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检索条件"主题词=Data Flow Graphs"

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A data-Driven Approach to dataflow-Aware Online Scheduling for Graph Neural Network Inference 25

A Data-Driven Approach to Dataflow-Aware Online Scheduling f...

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30th Asia and South Pacific Design Automation Conference, ASP-DAC 2025

作者： Puigdemont, Pol Russo, Enrico Wassington, Axel Das, Abhijit Abadal, Sergi Palesi, Maurizio Barcelona Spain Department of Electrical Electronics and Computer Engineering Univ. of Catania Catania Italy

ISBN: (纸本)9798400706356

Graph Neural Networks (GNNs) have shown significant promise in various domains, such as recommendation systems, bioinformatics, and network analysis. However, the irregularity of graph data poses unique challenges for efficient computation, leading to the development of specialized GNN accelerator architectures that surpass traditional CPU and GPU performance. Despite this, the structural diversity of input graphs results in varying performance across different GNN accelerators, depending on their dataflows. This variability in performance due to differing dataflows and graph properties remains largely unexplored, limiting the adaptability of GNN accelerators. To address this, we propose a data-driven framework for dataflow-aware latency prediction in GNN inference. Our approach involves training regressors to predict the latency of executing specific graphs on particular dataflows, using simulations on synthetic graphs. Experimental results indicate that our regressors can predict the optimal dataflow for a given graph with up to 91.28% accuracy and a Mean Absolute Percentage Error (MAPE) of 3.78%. Additionally, we introduce an online scheduling algorithm that uses these regressors to enhance scheduling decisions. Our experiments demonstrate that this algorithm achieves up to 3.17× speedup in mean completion time and 6.26× speedup in mean execution time compared to the best feasible baseline across all datasets. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： data flow graphs

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Improving ML-based Binary Function Similarity Detection by Assessing and Deprioritizing Control flow Graph Features 33

Improving ML-based Binary Function Similarity Detection by A...

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33rd USENIX Security Symposium

作者： Wang, Jialai Zhang, Chao Chen, Longfei Rong, Yi Wu, Yuxiao Wang, Hao Tan, Wende Li, Qi Li, Zongpeng Tsinghua Univ Beijing Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China Zhongguancun Lab Beijing Peoples R China Quancheng Labs Jinan Peoples R China

ISBN: (纸本)9781939133441

Machine learning-based binary function similarity detection (ML-BFSD) has witnessed significant progress recently. They often choose control flow graph (CFG) as an important feature to learn out of functions, as CFGs characterize the control dependencies between basic code blocks. However, the exact role of CFGs in model decisions is not explored, and the extent to which CFGs might lead to model errors is unknown. This work takes a first step towards assessing the role of CFGs in ML-BFSD solutions both theoretically and practically, and promotes their performance accordingly. First, we adapt existing explanation methods to interpreting ML-BFSD solutions, and theoretically reveal that existing models heavily rely on CFG features. Then, we design a solution dCFG to manipulate CFGs and practically demonstrate the lack of robustness of existing models. We have extensively evaluated dCFG on 11 state-of-the-art (SOTA) ML-BFSD solutions, and find that the models' results would flip if we manipulate the query functions' CFGs but keep semantics, showing that most models have bias on CFG features. Our theoretic and practical assessment solutions can also serve as a robustness validator for the development of future ML-BFSD solutions. Lastly, we present a solution to utilize dCFG to augment training data, which helps deprioritize CFG features and enhance the performance of existing ML-BFSD solutions. Evaluation results show that, MRR, Recall@1, AUC and F1 score of existing models are improved by up to 10.1%, 12.7%, 5.1%, and 27.2% respectively, proving that reducing the models' bias on CFG features could improve their performance.

关键词： data flow graphs

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Adaptive Time-Varying Graph Learning for Traffic flow data Based on Anomaly Moment Detection

Adaptive Time-Varying Graph Learning for Traffic Flow Data B...

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2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference

作者： Chen, Shuhong Chen, Zewei Li, Chen Zheng, Xianwei He, Minfan Li, Xutao Shantou Univ Dept Elect Engn Key Lab Digital Signal & Image Proc Guangdong Pro Shantou 515063 Peoples R China Foshan Univ Sch Math & Big Data Foshan 52800 Peoples R China

ISBN: (纸本)9798350367331;9798350367348

This paper proposes an adaptive time-varying graph structure construction strategy for traffic flow data based on anomaly moment detection, which is capable of identifying anomaly moments within traffic flow signals and adaptively dividing the temporal dimension to construct corresponding graph representations for distinct traffic patterns. Specifically, we leverage a threshold determination method that incorporates the smoothness and energy of temporal difference traffic flow signals to identify anomaly moments in the traffic flow. Upon detecting these anomaly moments, we utilize them as segmentation boundaries along the timeline, adaptively partitioning the continuous time series into multiple dynamic and relatively stable subsequences. Within each subsequence, we apply a time-varying graph learning method to independently construct a graph structure that reflects the traffic flow characteristics of that specific period, thereby achieving an adaptive time-varying property in the traffic flow graph structure. Experimental results demonstrate that the proposed method exhibits reliable performance in detecting anomaly moments in traffic flow, and the adaptive time-varying graph structures accurately reflect the varying characteristics of traffic flow across different time periods.

