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Transfer learning approach to modeling multichannel gate-all-around nanosheet field-effect transistors under work function fluctuations

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Engineering Applications of Artificial Intelligence 2025年 148卷

作者： Dash, Sagarika Li, Yiming Parallel and Scientific Computing Laboratory National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Communications Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Biomedical Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Artificial Intelligence Innovation National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Department of Microelectronics National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan

Deep learning (DL) has significantly advanced various industries, including semiconductors, by providing sophisticated methods for analyzing emerging device data. Transfer learning (TL), a prominent DL topology, leverages knowledge from a pre-trained model to improve the performance of a specific target task. This study aims to apply TL to predict the impact of work function fluctuation (WKF) on the variability of threshold voltage (σVTH) in gate-all-around silicon nanosheet field-effect transistors (GAA Si NS FETs). We used TL to transfer knowledge from models trained on 1-channel (1ch) GAA Si NS FETs to more complex 4-channel (4ch) counterparts. The prediction accuracy is evaluated by using the mean square error(MSE), root mean square error (RMSE), and the mean absolute error(MAE) metrics. Our TL approach achieved an error rate of less than 1%, demonstrating high accuracy in predicting WKF effects on σ VTH of 4ch GAA Si NS FETs using only 500 samples. This study presents a pioneering application of TL using a one-dimensional (1D) convolutional neural network (CNN) long short-term memory (LSTM) topology in semiconductor device technology. The results effectively address the challenge of predicting σ VTH variations in advanced GAA Si NS FETs because of WKF, which eliminates the need for extensive semiconductor device simulations. © 2025 Elsevier Ltd

关键词： Convolutional neural networks

Design of Morlet wavelet neural networks integrated with sequential quadratic programming to analyze the dynamics of Ebola virus disease

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Results in Engineering 2025年 26卷

作者： Asifa Ashraf Abaid Ur Rehman Virk Umar Ishtiaq Ahmed Samy Mohamed Elwahsh Ioan-Lucian Popa Department of Mathematics University of Management and Technology Lahore Pakistan Office of Research Innovation and Commercialization University of Management and Technology Lahore 54770 Pakistan Deanship of Scientific Research King Saud University P.O. Box 2460 Riyadh 11451 Saudi Arabia Department of Computing Mathematics and Electronics “1 Decembrie 1918” University of Alba Iulia 510009 Alba Iulia Romania Faculty of Mathematics and Computer Science Transilvania University of Brasov Iuliu Maniu Street 50 500091 Brasov Romania

Ebola virus disease is caused by a virus that originates in flying foxes, or fruit bats. These bats may spread the virus to humans. The proposed model partitions the infected population into unaware and aware, and has a hospitalized compartment. Unlike previous models, which focused mostly on the general population, our model provides a more in-depth study of public health measures. Artificial neural networks (ANNs) are a highly effective approach for modeling intricate systems due to their ability to accurately approximate nonlinear functions. Their versatility and adaptability make them well-suited for addressing complex challenges in a variety of domains, such as epidemiology. The study aims to investigate the design of an integrated intelligent computing strategy in the framework of Morlet wavelet neural networks (MWNNs) for predicting solutions for an Ebola virus disease (EVD) model. For the investigation of this model, we use a hybrid optimization algorithm with MWNNs and a genetic algorithm (GA) integrated with sequential quadratic programming (SQP). The effectiveness of the developed MWNN-GA-SQP framework is thoroughly evaluated using a variety of error metrics such as mean absolute error (MAE), mean square error (MSE), and Theil’s inequality coefficient (TIC) to ensure a detailed and multidimensional performance assessment. A comparative evaluation with the conventional RK-4 numerical method as a reference solution is conducted with results illustrated through a variety of graphical tools, including histograms, box plots, and loss curves. The MWNN-GA-SQP framework's accuracy, consistency, and resilience are confirmed by thorough validation and verification against reference solutions, demonstrating its effectiveness in capturing the complex dynamics of epidemiological systems. Furthermore, the statistical analyses affirm the model's capability to precisely describe the nonlinear EVD model using an exponential incidence function, highlighting its potential a

