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28th International Conference on Principles of Distributed Systems, OPODIS 2024

作者： Dhoked, Sahil Fahmy, Ahmed Golab, Wojciech Mittal, Neeraj Department of Computer Science The University of Texas DallasTX United States Department of Electrical and Computer Engineering University of Waterloo Canada

ISBN: (纸本)9783959773607

We describe a novel construction of arbitrary read-modify-write (RMW) primitives in a persistent shared memory model with process failures. Our construction uses blocking synchronization, in the form of recoverable mutual exclusion (RME), and is optimal in terms of the widely studied remote memory reference (RMR) complexity measure. The implemented objects tolerate either system-wide or independent process crashes, depending on the RME lock used, and also provide detectability for resolving the outcome of operations interrupted by failures. We prove that our construction is RMR-optimal using a reduction back to the RME problem. Our proof technique introduces a novel algorithmic style that enables solving challenging synchronization problems using a common execution path for both the system-wide and independent failure models, which previously required separate analyses, and relies only on a suitable implementation of the detectable base objects in each model to achieve RMR efficiency. Experiments demonstrate that our construction outperforms prior wait-free and lock-free algorithms on a multiprocessor with Intel Optane persistent memory. © Sahil Dhoked, Ahmed Fahmy, Wojciech Golab, and Neeraj Mittal.

关键词： Synchronization

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COMPASS: Compiler Pass Selection For Improving Fidelity Of NISQ Applications 9

COMPASS: Compiler Pass Selection For Improving Fidelity Of N...

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9th Annual IEEE International Conference on Rebooting Computing, ICRC 2024

作者： Dangwal, Siddharth Ravi, Gokul Subramanian Seifert, Lennart Maximilian Das, Poulami Sud, James Chong, Frederic T. Department of Computer Science University of Chicago United States Department of Computer Science and Engineering University of Michigan United States Department of Electrical and Computer Engineering The University of Texas Austin United States

ISBN: (纸本)9798331541279

Noisy qubit devices limit the fidelity of programs executed on near-term or Noisy Intermediate Scale Quantum (NISQ) systems. The fidelity of NISQ applications can be improved by using various optimizations during program compilation (or transpilation). These optimizations or passes are designed to minimize circuit depth (or program duration), steer more computations on devices with lowest error rates, and reduce the communication overheads involved in performing two-qubit operations between non-adjacent qubits. Additionally, standalone optimizations have been proposed to reduce the impact of crosstalk, measurement, idling, and correlated errors. However, our experiments using real IBM quantum hardware show that using all optimizations simultaneously often leads to sub-optimal performance and the highest improvement in application fidelity is obtained when only a subset of passes are used. Unfortunately, identifying the optimal pass combination is non-trivial as it depends on the application and device specific properties. In this paper, we propose COMPASS, an automated software framework for optimal Compiler Pass Selection for quantum programs. COMPASS uses dummy circuits that resemble a given program but is composed of only Clifford gates and thus, can be efficiently simulated classically to obtain its correct output. The optimal pass set for the dummy circuit is identified by evaluating the efficacy of different pass combinations and this set is then used to compile the given program. Our experiments using real IBMQ machines show that COMPASS improves the application fidelity by 4.3x on average and by upto 248.8x compared to the baseline. However, the complexity of this search scales exponential in the number of compiler steps. To overcome this drawback, we propose Efficient COMPASS (E-COMPASS) that leverages a divide-and-conquer approach to split the passes into sub-groups and exhaustively searching within each sub-group. Our evaluations show that E-COMPASS impro

关键词： Qubits

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A Simple Pre-Estimation Method for Calculating the Inductance of a Variable Inductor 12

A Simple Pre-Estimation Method for Calculating the Inductanc...

