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IEEE Open Journal of Nanotechnology 2023年 4卷 181-194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 National Yang Ming Chiao Tung University Institute of Communications Engineering the Institute of Biomedical Engineering the Department of Electronics and Electrical Engineering the Institute of Pioneer Semiconductor Innovation and the Institute of Artificial Intelligence Innovation Hsinchu300093 Taiwan

Machine learning (ML) is poised to play an important part in advancing the predicting capability in semiconductor device compact modeling domain. One major advantage of ML-based compact modeling is its ability to capture complex relationships and patterns in large datasets. Therefore, in this paper a novel design scheme based on dynamically adaptive neural network (DANN) is proposed to develop fast and accurate compact model (CM). This framework constitutes a powerful yet computationally efficient methodology and exhibits emergent dynamic behaviors. This paper demonstrates that the compact model based on ML can be designed to replicate the performance of conventional compact model for nanodevices. For this work, gate-all-around (GAA) nanosheet (NS) device characteristics are comprehensively analyzed for process variability sources using the proposed model. The device geometry parameters such as channel length, nanosheet width and nanosheet thickness are fed as input features to the DANN model. The adaptive neural network learns dynamically by updating weights of the model in accordance with the input features and achieves accurate neural weight convergence. The proposed model predicted the electrical characteristics of NS devices with less than 1% error rate. The model is also implemented and validated for the simulations of digital circuit designs such as inverter, and logic gates. ©;2023 The Authors.

关键词： Circuit simulation

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Automated Pricing-based Provisioning of SDN/NFV Services in Distributed Multi-access Edge Computing using Cooperative Multi-Agent Deep Reinforcement Learning 10

Automated Pricing-based Provisioning of SDN/NFV Services in ...

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10th International Conference on Dependable Systems and Their Applications, DSA 2023

作者： Julien, Jean Jimmy Nuannimnoi, Sirapop Huang, Ching-Yao National Yang-Ming Chiao Tung University Eecs International Graduate Program Hsinchu Taiwan National Yang-Ming Chiao Tung University Electronics Engineering Hsinchu Taiwan

ISBN: (纸本)9798350304770

The disruption caused by Software Defined Network and Network Function Virtualization (SDN/NFV) technologies will have many impacts on the telecom network. Specifically, the network architecture based on ETSI MANO comprising Virtual Infrastructure Manager (VIM), Virtual Network Function Manager (VNFM), and NFV Orchestrator (NFVO) will significantly change how we operate and manage the telecom network. These impacts on the network architecture will be made gradually. Thus, the migration from non-virtualized networks to all virtualized networks will happen step by step. By introducing new actors into the telecom ecosystem, NFV-MANO will bring in new business models. It is envisaged that these new actors/models will promote competition hence the demand for more flexible charging models with real-time charging. In this research, we will address the architectural realization of the SDN/NFV charging model under the business model of MANO (MANagement and Orchestration) when the service provider (SP) uses the Network Function Virtualization Infrastructure (NFVI) from the NFVIaaS Provider in a distributed multi-access edge computing (MEC) environment. To optimize the Quality of Service (QoS) of MEC resource allocation for incoming services and maximize the overall operating profit of the service provider adopting our charging model, we also propose a new cooperative multi-agent actor-critic based deep reinforcement learning (MADRL) method trained with proximal policy optimization algorithm, namely Coop MAPPO. The results of our experiments showcase the superiority of the Coop-MAPPO multi-agent system over alternative decision-making approaches, with its potential for enhancing operational efficiency and profitability while minimizing failure rates. © 2023 IEEE.

