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检索条件"主题词=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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An Extensible Framework for Hierarchical dataflow Modeling in Spatial Accelerators

An Extensible Framework for Hierarchical Dataflow Modeling i...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Park, Dongho Choi, Su-Il Chonnam National University Department of Electronic Computer Engineering Gwangju Korea Republic of

ISBN: (纸本)9798331510756

Spatial accelerators are a promising architectural paradigm. Several previous studies have focused on modeling the dataflow of spatial accelerators using the polyhedral model. However, these studies have notable limitations: (1) difficulty in representing hierarchical structures and (2) difficulty in extending algorithms for dataflow modeling. To address these limitations, we propose the PolyLink framework, which employs a polyhedral model-based hardware description graph to effectively represent hierarchical structures and data dependencies. Our framework offers several key contributions: (1) representing complex hierarchical structures as graphs, (2) integrating a polyhedral calculator with Diplomacy, a graph-based hardware generator. We demonstrated the effectiveness of PolyLink by applying it to CONV2D operations and analyzing the memory requirements. © 2025 IEEE.

关键词： data flow graphs

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LSAFE: a Lightweight Static Analysis Framework for binary Executables

LSAFE: a Lightweight Static Analysis Framework for binary Ex...

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2024 IEEE International Performance, Computing, and Communications Conference, IPCCC 2024

作者： Yue, Xiao Qu, Guangzhi Oakland University Department of Computer Science and Engineering RochesterMI United States

ISBN: (纸本)9798350367942

Static analysis is a widely used technique for analyzing various aspects of programs. However, as programs become more complex, static analysis tools require larger resources, such as CPU time and memory, to perform the same tasks. Moreover, the source code of programs may not always be accessible, requiring static analysis to be performed on the binary executable code directly. To overcome these challenges, we propose a lightweight static analysis framework called LSAFE, which constructs control flow graphs (CFGs) and data dependency graphs (DDGs) of target programs with optimized performance in terms of CPU and memory usage. We evaluated the proposed framework using both Spec benchmark programs and real-world industrial applications, and found that it outperformed Angr, an existing state-of-the-art static analysis tool. Additionally, we demonstrate a case study that utilizes the CFG generated by LSAFE to detect memory leaks. © 2024 IEEE.

关键词： data flow graphs

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Reducing Unfairness in Distributed Community Detection 24

Reducing Unfairness in Distributed Community Detection

引用

24th IEEE International Conference on data Mining, ICDM 2024

作者： Zhang, Hao Jayaweera, Malith Ren, Bin Wang, Yanzhi Soundarajan, Sucheta Syracuse University United States Northeastern University China William & Mary United States

ISBN: (纸本)9798331506681

Big graph data mining and processing have emerged as a crucial area of study. Distributed graph frameworks are commonly employed to process such big graph data in various applications. These frameworks have proven to be highly effective in improving both the accuracy and efficiency of processing large-scale graph data, but little attention has been paid to the algorithmic fairness of such methods. In this paper, we propose a novel graph reweighting algorithm, Homophily-Based Graph Reweighting (HBGR), which can be used with different distributed community detection frameworks. The findings of our study demonstrate that HBGR can significantly enhance the fairness of detected community results, without altering the overall distributed community detection algorithm workflow. Our analysis demonstrates that HBGR outperforms traditional performance-based distributed graph data processing frameworks in terms of fairness across 13 real social network datasets. This enhancement enables us to achieve fairness levels that are comparable, or even superior, to those achieved by linear community detection algorithms while maintaining good efficiency performance. Additionally, we examine the causes of unfairness in distributed community detection algorithms and conduct an interpretability analysis of HBGR's improved fairness performance. Finally, we provide a comprehensive evaluation of the trade-offs between efficiency, accuracy, and fairness in distributed community detection algorithms. © 2024 IEEE.

关键词： data flow graphs

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A Deep Spatial-Temporal Model Based on Discrete Wavelet Transform for Traffic flow Forecasting 12

A Deep Spatial-Temporal Model Based on Discrete Wavelet Tran...

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12th International Conference on Advanced Cloud and Big data, CBD 2024

作者： Qu, Hao Lv, Zhiqiang Li, Jianbo College of Computer Science & Technology Qingdao University Qingdao China

ISBN: (纸本)9798331511074

The widespread of the COVID-19 has seriously affected the normal order of transportation. To reduce the spread of the epidemic, many countries have issued policies to restrict resident travel, resulting in high dispersion and irregularity in traffic flow data. To overcome the influence of data discreteness on traffic flow forecasting, this study adopts discrete wavelet transform to split the traffic flow data into discrete quantities, changing trends and discrete baselines. Due to the lack of analysis of the hierarchy and direction among nodes in graph structure, this study proposes a tree convolution to analyze the spatial correlation of the traffic network. To solve the problem of high discreteness of traffic flow data, this study proposes a discrete forecasting module, which is used to convert discrete quantities separated by discrete wavelet transform into high-dimensional discrete features. Then, the predicted traffic data is segmented using discrete wavelet transform, and the newly segmented traffic trends and discrete baselines are then subjected to inverse discrete wavelet transform with the discrete model predicted by the discrete forecasting module to obtain the final traffic flow forecasting result. In comparative experiments, this study is evaluated against established high-level baselines. Our model demonstrates an average improvement of 23.25 % across the selected performance metrics, and it exhibits exceptional performance in long-term predictions. © 2024 IEEE.

