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31st IEEE International Symposium on High Performance computer Architecture, HPCA 2025

作者： Chen, Yuzong Abouelhamayed, Ahmed F. Dai, Xilai Wang, Yang Andronic, Marta Constantinides, George A. Abdelfattah, Mohamed S. Cornell University Computer Systems Lab United States Systems and Networking Research Group Microsoft Research United States Imperial College London Department of Electrical and Electronic Engineering United Kingdom

ISBN: (纸本)9798331506476

Large language models (LLMs) have demonstrated remarkable performance across various machine learning tasks. Yet the substantial memory footprint of LLMs significantly hinders their deployment. In this paper, we improve the accessibility of LLMs through BitMoD1, an algorithm-hardware co-design solution that enables efficient LLM acceleration at low weight precision. On the algorithm side, BitMoD introduces fine-grained data type adaptation that uses a different numerical data type to quantize a group of (e.g., 128) weights. Through the careful design of these new data types, BitMoD is able to quantize LLM weights to very low precision (e.g., 4 bits and 3 bits) while maintaining high accuracy. On the hardware side, BitMoD employs a bitserial processing element to easily support multiple numerical precisions and data types;our hardware design includes two key innovations: First, it employs a unified representation to process different weight data types, thus reducing the hardware cost. Second, it adopts a bit-serial dequantization unit to rescale the per-group partial sum with minimal hardware overhead. Our evaluation on six representative LLMs demonstrates that BitMoD significantly outperforms state-of-the-art LLM quantization and acceleration methods. For discriminative tasks, BitMoD can quantize LLM weights to 4 -bit with © 2025 IEEE.

关键词： Integrated circuit design

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Connection Between Damping Torque Analysis and Energy Flow Analysis in Damping Performance Evaluation for Electromechanical Oscillations in Power systems

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Journal of Modern Power systems and Clean Energy 2022年第1期10卷 19-28页

作者： Yong Hu Siqi Bu Xin Zhang Chi Yung Chung Hui Cai Department of Electrical Engineering and Research Institute for Smart Energy The Hong Kong Polytechnic UniversityHong KongChina Shenzhen Research Institute The Hong Kong Polytechnic UniversityShenzhenChina Centre for Energy Systems and Strategy Cranfield UniversityCranfieldUK Department of Electrical and Computer Engineering University of SaskatchewanSaskatoonCanada State Grid Jiangsu Economic Research Institute NanjingChina

The damping performance evaluation for electromechanical oscillations in power systems is crucial for the stable operation of modern power *** this paper,the connection between two commonly-used damping performance evaluation methods,i.e.,the damping torque analysis(DTA)and energy flow analysis(EFA),are systematically examined and revealed for the better understanding of the oscillatory damping ***,a concept of the aggregated damping torque coefficient is proposed and derived based on DTA of multi-machine power systems,which can characterize the integration effect of the damping contribution from the whole power ***,the pre-processing of measurements at the terminal of a local generator is conducted for EFA,and a concept of the frequency-decomposed energy attenuation coefficient is defined to screen the damping contribution with respect to the interested *** this basis,the frequency spectrum analysis of the energy attenuation coefficient is employed to rigorously prove that the results of DTA and EFA are essentially equivalent,which is valid for arbitrary types of synchronous generator models in multi-machine power ***,the consistency between the aggregated damping torque coefficient and frequency-decomposed energy attenuation coefficient is further verified by the numerical calculation in case *** relationship between the proposed coefficients and the eigenvalue(or damping ratio)is finally revealed,which consolidates the application of the proposed concepts in the damping performance evaluation.