关键词： Reinforcement learning

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Overview of Versatile Video Coding (H.266/VVC) and Its Coding Performance Analysis

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IEIE Transactions on Smart Processing and Computing 2023年第2期12卷 122-154页

作者： Lee, Minhun Song, HyeonJu Park, Jeeyoon Jeon, Byeungwoo Kang, Jungwon Kim, Jae-Gon Lee, Yung-Lyul Kang, Je-Won Sim, Donggyu Department of Computer Engineering Kwangwoon University Seoul Korea Republic of Department of Computer Engineering Sejong University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Kyunggi-do Korea Republic of Communication and Media Research Laboratory Electronics and Telecommunications Research Institute Deajeon Korea Republic of School of Electronics and Information Engineering Korea Aerospace University Kyunggi-do Korea Republic of Department of Electronic and Electrical Engineering Graduate Program in Smart Factory Ewha Womans University Seoul Korea Republic of

Versatile Video Coding (H.266/VVC) is the newest video coding standard jointly developed by the Joint Video Experts Team (JVET), which is organized by the ITU-T Video Coding Experts Group (VCEG) and the ISO/IEC Moving Picture Experts Group (MPEG). H.266/VVC provides about 40% bitrate reduction compared with High Efficiency Video Coding (H.265/HEVC) for the same visual quality. This paper introduces in detail the core structure of H.266/VVC by highlighting its features within block partitioning structure, intra/inter prediction, transform, quantization, and in-loop filtering, compared to its predecessor (H.265/HEVC). H.266/VVC yields significantly improved the coding performance, but it increased the computational complexity, particularly for the encoder side, which remains a problem to be tackled for successful commercialization in the future. This paper examined the statistical performance of H.266/VVC coding tools from the bitstreams encoded by the VVC test model (VTM12.0) encoder through rate-distortion optimization under the JVET common test conditions. In addition, the complexity and performance analyses are conducted on the block partitioning structure and group of picture structure. It is expected that an optimized H.266/VVC encoder can be designed and developed by minimizing the coding loss based on the analysis data. © 2023 Institute of electronics and Information Engineers. All rights reserved.

关键词： Image compression

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A methodology for coffee price forecasting based on extreme learning machines

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Information Processing in Agriculture 2022年第4期9卷 556-565页

作者： Carolina Deina Matheus Henrique do Amaral Prates Carlos Henrique Rodrigues Alves Marcella Scoczynski Ribeiro Martins Flavio Trojan Sergio Luiz Stevan Jr Hugo Valadares Siqueira Department of Production Engineering Federal University of Rio Grande do Sul90.035-190Brazil Department of Electronics Federal University of TechnologyParana 84017-220Brazil Graduate Program in Production Engineering Federal University of TechnologyParana 84017-220Brazil

This work introduces a methodology to estimate coffee prices based on the use of Extreme Learning *** process is initiated by identifying the presence of nonstationary components,like seasonality and *** components are withdrawn if they are ***,the temporal lags are selected based on the response of the Partial Autocorre-lation Function *** predictors,we address the following models:Exponential Smooth-ing(ES),Autoregressive(AR)and Autoregressive Integrated and Moving Average(ARIMA)models,Multilayer Perceptron(MLP)and Extreme Learning Machines(ELMs)neural *** computational results based on three error metrics and two coffee types(Ara-bica and Robusta)showed that the neural networks,especially the ELM,can reach higher performance levels than the other *** methodology,which presents preprocess-ing stages,lag selection,and use of ELM,is a novelty that contributes to the coffee prices forecasting field.

关键词： Coffee price forecasting Linear models Artificial neural networks Computational intelligence

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A 22nm CMOS PFM Buck Converter Used in a 32-Bit RISC-V Microcontroller for IoT Applications 16

A 22nm CMOS PFM Buck Converter Used in a 32-Bit RISC-V Micro...

