAS Runsheng WANG Ziyi WANG Xinming WEI Zhiyao XIE Qiang XU Chenhao XUE Junchi YAN Jun YANG Bei YU Mingxuan YUAN Evangeline F.Y.YOUNG Xuan ZENG Haoyi ZHANG Zuodong ZHANG Yuxiang ZHAO Hui-Ling ZHEN Ziyang ZHENG Binwu ZHU Keren ZHU Sunan ZOU Huawei Noah’s Ark Lab School of Integrated Circuits Peking University Faculty of Electrical Engineering and Computer Science Ningbo University Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Department of Computer Science and Engineering The Chinese University of Hong Kong School of Integrated Circuits Southeast University Huawei HiSilicon Peng Cheng Laboratory School of Computer Science Peking University School of Microelectronics State Key Laboratory of Integrated Chips and System Fudan University School of Artificial Intelligence Shanghai Jiao Tong University

Within the electronic design automation(EDA) domain, artificial intelligence(AI)-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domains, such as vision, text, and graph analytics, applying them to circuit design without tailoring to the unique complexities of electronic circuits. Such an “AI4EDA” approach falls short of achieving a holistic design synthesis and understanding,overlooking the intricate interplay of electrical, logical, and physical facets of circuit data. This study argues for a paradigm shift from AI4EDA towards AI-rooted EDA from the ground up, integrating AI at the core of the design process. Pivotal to this vision is the development of a multimodal circuit representation learning technique, poised to provide a comprehensive understanding by harmonizing and extracting insights from varied data sources, such as functional specifications, register-transfer level(RTL) designs, circuit netlists,and physical layouts. We champion the creation of large circuit models(LCMs) that are inherently multimodal, crafted to decode and express the rich semantics and structures of circuit data, thus fostering more resilient, efficient, and inventive design methodologies. Embracing this AI-rooted philosophy, we foresee a trajectory that transcends the current innovation plateau in EDA, igniting a profound “shift-left” in electronic design methodology. The envisioned advancements herald not just an evolution of existing EDA tools but a revolution, giving rise to novel instruments of design-tools that promise to radically enhance design productivity and inaugurate a new epoch where the optimization of circuit performance, power, and area(PPA) is achieved not incrementally, but through leaps that redefine the benchmarks of electronic systems' capabilities.

关键词： AI-rooted EDA large circuit models (LCMs) multimodal circuit representation learning circuit optimization

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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Science China(Information Sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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Lattice Design for Multiple Topologically Protected Edge Modes 16

Lattice Design for Multiple Topologically Protected Edge Mod...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Kim, Gyunghun Suh, Joseph Lee, Dayeong Park, Namkyoo Yu, Sunkyu Seoul National University Intelligent Wave Systems Laboratory Department of Electrical and Computer Engineering Seoul08826 Korea Republic of Seoul National University Photonic Systems Laboratory Department of Electrical and Computer Engineering Seoul08826 Korea Republic of

We propose the lattice design that allows multiple topologically protected edge modes. The scattering between these modes, which is linear, energy preserving, and robust against local disorders, is discussed in terms ...

ISBN: (纸本)9798350372076

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Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

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IEEE Transactions on Electron Devices 2024年第1期71卷 246-253页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation The Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program The Institute of Communications Engineering the Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

In this work, a dynamic weighting-artificial neural network (DW-ANN) methodology is presented for quick and automated compact model (CM) generation. It takes advantage of both TCAD simulations for high accuracy and SPICE simulations for cost-effectiveness. This methodology is developed for gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field effect transistor (CFET), a potential candidate for future CMOS technology due to its innate properties. The critical process variation (PV) sources that severely degrade the CFET performance are estimated using DW-ANN. It reduces the computation cost and predicts the effects of PV sources with less than 1% error. Furthermore, a compact DW-ANN-based Verilog-A model has been developed that captures the dc as well as transient behavior accurately for circuit-level analysis. CFET-based circuits such as inverter, 6T-static random access memory (SRAM), and ring oscillator (RO) are characterized and implemented using DW-ANN-based CM. The overall average error of the model is reported as less than 2%. Therefore, the proposed device and circuit modeling approach provides a feasible solution for the rapid compact modeling of new emerging devices with good convergence and high accuracy. © 1963-2012 IEEE.