关键词： Electromechanical oscillation damping performance damping torque analysis(DTA) energy flow analysis(EFA) frequency spectrum

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Metasurface- and PCSEL-Based Structured Light for Monocular Depth Perception and Facial Recognition

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Nano Letters 2024年第5期24卷 1808-1815页

作者： Hsu, Wen-Cheng Chang, Chia-Hsun Hong, Yu-Heng Kuo, Hao-Chung Huang, Yao-Wei Department of Photonics College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Semiconductor Research Center Hon Hai Research Institute Taipei11492 Taiwan

The novel depth-sensing system presented here revolutionizes structured light (SL) technology by employing metasurfaces and photonic crystal surface-emitting lasers (PCSELs) for efficient facial recognition in monocular depth-sensing. Unlike conventional dot projectors relying on diffractive optical elements (DOEs) and collimators, our system projects approximately 45,700 infrared dots from a compact 297-μm-dimention metasurface, drastically more spots (1.43 times) and smaller (233 times) than the DOE-based dot projector in an iPhone. With a measured field-of-view (FOV) of 158° and a 0.611° dot sampling angle, the system is lens-free and lightweight and boasts lower power consumption than vertical-cavity surface-emitting laser (VCSEL) arrays, resulting in a 5-10 times reduction in power. Utilizing a GaAs-based metasurface and a simplified optical architecture, this innovation not only addresses the drawbacks of traditional SL depth-sensing but also opens avenues for compact integration into wearable devices, offering remarkable advantages in size, power efficiency, and potential for widespread adoption. © 2024 American Chemical Society.

关键词： Surface emitting lasers

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Generalized Two-Point Visual Control Model of Human Steering for Accurate State Estimation1

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ASME Letters in Dynamic systems and Control 2025年第1期5卷 011004页

作者： Mai, Rene E. Sears, Katherine Roessling, Grace Julius, Agung Mishra, Sandipan Department of Mechanical Aerospace and Nuclear Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Department of Cognitive Science Rensselaer Polytechnic Institute TroyNY12180 United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

We derive and validate a generalization of the two-point visual control model, an accepted cognitive science model for human steering behavior. The generalized model is needed as current steering models are either insufficiently accurate or too complex for online state estimation. We demonstrate that the generalized model replicates specific human steering behavior with high precision (85% reduction in modeling error) and integrate this model into a human-as-advisor framework where human steering inputs are used for state estimation. As a benchmark study, we use this framework to decipher ambiguous lane markings represented by biased lateral position measurements. We demonstrate that, with the generalized model, the state estimator can accurately estimate the true vehicle state, providing lateral state estimates with under 0.15 m error across participants. However, without the generalized model, the estimator cannot accurately estimate the vehicle's lateral state. © 2024 by ASME.

关键词： Man machine systems

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Leveraging UAV-assisted communications to improve secrecy for URLLC in 6G systems

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Digital Communications and Networks 2023年第6期9卷 1458-1464页

作者： Hemant Kumar Narsani Ali Ranjha Kapal Dev Fida Hussain Memon Nawab Muhammad Faseeh Qureshi Faculty of Engineering and Technology University of SindhPakistan Department of Electrical Engineering Ecole de Technologie Superieure Montreal QCCanada Department of Institute of Intelligent Systems University of JohannesburgSouth Africa Department of Electrical Engineering Sukkur IBA UniversityPakistan Department of Computer Education Sungkyunkwan UniversitySeoulRepublic of Korea

Unmanned Aerial Vehicles(UAVs)will be essential to support mission-critical applications of Ultra Reliable Low Latency Communication(URLLC)in futuristic Sixth-Generation(6G)***,several security vulnerabilities and attacks have plagued previous generations of communication systems;thus,physical layer security,especially against eavesdroppers,is vital,especially for upcoming 6G *** this regard,UAVs have appeared as a winning candidate to mitigate security *** this paper,we leverage UAVs to propose two *** first method utilizes a UAV as Decode-and-Forward(DF)relay,whereas the second method utilizes a UAV as a jammer to mitigate eavesdropping attacks for URLLC between transmitter and receiver ***,we present a low-complexity algorithm that outlines the two aforementioned methods of mitigating interception,*** secrecy rate,and we compare them with the benchmark null method in which there is a direct communication link between transmitter and receiver without the UAV DF ***,simulation results show the effectiveness of such methods by improving the secrecy rate and its dependency on UAV height,blocklength,decoding error probability and transmitter-receiver separation ***,we recommend the best method to enhance the secrecy rate in the presence of an eavesdropper based on our simulations.

