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检索条件"任意字段=5th International Conference on Algorithms and Architectures for Parallel Processing"

共 3274 条记录，以下是81-90 订阅

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Design and Implementation of FIR Filter Using Different architectures: A Comparative Study

Design and Implementation of FIR Filter Using Different Arch...

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international conference on Signal processing and Communication (ICSC)

作者： Teena Soni Anil Kumar Manoj Kumar Panda PDPM-IIITDM Jabalpur India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

the article proposes a hybrid sparse particle swarm optimization algorithm (HSPSO). this paper also presents an improved low-pass FIR filter design utilizing the HSPSO algorithm. the effectiveness of the proposed algorithm is contrasted with particle swarm optimization and sparse PSO in resolving the FIR filter design problem. HSPSO offered smaller best cost and execution time values. Furthermore, the suggested approach competes with the existing approaches in the literature, providing higher stopband attenuation values and smaller passband ripple values. the proposed work implemented the designed FIR filters, employing various multiplierless techniques, including the canonical signed digit (CSD) number system, fractional CSD (FCSD), distributed arithmetic (DA), and parallel distributed arithmetic (PDA). the comparative study of all the architectures and multiplier-based structure (MULT) is performed for the FPGA and ASIC environments. the DA outperforms all the architecture in area and power. the PDA with decomposition factor 4 provides the highest throughput and also smaller area and power compared with MULT, CSD, and FCSD-based architecture.

关键词： Costs Finite impulse response filters Signal processing algorithms throughput Attenuation Passband Particle swarm optimization Field programmable gate arrays Optimization Arithmetic

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Lightweight Single Image Super-Resolution Network Integrating CNN and Transformer 27th

Lightweight Single Image Super-Resolution Network Integratin...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Zhu, Kai Chen, Li School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783031781247

Recently, remarkable progress has been achieved in single image super-resolution using methods based on CNN and Transformer architectures. However, existing approaches often construct a substantial number of network layers, leading to a significant increase in performance requirement and memory consumption, thereby limiting the practical deployment and usability of the models. To address this issue, we propose an Alternating CNN Transformer Block and an Integrative CNN Efficient Transformer for single image super-resolution. We enhance feature extraction efficiency by combining CNN within and between Transformer modules. In addition, we propose two novel structures: Multi-branch Gated CNN and parallel Channel Attention, aiming to efficiently extract local spatial information and global channel information from images. Extensive experiments demonstrate that our model achieves high performance while maintaining low model complexity. the proposed model attains PSNR values of 32.32 and 30.78 for the Set5 and Manga109 benchmark datasets, respectively, at a scale factor of ×4. Compared to other lightweight super-resolution models, our proposed model outperforms them at lower computational costs. the source codes are available at https://***/kylechuuuuu/ICTSRN. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： HTTP

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ORL-EPM: A Profit-Aware and Load-Driven Heterogeneous Resource Management Scheme with Collaborative Edge Computing 24th

ORL-EPM: A Profit-Aware and Load-Driven Heterogeneous Resou...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Huang, Fengyi Zhang, Yutao Wang, Wenhua Guo, Jianxiong Xu, Yang Fan, Wentao Wang, Tian Hong Kong Baptist University Hong Kong China Hong Kong Baptist University United International College Beijing Normal University Zhuhai China Beijing Normal University Zhuhai China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9789819615414

To address the limitations of energy-efficient but computationally limited ARM64-based AI edge devices and general-purpose edge servers, this paper proposes a Decentralized Collaborative Heterogeneous Edge Computing (DCEC) architecture. this architecture combines edge servers with embedded AI devices to achieve low latency and enhanced computational capabilities. Commercially, the DCEC framework is divided into edge private clouds and edge public clouds, aiming to maximize the profits of Edge Service Providers (ESPs) through dynamic resource management. Considering task dynamism, complexity, and resource heterogeneity, this complex problem is formulated as a multi-stage mixed-integer nonlinear programming (MINLP) problem. We developed the ORL-EPM resource management framework—a three-layer optimization system that adjusts task scheduling based on varying latency sensitivity weights and heterogeneous resource demands. Additionally, we introduced a resource collaboration system based on resource leasing to manage resource overloads and accommodate diverse task complexities. this system includes three Economic Payment Models (EPMs) designed to achieve efficient and profitable resource utilization. Extensive simulation results indicate that this method ensures convergence and closely approximates optimal solutions across various scenarios, significantly outperforming existing methods. Testbed experiments demonstrate that the DCEC architecture can reduce latency by up to 21.83% in real-world applications, notably exceeding previous approaches. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Energy efficiency