关键词： Neural networks

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Diversified and compatible web APIs recommendation based on game theory in IoT

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Digital Communications and Networks 2024年第4期10卷 1198-1209页

作者： Wenwen Gong Huiping Wu Xiaokang Wang Xuyun Zhang Yawei Wang Yifei Chen Mohammad R.Khosravi College of Information and Electrical Engineering China Agricultural UniversityBeijing100000China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyShouguang262700China Department of Computer Science St.Francis Xavier UniversityAntigonishNSCanada Department of Computing Macquarie UniversitySydneyNSW2122Australia Department of Computer Engineering Persian Gulf UniversityBushehr***Iran Department of Electrical and Electronic Engineering Shiraz University of TechnologyShiraz71557-13876Iran

With the ever-increasing popularity of Internet of Things(IoT),massive enterprises are attempting to encapsulate their developed outcomes into various lightweight Web Application Programming Interfaces(APIs)that can be accessible *** this context,finding and writing a list of existing Web APIs that can collectively meet the functional needs of software developers has become a promising approach to economically and easily develop successful mobile ***,the number and diversity of candidate IoT Web APIs places an additional burden on application developers’Web API selection decisions,as it is often a challenging task to simultaneously ensure the diversity and compatibility of the final set of Web APIs *** this challenge and latest successful applications of game theory in IoT,a Diversified and Compatible Web APIs Recommendation approach,namely DivCAR,is put forward in this *** of all,to achieve API diversity,DivCAR employs random walk sampling technique on a pre-built“API-API”correlation graph to generate diverse“API-API”correlation ***,with the diverse“API-API”correlation subgraphs,the compatible Web APIs recommendation problem is modeled as a minimum group Steiner tree search problem.A sorted set of multiple compatible and diverse Web APIs are returned to the application developer by solving the minimum group Steiner tree search *** last,a set of experiments are designed and implemented on a real dataset crawled from *** results validate the effectiveness and efficiency of our proposed DivCAR approach in balancing the Web APIs recommendation diversity and compatibility.

关键词： Internet of things Web APIs recommendation Game theory Diversity and compatibility

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Mutative Evolution for Calculating Maximally Localized Wannier Functions in Photonic Crystals

Mutative Evolution for Calculating Maximally Localized Wanni...

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2024 Conference on Lasers and Electro-Optics/Pacific Rim, CLEO-PR 2024

作者： Lee, Dayeong Lee, Gitae Park, Seungkyun Park, Hyungchul Park, Namkyoo Yu, Sunkyu Intelligent Wave Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Photonic Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of

We propose a neuroevolutionary algorithm to calculate maximally localized Wannier functions in photonic crystals, as an alternative method of conventional gradient-based approaches. © 2024 The Author(s)

关键词： Photonic crystals

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Preprocessing Taste Data for Deep Neural Networks 14

Preprocessing Taste Data for Deep Neural Networks

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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Lee, Hyunjong Jung, Han Hee Kwak, Jeongho Yea, Junwoo Choi, Jihwan P. Jang, Kyung-In Department of Electrical Engineering and Computer Science Daegu Korea Republic of Dgist Department of Robotics and Mechatronics Engineering Daegu Korea Republic of Dgist Department of Electrical Engineering and Computer Science Daegu Korea Republic of Department of Aerospace Engineering Dajeon Korea Republic of

ISBN: (纸本)9798350313277

Analyzing wine using taste data is a promising field due to the explosive expansion of online commerce. However, because of the wide variety of wine types with different flavors and aromas, it is difficult for consumers to choose the wine that suits their taste, and also difficult for sellers to recommend appropriate wines to consumers. Therefore, it is necessary to numerically analyze and classify wine, and a deep learning algorithm which mimics the human brain is appropriate for analyzing the wine data [1]. In this paper, we introduce several studies of wine classification using deep learning architectures and propose preprocessing methods for applying the taste data of wine to deep learning networks. © 2023 IEEE.