关键词： data flow graphs

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Adaptive Graph Convolution in Spatio-temporal Synchronous Networks for Traffic flow Prediction 24

Adaptive Graph Convolution in Spatio-temporal Synchronous Ne...

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8th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Xiao, Quanquan Xu, Yuehai College of Information Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9798400710094

The field of traffic flow prediction is vital for smart city development and traffic management. However, accurately predicting spatio-temporal data is challenging due to its complexity and potential heterogeneity. To address this, a novel model called the spatio-temporal synchronous graph convolutional network (STASCN) has been introduced. This model effectively captures local spatio-temporal relationships and incorporates an adaptive graph learning approach to capture node-specific spatial and temporal correlations in a more focused manner. and also outperforms other methods in terms of prediction accuracy in real-life datasets. © 2024 Copyright held by the owner/author(s).

关键词： data flow graphs

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Short-term traffic flow prediction of freeway based on improved Graph Convolutional Network 4

Short-term traffic flow prediction of freeway based on impro...

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2024 4th International Conference on Intelligent Traffic Systems and Smart City, ITSSC 2024

作者： Ning, Yuefei Samonte, Mary Jane C. Li, Yanping School of Information Technology Mapua University Makati City1191 Philippines School of Information Engineering Zhengzhou Shengda University Henan Xinzheng451191 China

ISBN: (纸本)9781510686304

Predicting traffic patterns is crucial for smart transportation systems, as it aids in enhancing city traffic management levels, elevating citizens' travel experiences, supporting environmental conservation and promoting economic development. Due to the fact that the existing algorithm for graph convolutional model ignores the dynamic spatio-temporal changes in traffic flow data and does not extract sufficient spatio-temporal correlation features, we propose an alternative model that incorporates the dynamic spatio-temporal changes of traffic flow data, the attention mechanism is introduced to improve the GCN model, and the features of data about traffic flow are analyzed from the three dimensions of time, space, and spatio-temporal fusion. The model's performance is evaluated on two real datasets, PeMS04 and PeMS08, and the results show that it is superior to several types of baseline models based on various evaluation metrics. © 2025 SPIE.

关键词： data flow graphs

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Structured Methodologies vs UML Artifacts Revisited: A perspective from a developing country

Structured Methodologies vs UML Artifacts Revisited: A persp...

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2023 International Conference on ENTERprise Information Systems, CENTERIS 2023 - International Conference on Project MANagement, ProjMAN 2023 - International Conference on Health and Social Care Information Systems and Technologies, HCist 2023

作者： Alkhatib, Ghazi P.O. Box 330127 Zarqa13133 Jordan

The primary objective of this study is to examine students' perceptions and comprehension of modeling techniques in the context of structured methodology (SM) and united modeling language (UML). This investigation will make use of a fundamental system that describes a university's registration system. Students frequently encounter projects that require the use of structured techniques and the creation of artifacts like data flow diagrams and entity relationship diagrams for documentation. In contrast, the use case and object data hierarchy are incorporated into the specification of UML artifacts. The students are provided with specification diagrams and instructed to evaluate the two system specifications using a set of 20 questions for each form of modeling artifact and six questions regarding the incorporation of both modeling methodologies. Under the guidance and supervision of a faculty member, students complete the questionnaire in a classroom setting. The study's findings indicated that statistically significant variation was not observed in the vast majority of questions. This discovery provides additional evidence to support the notion that both methodologies may be utilized successfully in undergraduate courses, albeit within the context of distinct courses. This examination places a primary emphasis on the educational instruction environment. However, project managers should exercise caution before adopting Unified Modeling Language (UML) without scrutinizing the unique characteristics and requirements of the given application domain. Future research may examine the adoption of active documentation by student collectives, with an emphasis on comparing agile methodologies to Unified Modeling Language (UML) and Structured Methods (SM). © 2024 The Author(s). Published by Elsevier B.V.

关键词： data flow graphs

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Spatial-temporal Graph Transformer Network for Spatial-temporal Forecasting

Spatial-temporal Graph Transformer Network for Spatial-tempo...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Dao, Minh-Son Zetsu, Koji Hoang, Duy-Tang National Institute of Information and Communications Technology Big Data Integration Research Center Tokyo Japan

ISBN: (纸本)9798350362480

In the context of smart cities, there is a growing demand for risk-free navigation systems that help citizens avoid congestion and enjoy outdoor activities in clean environments. Such systems require the ability to predict traffic and environmental hotspots by analyzing spatial-temporal big data from various sources, including IoT devices, stations, CCTV, and personal device networks. However, analyzing and forecasting spatial-temporal data presents significant challenges due to the complex interplay of spatial and temporal dependencies. To address these challenges, we introduce a novel dual attention mechanism graph transformer that leverages both spatial and temporal information to capture intricate patterns in spatial-temporal data. We evaluate our model on two forecasting tasks: air pollution and traffic flow. Our results demonstrate superior performance compared to other graph neural network models. Consequently, this model has been integrated into a traffic-risk navigator application, which will be evaluated in Yokohama, Japan, using real data collected from air pollution stations and traffic monitors. © 2024 IEEE.