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Optimizing Concurrent Data Structures in Rust

Optimizing Concurrent Data Structures in Rust

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE international conference on

作者： Xin Zhao Collage of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

Lock- Free data structures are a key approach in concurrent programming that allows for thread-safe data manipulation without the need for traditional locking methods. they can diminish the overhead of context switching and lock contention in multi-threaded scenarios, which in turn boosts program performance. this paper investigates the efficient construction of lock-free concurrent data structures, harnessing Rust's type system and the commutativity of operations to attain a considerable performance enhancement compared to traditional atomic data types.

关键词： Concurrent computing Costs Commutation Instruction sets Memory management Switches Programming parallel processing Data structures Safety

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30th international conference on parallel and Distributed processing Techniques, PDPTA 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

30th International Conference on Parallel and Distributed Pr...

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30th international conference on parallel and Distributed processing Techniques, PDPTA 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

ISBN: (纸本)9783031856372

the proceedings contain 24 papers. the special focus in this conference is on parallel and Distributed processing Techniques. the topics include: parallel N-Body Performance Comparison: Julia, Rust, and More;REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments;An Efficient Data Provenance Collection Framework for HPC I/O Workloads;using Minicasts for Efficient Asynchronous Causal Unicast and Byzantine Tolerance;a Comparative Study of Two Matrix Multiplication algorithms Under Current Hardware architectures;Is Manual Code Optimization Still Required to Mitigate GPU thread Divergence? Applying a Flattening Technique to Observe Performance;towards Automatic, Predictable and High-Performance parallel Code Generation;Attack Graph Generation on HPC Clusters;analyzing the Influence of File Formats on I/O Patterns in Deep Learning;inference of Cell–Cell Interactions through Spatial Transcriptomics Data Using Graph Convolutional Neural Networks;natural Product-Like Compound Generation with Chemical Language Models;improved Early–Modern Japanese Printed Character Recognition Rate with Generated Characters;Improved Method for Similar Music Recommendation Using Spotify API;Reconfigurable Virtual Accelerator (ReVA) for Large-Scale Acceleration Circuits;Building Simulation Environment of Reconfigurable Virtual Accelerator (ReVA);vector Register Sharing Mechanism for High Performance Hardware Acceleration;Efficient Compute Resource Sharing of RISC-V Packed-SIMD Using Simultaneous Multi-threading;introducing Competitive Mechanism to Differential Evolution for Numerical Optimization;hyper-heuristic Differential Evolution with Novel Boundary Repair for Numerical Optimization;jump Like a Frog: Optimization of Renewable Energy Prediction in Smart Gird Based on Ultra Long Term Network;vision Transformer-Based Meta Loss Landscape Exploration with Actor-Critic Method;Fast Computation Method for Stopping Condition of Range Restricted

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A Study of GPU Programming Paradigms: OpenACC vs. CUDA in Linear Algebra Computations

A Study of GPU Programming Paradigms: OpenACC vs. CUDA in Li...

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Advances in Electrical, Electronics and Computing Technology (EECT), international conference on

作者： Ezhilmathi Krishnasamy Pascal Bouvry Department of Computer Science FSTM University of Luxembourg Belval Luxembourg

ISBN: (数字)9798331541545

ISBN: (纸本)9798331541552

Scientific and engineering problems are frequently governed by partial differential equations; however, the analytical solutions of these equations are often impractical, thereby forcing the adoption of numerical methods. Basic Linear Algebra Sub-programs (BLAS) operations constitute a fundamental component of these numerical approaches, incorporating essential tasks such as Level 1 operations (dot products and vector addition), Level 2 operations (matrix-vector multiplication), and Level 3 operations (matrix-matrix multiplication). Graphics processing Units (GPUs), particularly those produced by NVIDIA, have gained significant computational power and are extensively employed to tackle a variety of numerical challenges. Nevertheless, substantial obstacles remain in targeting diverse GPU architectures, particularly concerning portability, the reduction of workarounds, and the enhancement of performance. this study utilizes directive-based programming languages, such as OpenACC, to effectively exploit GPU capabilities. We undertake a comprehensive comparative study and performance evaluation of the OpenACC programming model in comparison to CUDA in executing essential BLAS routines.