关键词： URLLC UAV-Assisted communications 6G systems Secrecy rate

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A novel deep learning approach for automated grading of knee osteoarthritis severity

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Multimedia Tools and Applications 2024年 1-20页

作者： Kaur, PrabSimran Kohli, Guneet Singh Bedi, Jatin Wasly, Saud Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Electrical and Computer Engineering Department King Abdulaziz University Jeddah22254 Saudi Arabia Center of Excellence in Intelligent Engineering Systems King Abdulaziz University Jeddah22254 Saudi Arabia

Knee Osteoarthritis (OA) is a prevalent musculoskeletal disorder that affects the knee joint that causes pain, stiffness, and reduced mobility. It is also known as "Degenerative Joint Disease" and is caused by the degeneration of cartilage in the knee joint, leading to bone-on-bone contact and further damage. Knee OA is prevalent in the population, affecting around 22% to 39% of people in India, and there is currently no treatment available that can halt the progression of the disease. Therefore, early diagnosis and management of symptoms are essential to reduce its impact on an individual’s quality of life. To address this issue, have introduced a framework that leverages ConvNeXt architecture, a modernization of ResNets (ResNet-50) architecture towards Hierarchical Transformers (Swin Transformers), to provide accurate identification and classification of knee osteoarthritis. The classification of knee osteoarthritis was done using the Kellgren and Lawrence (KL) graded X-ray images. These images of the damaged knees are preprocessed and augmented, creating a scaled, enhanced, and varied version of the features, thus making the data fitter and more significant for classification. The performance estimation of the proposed strategy is conducted on the Osteoarthritis Initiative (OAI), a research project focused on knee osteoarthritis that works in partnership with NIH and other private industries to develop a public domain dataset that can facilitate research and evaluation. It involves training the prepared data using various hyper-tuned versions of ConvNeXt. The different fine-tuned results of the ConvNeXt models on each KL Grade are evaluated against the other state-of-the-art models and vision transformers. The comparative assessment of widely used performance measures shows that the proposed approach outperforms the conventional models by generating the highest score for all the KL grades. Lastly, an approach is employed to statistically confirm the validity of t

关键词： Joints (anatomy)

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Optimization and Characterization of Foundry Fabricated SiN Mode Converters for Enhanced Misalignment Tolerance for On-Chip Laser Diodes

Optimization and Characterization of Foundry Fabricated SiN ...

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2024 Laser Science, LS 2024

作者： Begović, Amir Zhang, Meng Crowley, Daniel McDonough, Colin Dikshit, Amit Huang, Zhaoran Rena Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Research Foundation SUNY AlbanyNY12203 United States

We present and characterize various coupler designs developed in the AIM Photonics Foundry for efficient mode conversion from integrated lasers to on-chip waveguides, focusing on maximizing alignment tolerance. ©... 详细信息

关键词： Photonics

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A Novel Hybrid CNN-XGBoost Model for Photovoltaic System Power Forecasting 6

A Novel Hybrid CNN-XGBoost Model for Photovoltaic System Pow...