关键词： data flow graphs

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Vulnerability Detection Based on Adapter Tuning and Enhanced Feature Learning 36

Vulnerability Detection Based on Adapter Tuning and Enhanced...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Luo, Hui Lu, Lu Liang, Zhihong Suo, Siliang School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China PengCheng Laboratory Shenzhen China Electric Power Research Institute CSG Guangdong Guangzhou China Guangdong Provincial Key Laboratory of Power System Network Security Guangdong Guangzhou China

ISBN: (纸本)1891706594

Pre-trained code models have achieved promising results in the vulnerability detection field. The prevailing approach is to adapt these models with vulnerability datasets using inefficient full-model fine-tuning. These pre-trained models primarily capture the semantic features of code, while neglecting its structural characteristics. To address these issues, this paper proposes a vulnerability detection method with adapter tuning and enhanced feature learning. First, adapter modules are introduced to UniXcoder and tune only the parameters in the adapters to extract semantic features. This significantly reduces the number of training parameters and better adapts the model to downstream tasks. Then, structural features, including control and data flow information, are extracted from the Program Dependence Graph (PDG) to compensate for the limitations of pre-trained models that rely solely on semantic features. Finally, the semantic and structural features are integrated to train the detection model. The experimental results demonstrate that our proposed method outperforms state-of-the-art approaches and is highly efficient in terms of both training parameters and training data. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： data flow graphs

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Spatio-Temporal Dynamic Graph Convolutional Probability Sparse Attention Networks For Traffic flow Forecasting 14

Spatio-Temporal Dynamic Graph Convolutional Probability Spar...

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14th International Conference on Information Technology in Medicine and Education, ITME 2024

作者： Chen, Linlong Chen, Linbiao Wang, Hongyan Li, Zeping School of Big Data & Information Engineering Guiyang Institute of Humanities and Technology Guiyang China Lanzhou University of Technology College of Computer & Communication Lanzhou China Big Data & Intelligent Engineering School Chongqing College of International Business and Economics Chongqing China

ISBN: (纸本)9798350356670

Accurate traffic flow forecasting plays a vital role in the effective management and control of intelligent transportation systems. However, existing forecasting methods face constraints due to insufficient information sharing and a limited understanding of global temporal relationships, impeding a thorough analysis of temporal and spatial features. To address this issue, we propose a novel approach called ST-DGCPSA (Spatio-Temporal Dynamic Graph Probabilistic Sparse Attention Networks) for traffic flow forecasting. This framework serves as a general forecasting solution, effectively extracting the temporal and spatial characteristics of traffic flow. Firstly, a temporal graph convolution block is constructed, treating historical time slots as graph nodes and employing graph convolution to capture flexible global temporal dependencies. Next, a dynamic graph constructor is designed to explore spatial correlations between nodes and dynamic temporal dependencies across different time points, fully extracting dynamic spatio-temporal relationships. Lastly, the spatio-temporal convolution block enhances the model's ability to capture spatial correlations, temporal dependencies, and multi-scale modeling through the probabilistic sparse attention mechanism, leading to improved forecasting accuracy. Experimental results on four real datasets demonstrate the superiority of ST-DGCPSA over existing techniques. © 2024 IEEE.

关键词： data flow graphs

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An Architectural Template for FPGA Overlays Targeting data flow Applications

An Architectural Template for FPGA Overlays Targeting Data F...

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1st International Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Drewes, Anna Burtsev, Vitalii Gurumurthy, Bala Wilhelm, Martin Broneske, David Saake, Gunter Pionteck, Thilo Otto von Guericke Univ Magdeburg Germany

ISBN: (纸本)9798350364613;9798350364606

In this paper, we present a flexible and scalable architectural template for designing domain-specific FPGA overlays. The template can be parametrized in terms of number and size of tiles, communication topology and connectivity to external RAM. In addition to direct streaming connections between adjacent tiles for bulk data, it uses a novel lightweight packet based on-chip network for small data transfers. The tiles consist of configurable routing resources and the actual compute units, which are exchanged at runtime by means of dynamic partial reconfiguration. This allows the assembly of custom, varying data paths for processing data flow graphs in hardware. Since every connection within and at the edge of the template is AXI-Stream-compliant, it is highly extensible using standard IP cores and streaming-based High-Level Synthesis code, enabling plug-andplay deployment. The feasibility of our approach is demonstrated by means of an domain-specific overlay for analytical database query processing on a Xilinx Alveo U280 card. TPC-H queries and synthetic workloads are used as test cases. Insights from this demonstration system show that the resulting overlay does not affect the performance of the compute units and show the effectiveness of the lightweight packet-based on-chip network in saving routing resources.

关键词： FPGA Overlay Architectural Template Dynamic Partial Reconfiguration data flow graphs Analytical Query Processing

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