关键词： Performance evaluation Computers Computer languages Partial differential equations High performance computing Graphics processing units Vectors Mathematical models Performance analysis Numerical models

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5th international Symposium on Intelligent Computing Systems, ISICS 2024

5th International Symposium on Intelligent Computing Systems...

引用

5th international Symposium on Intelligent Computing Systems, ISICS 2024

ISBN: (纸本)9783031829307

the proceedings contain 25 papers. the special focus in this conference is on Intelligent Computing Systems. the topics include: Using LLMs to Enhance Research Output Tracking in Weakly Categorized Research Areas: A Case Study to Classify Publications in Islamic Economics, Finance, Banking and Business;Evaluating AI-Generated Video Quality: A Novel Assessment Model;a Non-blind Audio Watermarking in 3D Mesh Models Using Daubechies Wavelet Technique;Assessing the Impact of Data Augmentation Techniques on YOLO’s Performance in PV Anomaly Detection;studying Factors Affecting Attitudes on Artificial Intelligence Voice Assistants in the United Arab Emirates;Conceptualizing a Generative AI-Based Solution for Emotional Intelligence Interventions: Application to Education and Training;dental Caries Segmentation from Orthopantomograms Using Optimized DeepLabV3+;convolutional Neural Networks for the Classification of Comet Assay Cells;AI Driven Carbon Emission Estimation and Forecasting with Recommended Solution;leveraging Deep Learning for Accurate Classification of Leptograpsus Crabs Based on Morphological Measurements;heart Disease Prediction Using Machine Learning;development and Implementation of a Learning Management System with Data Analytics for Enhanced Online Education;VidLyze: Video Content Toxicity Identification Tool Using Natural Language processing (NLP) and Machine Learning;Comprehensive Evaluation of GAN architectures for Acute Lymphoblastic Leukemia Classification;a Novel Classification of Authentication Schemes for Internet of Drones;carbonMetric: An Intelligent System for Calculating and Reducing Carbon Footprints in Urban Environments;evaluating Adan vs. Adam: An Analysis of Optimizer Performance in Deep Learning;quality Assessment of Fundus Images for Further Classification: Lesson from thesis;empowering Students: Leveraging No-Code and Training Models with Google Machine Learning for Accessible App Development.

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A High-Performance Architectural Design for FIR Filtering in Fixed and Reconfigurable Applications

A High-Performance Architectural Design for FIR Filtering in...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： Harikaran S Naveen Kumar G Sharmila J Kanagamalliga S Dept. of Electronics and Communication Engineering Saveetha Engineering College Chennai India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

the FIR filter architecture is proposed that achieves high-speed operation focused on both fixed and programmable applications. Instead, we take a transpose form configuration of a block FIR filter in an area-delay efficient way, which is effective for medium-to-large orders of the filters we introduce a generalized block formulation for transpose FIR filters, providing a unified approach for implementation. Moreover, we also present a low-complexity architecture based on the Multiple Constant Multiplication (MCM) strategy for fixed FIR filters, which helps in inordinate reduction in associated complexity and resources. the proposed architecture shows significant savings in terms of the Area-Delay Product (ADP), when compared to the traditional direct-form FIR filter architectures when they are implemented using digital blocks, which is especially true for medium and large sizes. the direct-form implementation is more efficient for low filter lengths, but as they increase, it becomes impractical, where the proposed architecture applies. this renders the design highly beneficial for reconfigurable and resource-efficient FIR filter applications. To verify the functionality of the proposed architecture, the design is coded in Verilog HDL, modeled in ModelSim and synthesized in Xilinx tools. Extensive experimental results validate that the proposed architecture save a huge amount with respect to resource usage as well as delay, thus are very beneficial for future digital signal processing applications. therefore, the proposed method offers a flexible and efficient design of fixed and reconfigurable FIR filters that meets the increasing demand for high-performance and low-complexity digital filter structures.

