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检索条件"任意字段=15th Symposium on Computer Architecture and High Performance Computing"

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HyperCMR: Enhanced Multi-contrast CMR Reconstruction with Eagle Loss

HyperCMR: Enhanced Multi-contrast CMR Reconstruction with E...

引用

15th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2024, Held in Conjunction with MICCAI 2024

作者： Xu, Ruru Özer, Caner Oksuz, Ilkay Computer Engineering Department Istanbul Technical University Istanbul Turkey Department of Artificial Intelligence and Data Engineering Istanbul Technical University Istanbul Turkey Department of Applied Mathematics University of Twente Enschede7522 NB Netherlands

ISBN: (纸本)9783031877551

Accelerating image acquisition for cardiac magnetic resonance imaging (CMRI) is a critical task. CMRxRecon2024 challenge aims to set the state of the art for multi-contrast CMR reconstruction. this paper presents HyperCMR, a novel framework designed to accelerate the reconstruction of multi-contrast cardiac magnetic resonance (CMR) images. HyperCMR enhances the existing PromptMR model by incorporating advanced loss functions, notably the innovative Eagle Loss, which is specifically designed to recover missing high-frequency information in undersampled k-space. Extensive experiments conducted on the CMRxRecon2024 challenge dataset demonstrate that HyperCMR consistently outperforms the baseline across multiple evaluation metrics, achieving superior SSIM and PSNR scores. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Energy-Efficient Hardware architecture for VVC TZS Using Approximate Subtractors

Energy-Efficient Hardware Architecture for VVC TZS Using App...

引用

IEEE Latin American symposium on Circuits and Systems (LASCAS)

作者： Rafael Ferreira Luciano Agostini Cláudio M. Diniz Bruno Zatt Video Technology Research Group (ViTech) Graduate Program in Computing (PPGC) Federal University of Pelotas (UFPel) Pelotas RS Brazil Microelectronics Group (GME) Graduate Program in Microelectronics (PGMICRO) Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

this paper investigates the impact of approximate subtractors in the hardware architecture of the Sum of Absolute Differences (SAD) computation within the Test Zone Search (TZS) algorithm, commonly used in current video encoders like the Versatile Video Coding (VVC). Four state-of-the-art imprecise subtractors $(AppS, AXSC1, AXSC2, AXSC3)$ were analyzed across various video resolutions to assess their influence on computational effort, energy consumption, and coding efficiency. the results show that subtractors like $AppS_{4}$ and $AXCS1_{4}$ provide significant reductions in energy consumption with minimal impact on video coding quality. these findings are especially relevant for low-power devices and embedded systems, where energy efficiency is critical. the use of imprecise subtractors offers a promising tradeoff between computational efficiency and energy savings, making them a viable solution for high-performance video encoders.

关键词： Video coding Energy consumption Embedded systems Energy conservation Energy resolution computer architecture Energy efficiency Hardware Computational efficiency Power dissipation

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Improving CNN Runtime Robustness Against Soft Errors by Dropout Layer Optimization

Improving CNN Runtime Robustness Against Soft Errors by Drop...

引用

Latin American Test Workshop, LATW

作者： Robert Limas Sierra Giuseppe Esposito Juan-David Guerrero-Balaguera Josie E. Rodriguez Condia Matteo Sonza Reorda Politecnico di Torino - Department of Control and Computer Engineering (DAUIN)

ISBN: (数字)9781665477635

ISBN: (纸本)9781665477642

Convolutional Neural Networks (CNNs) have shown exceptional effectiveness in complex and data-intensive domains such as image and video processing, conversational systems, and healthcare. Moreover, sectors like high-performance computing and safety-critical applications, including automotive, aerospace, and autonomous robotics, impose stringent requirements on energy efficiency, performance, and robustness. However, modern semiconductor technologies are increasingly vulnerable to faults, which can degrade CNN performance and potentially result in catastrophic failures. this work explores the impact of regularization techniques (dropout layer) in enhancing the inference robustness of CNN models against soft errors. We analyzed soft error impacts on five widely adopted CNN architectures, each trained with ten different dropout rates. Our experimental results reveal that optimizing the dropout rate during training can improve the in-field robustness of CNN models by up to 12% compared to baseline configurations under soft error conditions. Additionally, fine-tuning this architectural parameter can lead to accurary improvements of up to 10%.

关键词： Training Accuracy Runtime computer architecture Medical services Robustness Hardware Convolutional neural networks Robots Optimization

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Machine Learning-Based GPU Energy Prediction for Workload Management in Datacenters

Machine Learning-Based GPU Energy Prediction for Workload Ma...

