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检索条件"任意字段=21st International Symposium on Computer Architecture and High Performance Computing"

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Delinquent Loop Pre-execution Using Predicated Helper Threads 31

Delinquent Loop Pre-execution Using Predicated Helper Thread...

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31st IEEE international symposium on high performance computer architecture, HPCA 2025

作者： Seshadri, Anirudh Rotenberg, Eric North Carolina State University Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798331506476

Branch pre-execution targets delinquent branches that are not predictable by conventional branch predictors. Helper threads attempt to resolve branches ahead of the main thread. Pre-executed branch outcomes are communicated to the main thread's fetch unit via a global branch queue or local branch queues (one per branch PC). Two key challenges are discussed in this paper. 1) Handling a delinquent branch b2 that is control-dependent on another delinquent branch b1. Prior works that include both branches resort to branch prediction of b1 in the helper thread to determine whether or not to pre-execute b2. But b1 is hard-to-predict and the misprediction bottleneck merely shifts from the main thread to the helper thread. 2) Handling a store instruction that both influences a delinquent branch and is control-dependent on it. We propose predicated helper threads (Phelps) to address these challenges. Phelps constructs a helper thread for each inner loop containing delinquent branches. All delinquent branches, even control-dependent ones (b2), are unconditionally pre-executed in each loop iteration. Per-branch queues are managed in lockstep based on loop iterations, allowing the helper thread to deposit outcomes for both b1 and b2 each iteration and the main thread to consume or ignore b2 outcomes in the correct sequence dictated by b1. The helper thread also retains influential stores for dynamic disambiguation and store-load forwarding. Any such store that is control-dependent on a delinquent branch is predicated on the branch's outcome, which is necessary because the helper thread no longer has control-flow (except for the loop branch). Phelps also features dual decoupled helper threads for outer-inner loop pairs, for effective branch pre-execution when the inner loop has a short and unpredictable trip count. © 2025 IEEE.

关键词： Parallel processing systems

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An Attention Model Based Approach for Leakage Detection in Water Distribution Networks Using Normal Pressure Data 21st

An Attention Model Based Approach for Leakage Detection in...

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21st Pacific Rim international Conference on Artificial Intelligence, PRICAI 2024

作者： Luo, Juan Zhou, Du Wang, Chongxiao Yang, Jielong Zhong, Xionghu Hunan University Changsha China Hunan Construction Investment Group Co. Ltd. Changsha China Institute of High Performance Computing Agency for Science Technology and Research Singapore Singapore Jiangnan University Wuxi China

ISBN: (纸本)9789819601271

In the past, machine learning methods such as Convolutional Neural Networks (CNN) and Autoencoder (AE) have been employed for leakage detection in water distribution networks (WDNs). However, these methods primarily rely on local pressure information and often fail to capture long-term dependencies in pressure series. In this paper, we propose a transformer based model to detect leakages in WDNs. This model incorporates an attention mechanism to learn data distributions and account for correlations between historical and daily interval pressures, thereby emphasizing long-term dependencies in the pressure series. The performance of the proposed method is evaluated using detection accuracy and F1-score. Experimental studies conducted on simulated pressure datasets from two different WDNs demonstrate that the transformer based model significantly outperforms traditional CNN methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Information leakage

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Cambricon-DG: An Accelerator for Redundant-Free Dynamic Graph Neural Networks Based on Nonlinear Isolation 31

Cambricon-DG: An Accelerator for Redundant-Free Dynamic Grap...

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31st IEEE international symposium on high performance computer architecture, HPCA 2025

作者： Yue, Zhifei Song, Xinkai Liu, Tianbo Hu, Xing Zhang, Rui Du, Zidong Li, Wei Guo, Qi Chen, Tianshi University of Science and Technology of China China Institute of Computing Technology Chinese Academy of Sciences SKLP China Shanghai Innovation Center for Processor Technologies SHIC China Cambricon Technologies China