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16th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2025

作者： Oliveira, Luiz H. N. Amaral, Wellington A. Haddad, Sandro A. P. Munoz, Daniel M. Barcelos, Eduardo A. Sotero, Vinicius V. M. Jonhson, Emmanuel K. M. Campagnucci, Pamela R. A. N. Lima, Luis V. N. Electronics Engineering Undergraduate Program Faculty of Gama Brazil Mechatronics Graduate Program University of Brasilia Faculty of Technology Department of Mechanical Engineering DF Brasilia Brazil

ISBN: (纸本)9798331522124

The demand for efficient power management solutions in electronic devices has increased significantly, especially for microcontrollers operating in low-power modes. This article presents the design and implementation of a Pulse Frequency Modulation (PFM)-based buck converter for applications in 32-bit microcontrollers using 22nm CMOS technology. The methodology involves the development of a Constant On Time (COT) architecture that maintains a fixed duration for the activation of the high-side MOSFET, using a monostable multivibrator to generate the necessary timing signals. The converter's performance was simulated under various load conditions and process corners to evaluate its efficiency and output characteristics. The results show that the proposed buck converter achieves high efficiency, with peak values above 95%, while maintaining a low output ripple. Specifically, the converter exhibits an efficiency of 93.4% at a load of 1mA, with output ripple voltages as low as 2.20 mV under ideal conditions. The contributions of this work lie in the advancement of PFM converter technology for modern microcontroller applications, demonstrating the potential of 22nm CMOS processes to improve power efficiency and performance. © 2025 IEEE.

关键词： CMOS integrated circuits

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Utilizing Mobile Robots to Teach Dengue and Chikungunya Prevention in Brazilian Schools

Utilizing Mobile Robots to Teach Dengue and Chikungunya Prev...

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2024 Brazilian Symposium on Robotics (SBR), and 2024 Workshop on Robotics in Education (WRE)

作者： R. B. Costa Pedro H. L. Gomes Luiz F. Rodriguez Silva M. A. Pastrana Maria C. C. Gontijo M. S. Luiz Daniel M. Muñoz Faculty of Gama Aerospace Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil Faculty of Gama Electronics Engineering Undergraduate Program Department of Mechanical Engineering Mechatronics Graduate Program

ISBN: (数字)9798350391084

ISBN: (纸本)9798350391091

This paper introduces a novel approach to combating the spread of Dengue and Chikungunya, two prevalent arboviruses in tropical regions, especially in Brazil. The research presents a low-cost differential topology robot designed as an educational tool to raise awareness about mosquito breeding grounds among students and teachers in public schools across the Federal District, Brazil. Developed at the University of Brasilia, the robot is deployed in collaboration with the Federal District Health Department to support prevention campaigns in schools. The paper evaluates the effectiveness of this innovative tool in enhancing community engagement and improving health outcomes. The findings suggest that integrating robotics into health education can significantly elevate awareness and strengthen prevention strategies against arboviral diseases.

关键词： Pediatrics Prevention and mitigation Education Collaboration Robot sensing systems Topology Public healthcare Robots Viruses (medical) Diseases

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EfficientSpeech: An On-Device Text to Speech Model

EfficientSpeech: An On-Device Text to Speech Model

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Rowel Atienza Electrical and Electronics Engineering Institute and AI Graduate Program University of the Philippines

State of the art (SOTA) neural text to speech (TTS) models can generate natural-sounding synthetic voices. These models are characterized by large memory footprints and substantial number of operations due to the long-standing focus on speech quality with cloud inference in mind. Neural TTS models are generally not designed to perform standalone speech syntheses on resource-constrained and no Internet access edge devices. In this work, an efficient neural TTS called EfficientSpeech that synthesizes speech on an ARM CPU in real-time is proposed. EfficientSpeech uses a shallow non-autoregressive pyramid-structure transformer forming a U-Network. EfficientSpeech has 266k parameters and consumes 90 MFLOPS only or about 1% of the size and amount of computation in modern compact models such as Mixer-TTS. EfficientSpeech achieves an average mel generation real-time factor of 104.3 on an RPi4. Human evaluation shows only a slight degradation in audio quality as compared to FastSpeech2.

关键词： Performance evaluation Degradation Computational modeling Signal processing Transformers Real-time systems Computational efficiency

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HyperFed: Free-riding Resistant Federated Learning with Performance-based Reputation Mechanism and Adaptive Aggregation using Hypernetworks 10

HyperFed: Free-riding Resistant Federated Learning with Perf...