关键词： data augmentation deep learning preprocessing taste classification taste sensor wine

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Graph neural network based method for robot path planning

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Biomimetic Intelligence & robotics 2024年第1期4卷 80-87页

作者： Xingrong Diao Wenzheng Chi Jiankun Wang Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical EngineeringSouthern University of Science and TechnologyShenzhen 518055China Robotics and Microsystems Center School of Mechanical and Electric EngineeringSoochow UniversitySuzhou 215021China Jiaxing Research Institute Southern University of Science and TechnologyJiaxing 314000China

Sampling-based path planning is widely used in robotics,particularly in high-dimensional state *** the path planning process,collision detection is the most time-consuming ***,we propose a learning-based path planning method that reduces the number of collision *** develop an efficient neural network model based on graph neural *** model outputs weights for each neighbor based on the obstacle,searched path,and random geometric graph,which are used to guide the planner in avoiding *** evaluate the efficiency of the proposed path planning method through simulated random worlds and real-world *** results demonstrate that the proposed method significantly reduces the number of collision checks and improves the path planning speed in high-dimensional environments.

关键词： Graph Neural Network(GNN) Collision detection Sampling-based path planning

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Improved Retrieval of Effective Parameters in Hyperbolic Metamaterials for Oblique Incidence 16

Improved Retrieval of Effective Parameters in Hyperbolic Met...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Park, Jusung Park, Seungkyun Kim, Kyuho Yu, Sunkyu Park, Namkyoo Seoul National University Photonic Systems Laboratory Department of Electrical and Computer Engineering Seoul08826 Korea Republic of Seoul National University Intelligent Wave Systems Laboratory Department of Electrical and Computer Engineering Seoul08826 Korea Republic of

An improved retrieval method for effective permittivity and permeability of hyperbolic metamaterials at oblique incidence is presented. We revisit a branching problem at oblique incidence. © 2024 The Authors OCIS...

ISBN: (纸本)9798350372076

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Federated Multiarmed Bandits Under Byzantine Attacks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1488-1501页

作者： Saday, Artun Demirel, Ilker Yildirim, Yigit Tekin, Cem Bilkent University Department of Electrical and Electronics Engineering Ankara06800 Turkey Massachusetts Institute of Technology Computer Science & Artificial Intelligence Laboratory CambridgeMA02139 United States

Multiarmed bandits (MAB) is a sequential decision-making model in which the learner controls the trade-off between exploration and exploitation to maximize its cumulative reward. Federated multiarmed bandits (FMAB) is an emerging framework where a cohort of learners with heterogeneous local models play an MAB game and communicate their aggregated feedback to a server to learn a globally optimal arm. Two key hurdles in FMAB are communication-efficient learning and resilience to adversarial attacks. To address these issues, we study the FMAB problem in the presence of Byzantine clients who can send false model updates threatening the learning process. We analyze the sample complexity and the regret of β-optimal arm identification. We borrow tools from robust statistics and propose a median-of-means (MoM)-based online algorithm, Fed-MoM-UCB, to cope with Byzantine clients. In particular, we show that if the Byzantine clients constitute less than half of the cohort, the cumulative regret with respect to \beta-optimal arms is bounded over time with high probability, showcasing both communication efficiency and Byzantine resilience. We analyze the interplay between the algorithm parameters, a discernibility margin, regret, communication cost, and the arms’ suboptimality gaps. We demonstrate Fed-MoM-UCB's effectiveness against the baselines in the presence of Byzantine attacks via experiments. © 2020 IEEE.

关键词： Federated learning

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