引用

IEEE Annual computing and Communication Workshop and Conference (CCWC)

作者： Brandon Ismalej Matthew Smith Xunfei Jiang Department of Computer Science California State University Northridge Northridge CA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Data centers (DCs) have become a vital component of a digital economy, accounting for approximately 1% of worldwide energy consumption, as the reliance on cloud services and GPU hardware increases for high-performance computing (HPC) tasks. Despite various strategies for improving energy efficiency, there is a lack of research integrating real-world workload traces into the prediction of GPU power consumption, mainly due to the lack of publicly available data. this research addresses this gap by utilizing synthetic data generated to mimic the GPU intensity of real-world workload characteristics and developing GPU power prediction models for integration into the GPUCloudSim Plus simulator. We conducted a statistical analysis of Alibaba and Helios workload traces and ran experiments with diverse GPU-intensive tasks to emulate real-world workloads. Using task-average metrics, we trained four machine learning (ML) models to predict GPU power consumption, with the best performing XGBoost model yielding an RMSE of 1.217. We propose this modeling design to integrate a GPU power prediction model into the GPUCloudSim Plus simulator. Energy-aware simulations for GPU-intensive workloads could be verified on workloads from real data centers, advancing the study of workload management.

关键词： Measurement Data centers Power demand Statistical analysis Biological system modeling Computational modeling Graphics processing units Predictive models Data models Synthetic data

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An Online Spike Pattern Learning Algorithm Based on Local Variable Driven STDP

An Online Spike Pattern Learning Algorithm Based on Local Va...

引用

IEEE Annual computing and Communication Workshop and Conference (CCWC)

作者： Jiahua Liu Xianghong Lin Zhengyi Zhang Chun Li College of Computer Science and Engineering Northwest Normal University Lanzhou China

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Online learning algorithms adjust the synaptic weights when the neurons fire spikes during the learning process of spiking neural networks, which are shown to be more suitable and effective for processing spatio-temporal information in the real world. this paper presents an online spike pattern supervised learning algorithm for spiking neural networks based on local variable driven STDP (LVDSTDP), which combines low-pass filtering of spike trains fired by pre- and postsynaptic neurons to adjust synaptic weights in real time. the LVDSTDP algorithm has been successfully applied to spike trains learning tasks, and the effects of synaptic parameters are analyzed in detail. Experimental results show that the proposed algorithm can effectively adjust the synaptic weights to improve learning performance. this demonstrates effective for solving complex pattern learning problems using the LVDSTDP rule, and many further optimizations are possible. We also applied the proposed method in training SNNs, and obtained comparably high recognition accuracies on the MNIST datasets.

关键词： Training Accuracy Neurons Supervised learning Pattern classification Spiking neural networks Real-time systems Classification algorithms Pattern recognition Optimization

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A Big Data Optimization Comparison using Apache Spark and Apache Airflow

A Big Data Optimization Comparison using Apache Spark and Ap...

引用

IEEE Annual computing and Communication Workshop and Conference (CCWC)

作者： Taylor Hartman Samir Poudel Jiblal Upadhya Md Nahid Hasan Kritagya Upadhyay Khem Poudel Department of Computer Science Middle Tennessee State University Murfreesboro TN Computational and Data Science Middle Tennessee State University Murfreesboro TN

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

While comparisons between Apache Hadoop and Apache Spark are well-documented, there has been limited research comparing Apache Spark with Apache Airflow, especially in terms of speed and memory usage. With Apache Airflow's recent introduction of dynamic task mapping, which performs similar functions to Apache Spark's map operation, a detailed comparison between the two tools has become increasingly relevant. A comparison in these areas would provide valuable insights for the Big Data Science community, helping determine which methods are better suited for tasks requiring high speed and efficient memory usage. this study focuses on comparing the Apache Spark Map function and Apache Airflow Dynamic Task Mapping function on two key metrics: memory utilization and computation speed. Specifically, we evaluate their performance in sorting formatted electrocardiogram sensory data. We hypoth-esize that Apache Spark will demonstrate faster processing times due to its advanced in-memory processing and sorting algorithms. However, this speed advantage is expected to come with higher memory usage compared to Apache Airflow. Our findings provide actionable insights into the strengths and limitations of these tools, guiding data scientists and engineers in choosing the most suitable framework for specific big data processing tasks. these results are particularly relevant for large-scale data sorting and transformation operations, contributing to informed decision-making in the Big Data Science community.

关键词： Measurement Hands Machine learning algorithms Heuristic algorithms Pipelines Cluster computing Big Data In-memory computing Optimization Sorting

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SPADE: Enhancing Adaptive Cyber Deception Strategies with Generative AI and Structured Prompt Engineering

SPADE: Enhancing Adaptive Cyber Deception Strategies with Ge...