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10th International Conference on Dependable Systems and Their Applications, DSA 2023

作者： Nuannimnoi, Sirapop Delizy, Florian Huang, Ching-Yao National Yang-Ming Chiao Tung University Eecs International Graduate Program Hsinchu Taiwan National Yang-Ming Chiao Tung University Department of Electronics Engineering Hsinchu Taiwan

ISBN: (纸本)9798350304770

Traditional machine learning solutions rely on Cloud based services, which could potentially lead to major problems including security, privacy data leakage, unacceptable latency, and excessive operating expenses. Federated Learning techniques (FL) were introduced to tackle these challenges by allowing distributed edge nodes/servers to collaboratively train AI models without sharing raw training data. However, some of the nodes may intentionally or unintentionally upload virtual (fake) models to the main server. This behavior is called "Free-riding", and it could potentially have a negative effect on the overall performance of the FL system. In this paper, we propose a new adaptive contribution-based aggregation technique using hypernetworks, namely "HyperFed", and evaluate it on two important aspects: resistance against free-riders' fake contributions, and average convergence speed of global model on local datasets. Our simulation results on Federated EMNIST dataset display promising performance in comparison to FedAvg and AdaFed aggregation techniques. © 2023 IEEE.

关键词： Edge computing

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A Physical-Based Artificial Neural Networks Compact Modeling Framework for Emerging FETs

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IEEE Transactions on Electron Devices 2024年第1期71卷 223-230页

作者： Yang, Ya-Shu Li, Yiming Kola, Sekhar Reddy Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory The Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Communications Engineering Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan

We report a compact modeling framework based on the Grove-Frohman (GF) model and artificial neural networks (ANNs) for emerging gate-all-around (GAA) MOSFETs. The framework consists of two ANNs;the first ANN constructed with the drain current model not only can capture the main trend of device I - V characteristics but also can predict its variation even when the amount of training data for the ANN is insufficient or outside the range of applied biases. The second one is then designed to improve the model accuracy by further minimizing the errors between the target and the model outputs. We implement the proposed framework to accurately model emerging GAA nanosheet (NS) MOSFETs and complementary FETs (CFETs) without suffering from divergent issues in circuit simulation. In addition, nonphysical behaviors, such as nonzero current at zero bias, do not occur in the modeling framework. Compared to recently reported machine-learning (ML) models, our approach can achieve a similar level of model accuracy with merely 20% amount of the training data. © 1963-2012 IEEE.

关键词： Neural networks

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An Evaluation of Energy-efficient AI Models for Edge Deployment in 12-lead ECG Classification 13

An Evaluation of Energy-efficient AI Models for Edge Deploym...

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13th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2024

作者： Baskoro, Angger Lin, Yu-Chi Nuannimnoi, Sirapop Rijal, Himmatur Traore, Gnangze Elie Gildas Azhar, Milzam Waf Huang, Ching-Yao National Yang Ming Chiao Tung Univ. Eecs International Graduate Program Hsinchu Taiwan Institute of Electronics National Yang Ming Chiao Tung Univ. Hsinchu Taiwan National Yang Ming Chiao Tung Univ. Department of Electronics Engineering Hsinchu Taiwan

ISBN: (纸本)9798350368086

In recent years, artificial intelligence (AI) techniques, particularly deep learning, have shown significant promise in modern tele-medicine. Deep learning models, particularly Convolutional Neural Networks (CNNs), have been trained on large datasets to identify patterns and anomalies within 12-lead electrocardiograms (ECGs), which are crucial for diagnosing various cardiac conditions. In this study, we conducted a comparative analysis to identify and evaluate models that can be effectively deployed in rural healthcare settings, where energy efficiency, low computational requirements, and ease of use are critical factors. We compare the performance of two well-known sequential data processing model architectures: time-series RetNet and Mamba. The best model from this comparative study achieves a notable precision of over 95% in a 10 fold cross-validation for the classification of nine types of cardiac arrhythmias (CA) in the 2018 PhysioNet/CinC Challenge (CPSC 2018) dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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