关键词： Morlet wavelet neural network Sequential quadratic programming Genetic algorithm Ebola virus disease Statistical analysis

Eagle+: A fast incremental approach to automaton and table online updates for cloud services

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Future Generation computer Systems 2018年 80卷 275-285页

作者： Li, Jianxin Peng, Hao Yang, Erica Hu, Chunming Zhong, Shenghai Wang, Lihong School of Computer Science & Engineering Beihang University Beijing China Scientific Computing Department STFC Rutherford Appleton Laboratory Oxfordshire United Kingdom National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China

Automaton or table-based multi-pattern matching methods have been widely used in cloud services, i.e., virtual Firewall service, virtual IDS service, etc. In cloud, a large scale of patterns in such services are frequently updated causing by users’ joining or quitting and adjustment of security and management policies. Therefore, how to quickly and accurately update the Automaton and Table becomes an important issue. In this paper, we propose Eagle+, an incremental approach for updating the matching Automaton and Table whilst avoiding recalculating the whole patterns after each change. In Eagle+, we attain efficiency by computing only the latest update set of patterns when updating the Automaton and Table. Moreover, Eagle+ achieves accurately local updating based on three atomic operations, adding, updating and deleting, each of which modifies values on classical Aho–Corasick (AC) automaton, Set Backward Oracle Matching (SBOM) automaton and Wu–Manber (WM) table. Compared with existing pattern updating methods, Eagle+ reduces the computation complexity from O(n2) to O(n). The experimental results show that Eagle+ can save nearly 72%–92% of the time consumption in updating automatons and perform 100X faster in WM table. © 2017 Elsevier B.V.

关键词： Web services

GPU algorithms for efficient exascale discretizations

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arXiv 2021年

作者： Abdelfattah, Ahmad Barra, Valeria Beams, Natalie Bleile, Ryan Brown, Jed Camier, Jean-Sylvain Carson, Robert Chalmers, Noel Dobrev, Veselin Dudouit, Yohann Fischer, Paul Karakus, Ali Kerkemeier, Stefan Kolev, Tzanio Lan, Yu-Hsiang Merzari, Elia Min, Misun Phillips, Malachi Rathnayake, Thilina Rieben, Robert Stitt, Thomas Tomboulides, Ananias Tomov, Stanimire Tomov, Vladimir Vargas, Arturo Warburton, Tim Weiss, Kenneth Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA94550 United States Mathematics and Computer Science Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Innovative Computing Laboratory University of Tennessee KnoxvilleTN37996 United States Department of Computer Science University of Colorado BoulderCO80309 United States Department of Mathematics Virginia Tech BlacksburgVA24061 United States AMD Research Advanced Micro Devices Inc. AustinTX78735 United States Weapons and Complex Integration Lawrence Livermore National Laboratory LivermoreCA94550 United States Computational Engineering Division Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Nuclear Engineering Penn State PA16802 United States Department of Mechanical Engineering Aristotle University of Thessaloniki 54124 Greece Mechanical Engineering Department Middle East Technical University Ankara06800 Turkey

In this paper we describe the research and development activities in the Center for Efficient Exascale Dis-cretization within the US Exascale computing Project, targeting state-of-the-art high-order finite-element algorithms for high-order applications on GPU-accelerated platforms. We discuss the GPU developments in several components of the CEED software stack, including the libCEED, MAGMA, MFEM, libParanumal, and Nek projects. We report performance and capability improvements in several CEED-enabled applications on both NVIDIA and AMD GPU systems. © 2021, CC BY-NC-ND.