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12th Iranian Conference on Renewable Energies and Distributed Generation, ICREDG 2025

作者： Fardadidokht, Sahand Talebi, Seyedamirhossein Sadeghian, Salar Zarei, Seyed Fariborz School of Electrical Engineering Iran University of Science and Technology Tehran Iran North Carolina State University Department Electrical and Computer Engineering NC United States Department of Electrical and Computer Engineering Qom Iran

ISBN: (纸本)9798331525132

This paper presents a simple method for predicting inductance in applications of variable inductors with ferrite cores. Today, switching converters are widely used for voltage and current level conversion in renewable energy applications. The variation of inductance in switching converters improves the voltage gain range and other converter characteristics. In this paper, through the analysis of core saturation, a simple circuit model is proposed to predict inductance when the core reaches saturation. This model enables designers to predict inductance variations prior to the winding of the core and the construction of the inductor. Two approaches regarding core permeability are analyzed: the well-known Brauer relationship and a proposed alternative. Finally, the proposed method has been validated and tested on two types of cores, includes E42 and E55. © 2025 IEEE.

关键词： Electric transformers

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Effect of Signal Conditioning and Evoked-Potential Based Representation on Stability and Distinctiveness of EEG Brain Signatures 12

Effect of Signal Conditioning and Evoked-Potential Based Rep...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Oztemel, Muhammed Esad Soysal, Omer Muhammet Louisiana State University Division of Electrical and Computer Engineering United States Southeastern Louisiana University Computer Science United States

ISBN: (纸本)9798350330366

Biometric patterns have been used for authentication purposes for more than decades. Although traditional biometric measurements including fingerprints, facial recognition, and sound recognition are often used for authentication purposes, there is ongoing concern about the risks associated with their repeatability and susceptibility to theft. The utilization of electroencephalography (EEG) has promising potential thanks to the distinctive patterns extracted from brain signals. The main goal of this study is to explore the effectiveness of signal conditioning and evoked potentials on uniqueness and permanence of individuals' EEG signals for authentication purpose. We utilized the Laplacian of Gaussian as a signal conditioning operator. In addition, three signifying functions were explored in obtaining evoked potential time-domain representation from an individual EEG signal. Different stimuli protocols were employed for comparison. A time-series and a statistical technique were utilized for uniqueness and permanence analysis. © 2024 IEEE.

关键词： Authentication

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ProTEA: Programmable Transformer Encoder Acceleration on FPGA

ProTEA: Programmable Transformer Encoder Acceleration on FPG...

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2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Kabir, Ehsan Bakos, Jason D. Andrews, David Huang, Miaoqing University of Arkansas Department of Electrical Engineering and Computer Science Fayetteville United States University of South Carolina Department of Computer Science and Engineering United States

ISBN: (纸本)9798350355543

Transformer neural networks (TNN) have been widely utilized on a diverse range of applications, including natural language processing (NLP), machine translation, and computer vision (CV). Their widespread adoption has been primarily driven by the exceptional performance of their multi-head self-attention block used to extract key features from sequential data. The multi-head self-attention block is followed by feedforward neural networks, which play a crucial role in introducing non-linearity to assist the model in learning complex patterns. Despite the popularity of TNNs, there has been limited numbers of hardware accelerators targeting these two critical blocks. Most prior works have concentrated on sparse architectures that are not flexible for popular TNN variants. This paper introduces ProTEA, a runtime programmable accelerator tailored for the dense computations of most of state-of-the-art transformer encoders. ProTEA is designed to reduce latency by maximizing parallelism. We introduce an efficient tiling of large matrices that can distribute memory and computing resources across different hardware components within the FPGA. We provide run time evaluations of ProTEA on a Xilinx Alveo U55C high-performance data center accelerator card. Experimental results demonstrate that ProTEA can host a wide range of popular transformer networks and achieve near optimal performance with a tile size of 64 in the multi-head self-attention block and 6 in the feedforward networks block when configured with 8 parallel attention heads, 12 layers, and an embedding dimension of 768 on the U55C. Comparative results are provided showing ProTEA is 2.5× faster than an NVIDIA Titan XP GPU. Results also show that it achieves 1.3 - 2.8× speed up compared with current state-of-the-art custom designed FPGA accelerators. © 2024 IEEE.