关键词： Materials science and technology Power demand Finite impulse response filters Computer architecture throughput Market research Real-time systems Complexity theory Hardware design languages Pipeline processing

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AIDetectVul: Software Vulnerability Detection Method Based on Feature Fusion of Pre-trained Models

AIDetectVul: Software Vulnerability Detection Method Based o...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE international conference on

作者： Shiying Xue Lin Li Tao Li Haodong Chen Jiapan Li Yangqing Qin School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Intelligent Information Processing and Real time Industrial Systems Wuhan University of Science and Technology Wuhan China Wuhan Jiaoyun Huizhi Information Technology Co. Ltd. Wuhan China Guotou Securities Co. Ltd. Shenzhen China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

Data-driven deep learning models are constrained by the scale and diversity of training data, making them vulnerable to data bias. While large language models (LLMs) exhibit superior generalization in vulnerability detection, their low inference efficiency and high computational costs hinder practical deployment in industrial settings. To address these limitations, we propose AIDetectVul, a novel vulnerability detection framework leveraging feature fusion from pre-trained models. Our approach concurrently utilizes encoder-only and decoder-only architectures to extract complementary code embeddings, with feature fusion enhancing semantic diversity. these enriched representations are then processed by a Transformer model, where the self-attention mechanism effectively captures long-range code dependencies, ultimately improving both detection accuracy and generalization capability. Comprehensive evaluations on proprietary enterprise datasets and open-source benchmarks demonstrate that AIDetectVul achieves comparable detection accuracy to the state-of-the-art LineVul model while demonstrating measurable improvements in generalization performance. Compared to LLM-based approaches, our solution maintains significantly lower computational overhead and training costs, making it particularly suitable for industrial applications.

关键词： Training Accuracy Codes Computational modeling Large language models Training data Computer architecture Feature extraction Transformers Vectors

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A Bio-Inspired Hybrid Optimization Model for Enhancing Operating Performance and Efficiency of Electric Vehicles

A Bio-Inspired Hybrid Optimization Model for Enhancing Opera...

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Smart Grid and Green Energy (ICSGGE), international conference on

作者： Muhammad Zeshan Afzal Fushuan Wen Nimrah Saeed College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798331542139

ISBN: (纸本)9798331542146

Electric Vehicles (EVs) are critical for the advancement of sustainable transportation, however, there is still a need to optimize energy consumption, battery longevity, and system adaptability. In this study, we introduce Quantum-BioNet (QBN), a hybrid optimization framework that combines quantum computing principles and bio inspired algorithms to address these issues. For efficient exploration of a vast solution space, QBN utilizes parameterized quantum circuits; for real time responsiveness, it uses bio inspired adaptation. the model allows critical EV parameters to be dynamically optimized (i.e., energy management and regenerative braking) under varying operational conditions. Implemented in a MATLAB/Simulink simulation environment replicating real-world scenarios, QBN achieved significant performance improvements: 1) ${2 0 \%}$ decrease in energy consumption; 2) ${3 0 \%}$ increase in battery life; 3) fast adaptation within 5-6 seconds to changes in traffic density and road grade. QBN was found to have better computational efficiency (90 seconds to converge in a computer with ${6 0 \%}$ CPU and 950 MB memory) than state-of-the-art Quantum Optimization (QO) and Bio Inspired Optimization (BIO) algorithms, outperforming QO and BIO by $25 \%$ and $18 \%$, respectively. the results indicate QBN’s capability to revolutionize EV optimization with a scalable, energy efficient and cost-effective solution. QBN combines quantum parallelism and adaptive bio-inspired strategies to implement intelligent and green mobility systems that meet critical requirements of real-world EV operation and accommodate future progress.

关键词： Energy consumption Scalability Computational modeling Transportation parallel processing Real-time systems Energy efficiency Batteries Computational efficiency Optimization

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