关键词： Finite element method

Eigenvalues and eigenvectors of tau matrices with applications to markov processes and economics

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arXiv 2020年

作者： Ekström, Sven-Erik Garoni, Carlo Jozefiak, Adam Perla, Jesse Department of Information Technology Division of Scientific Computing Uppsala University Sweden Faculty of Mathematics and Natural Sciences Bergische Universität Wuppertal Germany Department of Mathematics University of Rome Tor Vergata Italy Department of Computer Science University of British Columbia Canada Vancouver School of Economics University of British Columbia Canada

In the context of matrix displacement decomposition, Bozzo and Di Fiore introduced the so-called τΕ, algebra, a generalization of the more known τ algebra originally proposed by Bini and Capovani. We study the properties of eigenvalues and eigenvectors of the generator Tn,Ε, of the τΕ, algebra. In particular, we derive the asymptotics for the outliers of Tn,Ε, and the associated eigenvectors;we obtain equations for the eigenvalues of Tn,Ε,, which provide also the eigenvectors of Tn,Ε,;and we compute the full eigendecomposition of Tn,Ε, in the specific case Ε = 1. We also present applications of our results in the context of queuing models, random walks, and diffusion processes, with a special attention to their implications in the study of wealth/income inequality and portfolio *** Codes 15A18, 15B05, 60K25, 60G50, 60J60, 91G10 Copyright © 2020, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

Quantum-inspired classical sublinear-time algorithm for solving low-rank semidefinite programming via sampling approaches

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arXiv 2019年

作者： Chia, Nai-Hui Li, Tongyang Lin, Han-Hsuan Wang, Chunhao Department of Computer Science University of Texas Austin United States Department of Computer Science Institute for Advanced Computer Studies Joint Center for Quantum Information and Computer Science University of Maryland U.S. Department of Energy Office of Science Office of Advanced Scientific Computing Research Quantum Algorithms Teams program

Semidefinite programming (SDP) is a central topic in mathematical optimization with extensive studies on its efficient solvers. Recently, quantum algorithms with superpolynomial speedups for solving SDPs have been proposed assuming access to its constraint matrices in quantum superposition. Mutually inspired by both classical and quantum SDP solvers, in this paper we present a sublinear classical algorithm for solving low-rank SDPs which is asymptotically as good as existing quantum algorithms. Specifically, given an SDP with m constraint matrices, each of dimension n and rank poly(log n), our algorithm gives a succinct description and any entry of the solution matrix in time O(m · poly(log n, 1/Ε)) given access to a sample-based low-overhead data structure of the constraint matrices, where Ε is the precision of the solution. In addition, we apply our algorithm to a quantum state learning task as an application. Technically, our approach aligns with both the SDP solvers based on the matrix multiplicative weight (MMW) framework and the recent studies of quantum-inspired machine learning algorithms. The cost of solving SDPs by MMW mainly comes from the exponentiation of Hermitian matrices, and we propose two new technical ingredients (compared to previous sample-based algorithms) for this task that may be of independent interest: • Weighted sampling: assuming sampling access to each individual constraint matrix A1,..., Aτ, we propose a procedure that gives a good approximation of A = A1 + ··· + Aτ. • Symmetric approximation: we propose a sampling procedure that gives low-rank spectral decomposition of a Hermitian matrix A. This improves upon previous sampling procedures that only give low-rank singular value decompositions, losing the signs of eigenvalues. Copyright © 2019, The Authors. All rights reserved.

关键词： Singular value decomposition

Uplink-downlink duality between multiple-access and broadcast channels with compressing relays

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arXiv 2020年

作者： Liu, Liang Liu, Ya-Feng Patil, Pratik Yu, Wei the Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong the State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Edward S. Rogers Sr. Department of Electrical and Computer Engineering the University of Toronto the Department of Statistics and Data Science and the Machine Learning Department Carnegie Mellon University PittsburghPA15213 United States The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto 10 King’s College Road TorontoONM5S3G4 Canada