关键词： Attention Encoder FPGA Hardware Accelerators High-Level Synthesis Natural Language Processing Neural Networks Transformer

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DGeC: Dynamically and Globally Enhanced Convolution

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 921-933页

作者： Zhang, Zihang Liu, Yuling Zhou, Zhili Yang, Gaobo Liao, Xin Wu, Q. M. Jonathan Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Guangzhou University Institute of Artificial Intelligence and Blockchain Guangzhou510006 China University of Windsor Department of Electrical and Computer Engineering WindsorON250101 Canada

We explore the reasons for the poorer feature extraction ability of vanilla convolution and discover that there mainly exist three key factors that restrict its representation capability, i.e., regular sampling, static aggregation, and limited receptive field. With the cost of extra parameters and computations, existing approaches merely alleviate part of the limitations. It drives us to seek a more lightweight operator to further improve the extracted image features. Through a closer examination of the convolution process, we discover that it is composed of two distinct interactions: spatial-wise interaction and channel-wise interaction. Based on this discovery, we decouple the convolutional blocks into these two interactions which not only reduces the parameters and computations but also enables a richer ensemble of interactions. Then, we propose the dynamically and globally enhanced convolution (DGeC), which includes several components as follows: a dynamic area perceptor block (DAP) that dynamically samples spatial cues, an adaptive global context block (AGC) that introduces the location-aware global image information, and a channel attention perceptor block (CAP) that merges different channel-wise features. The experiments on ImageNet for image classification and on COCO-2017 for object detection validate the effectiveness of DGeC. As a result, our proposed method consistently improves the performance with fewer parameters and computations. In particular, DGeC achieves a 3.1% improvement in top-1 accuracy on ImageNet dataset compared to ResNet50. Moreover, with Faster RCNN and RetinaNet, our DGeC-ResNet50 also consistently outperforms ResNet and ResNeXt. © 2024 IEEE.

关键词： Convolutional neural networks

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Understanding Consumer Perceptions of Green Software: A Study in the Online Social Media Marketing Context

Understanding Consumer Perceptions of Green Software: A Stud...

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2023 International Conference on Industry sciences and computer science Innovation, iSCSi 2023

作者： Irtisamul, Shadat Hassan, Marzan Binte Sadia, Farzana Hasan, Mahady Rokonuzzaman, Mohammad Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh Department of Electrical Engineering and Computer Science North South University Dhaka Bangladesh

This study explores the influence of social media marketing on consumers' decisions to purchase green software and identifies key factors affecting those decisions. The findings contribute to effective marketing strategies for promoting environmentally friendly technologies and increasing the adoption of green software. Social media marketing is highlighted as a crucial platform for communication and collaboration, shaping consumers' subjective norms and influencing decision-making. By utilizing persuasive techniques and emphasizing eco-friendly choices, social media marketing can significantly impact consumers' behavior, attitudes, and decision-making processes, promoting sustainable practices and increasing the adoption of eco-friendly software. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Consumer behavior

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A novel deep dual self-attention and Bi-LSTM fusion framework for Parkinson’s disease prediction using freezing of gait: a biometric application

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Multimedia Tools and Applications 2024年第33期83卷 80179-80200页

作者： Habib, Zeeshan Mughal, Muhammad Ali Khan, Muhammad Attique Hamza, Ameer Alturki, Nazik Jamel, Leila Department of Electrical Engineering HITEC University Taxila Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia

Parkinson’s disease (PD) disorder is caused by the imbalance of inhibitory dopamine and excitatory acetylcholine neurotransmitters, which causes hindrance in locomotion. Freezing of gait (FOG), tremors, and bradykinesia are the most debilitating and disconcerting symptoms of Parkinson’s Disease (PD). FOG episodes occur due to neurological control disorder and motorized impairments, which sternly hinder voluntary movement and forward locomotion. The computational intelligence techniques can effectively predict PD from the sensor data. To detect the FOG episodes often experienced by PD patients, this paper utilizes a 5G spectrum that operates at 4.8 GHz. The main idea of the proposed system is to extract the wireless channel characteristics information (containing amplitude variances) that can be integrated into a 5G communication system. This system collects data through wireless devices such as dipole antennas, network interface cards, and RF signal generators. Five human activities were performed: fast walking, slow walking, sitting on a chair, FOG episodes, and voluntary stopping. We proposed a novel deep dual self-attention and Bi-LSTM fusion architecture for PD prediction to train the collected dataset. Two architectures have been proposed, namely Deep Dual Attention Neural Network (D2ANN) and BiLSTM Neural Network (BNN), for the classification of PD using Chanel State Information (CSI) data. After that, the most prominent information is extracted using self-attention activation. A newly informed weighted fusion approach is proposed, and tree growth optimization is employed for the best feature selection. Finally, the selected features are classified using a multilayer full SoftMax neural network. The experimental process was conducted on FOG wireless sensor network data and obtained the highest accuracy of 98.66%. Compared to the results with state-of-the-art techniques, the proposed framework shows improved accuracy, recall, and precision rates. © The Author(s

关键词： Network architecture

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Forecasting Bitcoin Prices Using Deep Learning for Consumer-Centric Industrial Applications

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1351-1358页

作者： Roy, Pradeep Kumar Kumar, Abhinav Singh, Ashish Sangaiah, Arun Kumar Indian Institute of Information Technology Surat Department of Computer Science and Engineering Surat394190 India Motilal Nehru National Institute of Technology Allahabad Department of Computer Science and Engineering Prayagraj211004 India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar751024 India National Yunlin University of Science and Technology International Graduate Institute of AI Douliou64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102-2801 Lebanon

As cryptocurrencies become more popular as investment vehicles, bitcoin draws interest from businesses, consumers, and computer scientists all across the world. Bitcoin is a computer file stored in digital wallet applications where each transaction is secured using strong cryptographic algorithms. It was challenging to forecast the future price of bitcoin due to its nonlinearity and extreme volatility. Several recent classic parametric models have been found with limited accuracy. To address the limitations and fill the existing research gaps, there is a need for a good prediction model which will provide the desired accuracy in the case of uncertainty and dynamism. This research suggested a deep learning-based framework for predicting and forecasting Bitcoin price. The research will be helpful for worldwide consumers and industries to take their decision on whether to invest or not. The research utilizes Yahoo! finance dataset for the period of 01-03-2016 to 26-02-2021 having 1828 samples. The experimental outcomes of the proposed Long Short-Term Memory (LSTM) model outperformed similar deep learning models by securing minimum loss and confirming that it can be used for future price prediction of the cryptocurrencies, which is helpful for the buyer to take their decision. © 1975-2011 IEEE.

关键词： Hidden Markov models

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Modular DAB Converter Power Balancing with Variable Inductor 12

Modular DAB Converter Power Balancing with Variable Inductor

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12th Iranian Conference on Renewable Energies and Distributed Generation, ICREDG 2025

作者： Talebi, Seyedamirhossein Sadeghian, Salar Zarei, Seyed Fariborz North Carolina State University Department Electrical and Computer Engineering NC United States School of Electrical Engineering Iran University of Science and Technology Tehran Iran Department of Electrical and Computer Engineering Qom Iran

ISBN: (纸本)9798331525132

The Dual Active Bridge (DAB) converter is one of the suitable isolated structures for high-power applications. With the increasing demand for energy, consumers such as electric vehicles and similar devices require significant electrical power. Due to the limitations of power handling in switching converter components, such as the maximum current and voltage ratings of semiconductors, it has become necessary to connect multiple converters in a modular configuration to achieve the required total power. However, this approach can result in power imbalance among the modules due to non-idealities and mismatched component characteristics in each module. In this paper, by modifying the inductor of the DAB converter, power balance between the modules is achieved. Finally, two DAB converter modules providing a total power of 10 kW are simulated and verified in MATLAB software. © 2025 IEEE.

关键词： MATLAB

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