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6th International Youth Conference on Radio Electronics, electrical and Power engineering, REEPE 2024

作者： Bashir, Safia Babikir Farag, Mena Maurice Hamid, Abdul Kadir Adam, Ali Ahmed Abo-Khalil, Ahmed Galal Bansal, Ramesh Sustainable Energy and Power Systems Research Center Research Institute of Sciences and Engineering University of Sharjah Smart Grid and Power Systems Research Group Department of Electrical Engineering Sharjah United Arab Emirates

ISBN: (纸本)9798350382891

Photovoltaic (PV) systems have been at the forefront of renewable energy resources, being a primary alternate source of energy within the electricity supply. However, PV systems are vulnerable to abrupt changes in weather conditions, which affects the stability of DC output generation. Therefore, forecasting the DC power with respect to variable weather conditions is a necessity. This paper proposes a hybrid forecasting model composing of convolution neural network (CNN) and XGBoost models, to enhance the forecasting accuracy during dynamic weather conditions. The model is validated based on a 2.88 kW grid-connected PV system located in Sharjah, UAE. The proposed model has shown superior performance with respect to contemporary models, depicting an RMSE of 44.18 and a R2 of 0.9962, which provides accurate forecasting of DC power under dynamic weather conditions. The infusion of CNN model has significantly enhanced the forecasting accuracy, as compared to the standalone XGBoost. © 2024 IEEE.

关键词： Solar energy

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A SiC-Based 243 kW 60 kW/L Electric Vehicle Traction Inverter

A SiC-Based 243 kW 60 kW/L Electric Vehicle Traction Inverte...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Wu, Chih-Chiang Rout, Uma Sankar Chou, Hsin-Ping Hu, Jwu-Sheng Industrial Technology Research Institute Mechanical and Mechatronics Systems Research Laboratories Hsinchu Taiwan National Yang Ming Chiao Tung University Department of Electrical Engineering & Computer Science Hsinchu Taiwan

ISBN: (纸本)9798350376067

This paper investigates the evolving landscape of EV traction inverters, addressing key technical goals outlined by US Drive and analyzing parameters such as voltage, power density, and efficiency. Through innovative methodologies, a high-efficiency SiC-based traction inverter is developed to overcome challenges like spatial constraints. Leveraging international benchmarks, a cutting-edge SiC-based 800V 243 kW traction inverter with a power density of 60 kW/L is presented, poised to surpass US Drive's 2025 targets. This study emphasizes SiC technology's role in advancing EV propulsion systems towards sustainable and efficient transportation solutions. © 2024 IEEE.

关键词： power density silicon carbide high-efficiency sustainable transportation traction inverter

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Dynamic Modeling and Quantum-Enhanced Forecasting of Multi-Seasonal Energy Prices in Simulated Microgrid Environments

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IEEE Access 2025年 13卷 90362-90388页

作者： Dash, Ritesh Sinha, Anupa Jyotheeswara Reddy, K. Dhanamjayulu, C. Kamwa, Innocent Kalinga University Research Scholar Computer Science Engineering Raipur India Kalinga University Computer Science Engineering Raipur India REVA University School of Electrical and Electronics Engineering Bangalore India Vellore Institute of Technology School of Electrical Engineering Vellore632014 India Laval University Department of Electrical Engineering and Computer Engineering Quebec G1V 0A6 Canada

The study address the challenge of forecasting per unit energy prices in a microgrid environment consisting of solar and hydro power resources under multi-seasonal *** deep learning techniques such as LSTM,GRU and ESN often struggle with non-linear dependencies and volatility in energy market. To overcome these we propose a hybrid framework incorporating Adiabatic Quantum Computing (AQC) for electricity price forecasting. The proposed AQC model encodes-32 system and market related variables into quantum states and applies adiabatic evolution to derive optimized price prediction. Simulation results using real microgrid data set-up based on HIL shows that AQC reduces forecasting error by 17.03% compared to LSTM, 14.29% to GRU and 13.88% to ESN over 24-hrs and 48-hrs horizons. The enhanced accuracy and robustness of the quantum assisted model demonstrates its potential for next generation energy market forecasting and decisions making *** entire framework is tested using a synthetic microgrid dataset designed to emulate real-world seasonal and operational dynamics. While this enables controlled validation of the models, the generalizability of the results to real world deployment requires further empirical evaluations on physical microgrid data set. © 2013 IEEE.

关键词： Qubits

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