ISBN: (纸本)9798331506476

Dynamic Graph Neural Networks (DGNNs) have demonstrated significant potential in handling temporal graph-structured data. Typical DGNNs combine GNNs to process the structural information of graph and RNNs to capture the dynamic temporal information of graphs. However, current GNN accelerators process DGNNs by treating graph snapshots as static entities, leading to substantial redundant computations and memory accesses. While some DGNN accelerators have attempted to mitigate this redundancy, they face the challenge of maintaining accuracy for incremental processing due to the nonlinear activation function in GNNs. To address these issues, we propose a novel nonlinear isolation mechanism for incremental processing of DGNNs, which eliminates all redundant operations without accuracy loss. Additionally, we introduce a vertex-wise computation scheme for RNNs to minimize redundancy and reduce off-chip memory access. To validate the effectiveness of the mechanism, we implemented software optimizations on GPUs, achieving an 8.53 × performance improvement over full-graph computation schemes. To address the inefficiency of graph topology operations on GPUs, we propose the Cambricon-DG accelerator, which features a dedicated hardware pipeline, an efficient topology management engine for graph traversal and topology sorting, and a hybrid computation engine that supports both GNN and RNN computations, thus improving hardware utilization. We evaluate Cambricon-DG on seven real-world dynamic graph datasets using five representative DGNN models. The results show that Cambricon-DG achieves an average speedup of 49.73 ×, 12.15 ×, and 7.03 ×, and an average energy saving of 41.31 ×, 9.47 ×, and 5.23 × over the state-of-the-art static/dynamic GNN accelerators I-GCN, RACE, and DeltaGNN, respectively. © 2025 IEEE.

关键词： Graph neural networks

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2015 IEEE 21st international symposium on high performance computer architecture, HPCA 2015

2015 IEEE 21st International Symposium on High Performance C...

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2015 21st IEEE international symposium on high performance computer architecture, HPCA 2015

ISBN: (纸本)9781479989300

The proceedings contain 55 papers. The topics discussed include: exploring architectural heterogeneity in intelligent vision systems;increasing multicore system efficiency through intelligent bandwidth shifting;exploiting compressed block size as an indicator of future reuse;talus: a simple way to remove cliffs in cache performance;priority-based cache allocation for throughput processors;bamboo ECC: strong, safe, and flexible codes for reliable computer memory;heterogeneous memory architectures: a HW/SW approach for mixing die-stacked and off-package memories;domain knowledge based energy management in handhelds;GPU voltage noise: characterization and hierarchical smoothing of spatial and temporal voltage noise interference in GPU architectures;and hierarchical private/shared classification: the key to simple and efficient coherence for clustered cache hierarchies.

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A Reconfigurable stream-Based FPGA Accelerator for Bayesian Confidence Propagation Neural Networks 21st

A Reconfigurable Stream-Based FPGA Accelerator for Bayesian...

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21st international symposium on Applied Reconfigurable computing, ARC 2025

作者： Al Hafiz, Muhammad Ihsan Ravichandran, Naresh Lansner, Anders Herman, Pawel Podobas, Artur KTH Royal Institute of Technology Stockholm Sweden Stockholm University Stockholm Sweden Stockholm Sweden Digital Futures Stockholm Sweden International Research Centre for Neurointelligence University of Tokyo Tokyo Japan

ISBN: (纸本)9783031879944

Brain-like algorithms are attractive and emerging alternatives to classical deep learning methods for use in various machine learning applications. Brain-like systems can feature local learning rules, both unsupervised/semi-supervised learning and different types of plasticity (structural/synaptic), allowing them to potentially be faster and more energy-efficient than traditional machine learning alternatives. Among the more salient brain-like algorithms are Bayesian Confidence Propagation Neural Networks (BCPNNs). BCPNN is an important tool for both machine learning and computational neuroscience research, and recent work shows that BCPNN can reach state-of-the-art performance in tasks such as learning and memory recall compared to other models. Unfortunately, BCPNN is primarily executed on slow general-purpose processors (CPUs) or power-hungry graphics processing units (GPUs), reducing the applicability of using BCPNN in Edge systems, among others. In this work, we design a reconfigurable stream-based accelerator for BCPNN using Field-Programmable Gate Arrays (FPGA) using Xilinx Vitis high-Level Synthesis (HLS) flow. Furthermore, we model our accelerator’s performance using first principles, and we empirically show that our proposed accelerator (full-featured kernel non-structural plasticity) is between 1.3x - 5.3x faster than an Nvidia A100 GPU while at the same time consuming between 2.62x - 3.19x less power and 5.8x - 16.5x less energy without any degradation in performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Enhance statistical Features with Changepoint Detection for Driver Behaviour Analysis 21st

Enhance Statistical Features with Changepoint Detection for ...