引用

IEEE Annual computing and Communication Workshop and Conference (CCWC)

作者： Shihab Ahmed A B M Mohaimenur Rahman Md Morshed Alam Md Sajidul Islam Sajid Department of Computer and Information Sciences Towson University Towson Maryland USA Department of Software and Information Systems UNC Charlotte Charlotte North Carolina USA School of Cybersecurity Old Dominion University Norfolk Virginia USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

the rapid evolution of modern malware presents significant challenges to the development of effective defense mechanisms. Traditional cyber deception techniques often rely on static or manually configured parameters, limiting their adaptability to dynamic and sophisticated threats. this study leverages Generative AI (GenAI) models to automate the creation of adaptive cyber deception ploys, focusing on structured prompt engineering (PE) to enhance relevance, actionability, and deploy-ability. We introduce a systematic framework (SPADE) to address inherent challenges large language models (LLMs) pose to adaptive deceptions, including generalized outputs, ambiguity, under-utilization of contextual information, and scalability constraints. Evaluations across diverse malware scenarios using metrics such as Recall, Exact Match (EM), BLEU Score, and expert quality assessments identified ChatGPT-4o as the top performer. Additionally, it achieved high engagement (93%) and accuracy (96%) with minimal refinements. Gemini and ChatGPT-4o Mini demonstrated competitive performance, with Llama3.2 showing promise despite requiring further optimization. these findings highlight the transformative potential of GenAI in automating scalable, adaptive deception strategies and underscore the critical role of structured PE in advancing real-world cybersecurity applications.

关键词： Measurement Systematics Limiting Scalability Large language models Focusing Malware Quality assessment Prompt engineering Optimization

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HEALth-ML: A Machine Learning Framework for Equity-Driven Public Health Outcome Prediction

HEALTH-ML: A Machine Learning Framework for Equity-Driven Pu...

引用

IEEE Annual computing and Communication Workshop and Conference (CCWC)

作者： Jake Ekoniak Marjan Asadinia Department of Mathematics University of California Los Angeles USA Computer Science Department California State University Northridge USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Significant health disparities persist across U.S. counties due to socioeconomic inequalities, environmental challenges, and unequal access to healthcare. Leveraging large health datasets, this study employs machine learning and advanced analytics to classify county health levels-categorized as low, medium, or high-based on environmental, health, and social factors. Emphasizing fairness, we incorporate bias mitigation techniques, ensuring the models avoid reinforcing existing disparities. Our approach includes comprehensive data preprocessing, feature engineering, and employing diverse machine learning algorithms, including Neural Networks, tree-based models, Support Vector Machines, and logistic regression, to capture complex relationships within the data. Results show accuracy scores exceeding 87% and AUC scores over 95%, highlighting the predictive strength of our models. Model performance is evaluated through accuracy, precision, recall, F1-score, and fairness metrics such as Equal Opportunity. Additionally, we assess computational efficiency and interpretability, ensuring the models are both practical and actionable. By providing accurate, fair, and interpretable predictions, this research offers a foundation for data-driven interventions, guiding policymakers toward reducing health inequalities across counties. Our study underscores the ethical imperative of fairness in public health analytics, setting a precedent for prioritizing social equity in future health data research.

关键词： Support vector machines Adaptation models Logistic regression Accuracy Computational modeling Neural networks Machine learning Predictive models Computational efficiency Public healthcare

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BSP/CGM algorithm for maximum matching in convex bipartite graphs 15

BSP/CGM algorithm for maximum matching in convex bipartite g...

引用

15th symposium on computer architecture and high performance computing

作者： Soares, J Stefanes, M Univ Sao Paulo Dept Ciencia Comp Sao Paulo SP Brazil

ISBN: (纸本)0769520464

A bipartite graph G = (V, W, E) is convex if there exists an ordering of the vertices of W such that, for each v is an element of V, the neighbors of v are consecutive in W. In this work we describe a BSP/CGM algorithm for finding a maximum matching in a convex bipartite graph. For p processors, the algorithm runs in time O((\V\/p) lg(\V\/p) lgp) and it uses O(lgp) communication rounds.

关键词： Bipartite graph computer architecture high performance computing User-generated content

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X4CP32: A new parallel/reconfigurable general-purpose processor 15

X4CP32: A new parallel/reconfigurable general-purpose proces...

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15th symposium on computer architecture and high performance computing

作者： Soares, R Azevedo, A Silva, IS Univ Fed Rio Grande Norte Dept Informat & Matemat Aplicada BR-59072970 Natal RN Brazil

ISBN: (纸本)0769520464

the X4CP32 is a parallel/reconfigurable microprocessor with 2 programming levels. Although it is a general-purpose microprocessor, it has the reliable performance of a reconfigurable architecture. this paper exposes its architecture and programming levels, and discusses the powerful interaction between parallel programming and reconfiguration. It shows two performance-optimized implementations of matrix multiplication using both parallel and reconfigurable paradigms and a parallel implementation of miner intelligent agents.

关键词： Access protocols Buildings Communication channels Communication system control computer architecture high performance computing Parallel processing Parallel programming Process control Routing

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