—Uplink-downlink duality refers to the fact that under a sum-power constraint, the capacity regions of a Gaussian multiple-access channel and a Gaussian broadcast channel with Hermitian transposed channel matrices are identical. This paper generalizes this result to a cooperative cellular network, in which remote access-points are deployed as relays in serving the users under the coordination of a central processor (CP). In this model, the users and the relays are connected over noisy wireless links, while the relays and the CP are connected over noiseless but rate-limited fronthaul links. Based on a Lagrangian technique, this paper establishes a duality relationship between such a multiple-access relay channel and broadcast relay channel, under the assumption that the relays use compression-based strategies. Specifically, we show that under the same total transmit power constraint and individual fronthaul rate constraints, the achievable rate regions of the Gaussian multiple-access and broadcast relay channels are identical, when either independent compression or Wyner-Ziv and multivariate compression strategies are used. The key observations are that if the beamforming vectors at the relays are fixed, the sum-power minimization problems under the achievable rate and fronthaul constraints in both the uplink and the downlink can be transformed into either a linear programming or a semidefinite programming problem depending on the compression technique, and that the uplink and downlink problems are Lagrangian duals of each other. Moreover, the dual variables corresponding to the downlink rate constraints become the uplink powers;the dual variables corresponding to the downlink fronthaul constraints become the uplink quantization noises. This duality relationship enables an efficient algorithm for optimizing the downlink transmission and relaying strategies based on the uplink. Copyright © 2020, The Authors. All rights reserved.

关键词： Linear programming

Optimal provable robustness of quantum classification via quantum hypothesis testing

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arXiv 2020年

作者： Weber, Maurice Liu, Nana Li, Bo Zhang, Ce Zhao, Zhikuan Department of Computer Science ETH Zürich Universitätstrasse 6 Zürich8092 Switzerland Institute of Natural Sciences Shanghai Jiao Tong University Shanghai200240 China Ministry of Education Key Laboratory in Scientific and Engineering Computing Shanghai Jiao Tong University Shanghai200240 China University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai200240 China Department of Computer Science University of Illinois UrbanaIL61801 United States

Quantum machine learning models have the potential to offer speedups and better predictive accuracy compared to their classical counterparts. However, these quantum algorithms, like their classical counterparts, have been shown to also be vulnerable to input perturbations, in particular for classification problems. These can arise either from noisy implementations or, as a worst-case type of noise, adversarial attacks. In order to develop defence mechanisms and to better understand the reliability of these algorithms, it is crucial to understand their robustness properties in presence of natural noise sources or adversarial manipulation. From the observation that measurements involved in quantum classification algorithms are naturally probabilistic, we uncover and formalize a fundamental link between binary quantum hypothesis testing and provably robust quantum classification. This link leads to a tight robustness condition which puts constraints on the amount of noise a classifier can tolerate, independent of whether the noise source is natural or adversarial. Based on this result, we develop practical protocols to optimally certify robustness. Finally, since this is a robustness condition against worst-case types of noise, our result naturally extends to scenarios where the noise source is known. Thus, we also provide a framework to study the reliability of quantum classification protocols beyond the adversarial, worst-case noise scenarios. Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum theory

Intrusion Detection Systems using Data Mining Techniques: A comparative study

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Intrusion Detection Systems using Data Mining Techniques: A ...

Information Systems and Advanced Technologies (ICISAT), International Conference on

作者： Mohamed Haddadi Abdelhamid Khiat Nacera Bahnes Faculté des sciences économiques commerciales et des sciences de gestion Université M’hamed Bougara de Boumerdes Avenue de l’indépendence Boumerdes Algérie Networks and Distributed Systems Division Research Center on Scientific and Technical Information Algeria Industrial Computing and Networks Laboratory (RIIR) University of Oran 1 Ahmed Ben Bella PO Box 1524 El M’naouar Oran Algeria Faculty of Exact Sciences and Computer Science University of Abdelhamid Ibn Badis Mostaganem Algeria

ISBN: (纸本)9781665478250

Data mining tools are widely used in computer networks. The well-known and mostly used tools to secure computers and network systems are WEKA and TANAGRA. The purpose of this study is to compare these two tools in terms of detection accuracy and computation time. This comparison was conducted using a well-known NSL-KDD dataset. Experiments show that TANAGRA achieves better results than WEKA in detection accuracy. But, TANAGRA is competitive with WEKA in terms of computation time.

关键词： Training Java Codes computer hacking Intrusion detection computer networks Data mining