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21st Pacific Rim international Conference on Artificial Intelligence, PRICAI 2024

作者： Maktoubian, Jamal Tran, Son N. Shillabeer, Anna Amin, Muhammad Bilal Sambrooks, Lawrence School of Information and Communication Technology University of Tasmania Hobart Australia School of Information Technology Deakin University VIC Australia School of Computing Mathematics and Engineering Charles Sturt University NSW Australia

ISBN: (纸本)9789819601240

Driver behaviour modelling is a critical field that addresses complex and dynamic driving behaviours on roads with the goal of enhancing road safety, reducing air pollution, and improving vehicle performance. Recent advancements in sensor technology and machine learning (ML) techniques have facilitated the capture and analysis of driver behaviour patterns. Nonetheless, the efficacy of ML models heavily relies on the quality of the data used. Therefore, developing feature extraction techniques that provide high-quality inputs is crucial. In this paper, we conceptualised, implemented, and evaluated a novel feature model called Changepoint-based statistical Feature (C-bSF). Initially, we extracted various statistical functions from raw sensor data, which were then aggregated using lagging windows. Following this, a changepoint detection method was used to derive the C-bSF feature. We compared the performance metrics of this new approach with other feature extraction methods, demonstrating the superiority of C-bSF in driver behaviour classification tasks across three datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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False Positive Detection for Text-Based Person Retrieval 21st

False Positive Detection for Text-Based Person Retrieval

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21st Pacific Rim international Conference on Artificial Intelligence, PRICAI 2024

作者： Cao, Yan Lu, Jun College of Computer Science and Technology Heilongjiang University Harbin150080 China Jiaxiang Industrial Technology Research Institute of Heilongjiang University Shandong Jining272400 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Heilongjiang University Harbin150080 China

ISBN: (纸本)9789819601189

Text-based person retrieval (TBPR) is a challenging topic in cross-modal retrieval tasks, aiming to query corresponding person images based on textual descriptions. This task is complicated by noisy correspondences between images and text due to incorrect text annotations and low-quality images. Although many robust noise learning methods have been proposed with satisfactory performance, they tend to directly filter out a portion of the noise in the dataset, which decreases the ability to distinguish the noise as the number of training epochs increases. This paper proposes a noise-robust learning strategy based on false positive detection (FPD). Precisely, FPD consists of two main components: 1) The key-value library (KVL) module utilizes a set of clean samples, assigns confidence weights to each training sample during the training process, and adaptively adjusts the contribution of each sample to reduce the accumulation of noise. 2) To improve the performance of TBPR, this paper designs a local token-based selection method, which utilizes the contribution of each local token to the weights in the self-attention matrix and selects the local tokens with vital information as global representations. Extensive experiments are conducted on three datasets (CUHK-PEDES, ICFG-PEDES, and RstPReID) to evaluate the performance and robustness of FPD under noisy conditions. The experimental results show that the FPD has better results at high noise ratios, and all the metrics outperform the state-of-the-art method under the RstPReID dataset with a noise ratio of 80%, where Rank-1 outperforms the best method by 2.28%, 1.64% for mAP, and 1.92% for mINP. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image annotation

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21st international symposium on high performance computing Systems and Applications, HPCS 2007

21st International Symposium on High Performance Computing S...

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21st international symposium on high performance computing Systems and Applications, HPCS 2007

ISBN: (纸本)0769528139

The proceedings contain 29 papers. The topics discussed include: on the programming impact of multi-core, multi-processor nodes in MPI clusters;an adaptive double-layer workflow scheduling approach for grid computing;improved grid metascheduler design using the plackett-burman methodology;effect of medium dispersivity on the viscous fingering instability in porous media;observations on greedy composite Newton methods;parallel computation of skyline queries;selection algorithm for grid services based on a quality of service metric;construction of a webportal and user management framework for grid;secured grid information service and performance study;utilizing averaged configurations from molecular dynamics simulation trajectories;an improved job co-allocation strategy in multiple HPC clusters;and a language-independent API for unstructured mesh access and manipulation.

关键词： Supercomputers

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Non-Ergodic Theory vs. Fractal Geometry in Organizational Learning and Dynamic Skills at a Healthcare Food Catering Service 21st

Non-Ergodic Theory vs. Fractal Geometry in Organizational Le...

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21st international symposium on Distributed computing and Artificial Intelligence, DCAI 2024

作者： Lima, Rui Marreiros, Goreti Fdez-Riverola, Florentino Vicente, Henrique Neves, José CINBIO Universidade de Vigo Department of Computer Science ESEI – Escuela Superior de Ingeniería Informática Ourense32004 Spain SERGAS-UVIGO Vigo Spain Instituto Politécnico de Saúde do Norte CESPU Famalicão Paredes Portugal Departamento de Engenharia Informática Instituto Superior de Engenharia do Porto Porto Portugal Departamento de Química e Bioquímica Escola de Ciências e Tecnologia REQUIMTE/LAQV Universidade de Évora Évora Portugal Centro Algoritmi/LASI Universidade do Minho Braga Portugal

ISBN: (纸本)9783031809453

This paper examines the adoption of non-Ergodic Theory and Fractal Geometry to enhance organizational learning in healthcare food catering. It critiques conventional linear models, promoting an understanding of varied learning processes across organizational strata. Non-Ergodic Theory emphasizes the diversity of learning among individuals and groups, while Fractal Geometry examines learning as self-similar patterns at multiple scales. This work analyses how employee diversity, knowledge sharing, and cross-level collaboration contribute to a fractal learning environment that supports adaptability and innovation. Findings indicate that implementing these theoretical models can enhance service delivery and customer approval by promoting a culture of relentless education and creative ideas. For managers and practitioners, this study provides a blueprint for enhancing learning processes that can lead to greater efficiency and performance in healthcare food services, suggesting a strategic embrace of employee diversity and interconnected learning patterns for long-term organizational success. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Fractals

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Advancing Smart Urban Futures in the Healthcare Sector 21st

Advancing Smart Urban Futures in the Healthcare Sector

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21st international symposium on Distributed computing and Artificial Intelligence, DCAI 2024

作者： Alves, Vitor Marreiros, Goreti Fdez-Riverola, Florentino Neves, José Ribeiro, Jorge Vicente, Henrique ADiT-LAB Instituto Politécnico de Viana do Castelo Viana do Castelo Portugal Department of Computer Science ESEI – Escuela Superior de Ingeniería Informática CINBIO Universidade de Vigo Ourense32004 Spain SERGAS-UVIGO Vigo Spain Departamento de Engenharia Informática Instituto Superior de Engenharia do Porto Porto Portugal Centro Algoritmi/LASI Universidade do Minho Braga Portugal Instituto Politécnico de Saúde do Norte CESPU Famalicão Portugal Departamento de Química e Bioquímica Escola de Ciências e Tecnologia REQUIMTE/LAQV Universidade de Évora Evora Portugal

ISBN: (纸本)9783031809453

As urban development stands on the brink of a new epoch, the imperative exploration of artificial intelligence, logic programming, and entropy demands visionary planning, ethical conduct, and a deep appreciation of their far-reaching impact when synergized within smart city frameworks. This study delves into the complex relationship between artificial intelligence, logic programming, and entropy, highlighting their pivotal role in advancing smart cities towards operational excellence and evolutionary success. By harnessing artificial intelligence’s understanding of human emotions and sentiments, urban centers are redefining city living. Employing cutting-edge technologies not only empowers healthcare innovation but also improves city planning, public safety, transit systems, and environmental management, significantly enhancing the quality of life for citizens. The key finding of this study underscores the importance of addressing challenges inherent in such integration, particularly in healthcare, leading to a high degree of citizens’ wellbeing. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Environmental management systems

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