关键词： data flow graphs

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9th International Conference on Big data Analytics, ICBDA 2024

作者： Kumar, Amit Yoshiuchi, Hideya Hitachi Ltd. Research and Development Group Tokyo Japan

ISBN: (纸本)9798350352962

This paper presents a dedicated data management platform designed for efficiently determining material process flow, complemented by a customized big data search system tailored for Material Informatics (MI). The material manufacturing sector heavily depends on intricate process flow for product development. Material industries increasingly adopt modern technology to optimize product quality, quantity, and resource conservation, and developing new materials is intricate, demanding a deep understanding of the process flow steps. Effective resource management remains challenging for the data management platform. Process flow assists the analytical approach to developing big data management, such as data similarity identification based on user requirements. Process flow can easily be represented in a graph, making it easy for analytical analysis. Graphical representations aid in control and innovation for big data management. Our novel approach utilizes an advanced graph similarity method incorporating Hamiltonian paths and sophisticated machine learning algorithms. We have incorporated a graph similarity procedure to pinpoint control steps. In product development, finding the principal flow graph is vital. Our data management platform aids R&D in discovering efficient pathways, offering a swift response. Our method's path similarity scores matching yields 73 %, with 87% topology similarity and 86% node data matching. © 2024 IEEE.

关键词： data flow graphs

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RLSM: A Strategy Matrix based Microservice Splitting Method for Reinforcement Learning

RLSM: A Strategy Matrix based Microservice Splitting Method ...

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2024 International Conference on Intelligent Systems and Robotics, CISR 2024

作者： Liu, Yuqiang Yu, Jianyong Han, Xue Liu, Yuqi The School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China The School of Hunan Software Vocational and Technical University Xiangtan China

We introduce a novel microservice splitting approach, the Policy Matrix-based Reinforcement Learning Splitting Method (RLSM), designed to overcome the limitations of traditional service splitting schemes by providing a solution that is both fine-grained and efficient, with a strong emphasis on automation. This method utilizes dynamic link tracking and static code analysis techniques to analyze business modules, and extract entities using data flow graphs. We use these entities as agents in reinforcement learning to construct a reinforcement learning environment model, and optimize and update the value of entities by constructing a policy matrix, replacing the Q table in traditional Q-learning algorithms. Finally, we obtained a set of entity categories and used the K-means algorithm to cluster the entities of these categories, with each clustered cluster being a split individual microservice. The experimental results show that this splitting scheme not only achieves automated splitting, but also maintains a stable compliance rate of about 97% for microservices. This is enough to demonstrate that RLSM is more efficient and flexible in completing service design and evaluation. © Published under licence by IOP Publishing Ltd.

关键词： data flow graphs

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Sparsepipe: Sparse Inter-operator dataflow Architecture with Cross-Iteration Reuse 57

Sparsepipe: Sparse Inter-operator Dataflow Architecture with...

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57th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2024

作者： Zhang, Yunan Tsai, Po-An Tseng, Hung-Wei University of California Riverside United States NVIDIA United States

ISBN: (纸本)9798350350579

Sparse Tensor Algebra (STA) applications are limited by data movement and can benefit from better data reuse. Prior research has focused on intra-operator data reuse, such as better dataflow or caching technique for a single STA operation, missing other reuse opportunities at the application level. By expressing STA applications as sparse tensor dataflow graphs, we first identify two unexplored inter-operator data reuse opportunities: 1) producer-consumer reuse and 2) cross-iteration reuse. Producer-consumer reuse combines multiple operations to reduce data movement for intermediate results. Cross-iteration data reuse, a new opportunity identified in this paper, reduces the data movement for the shared sparse data (e.g., graph) across iterations. We then propose Output-stationary-Element-wise-Input-stationary (OEI) dataflow, a novel dataflow to capture both reuse opportunities in STA applications, and Sparsepipe, a sparse dataflow architecture to support the OEI dataflow and maximize data reuse. Evaluation results show that Sparsepipe with OEI dataflow is 19.82×/4.65× faster than CPU/GPU and 1.77× faster than an ideal sparse accelerator that cannot exploit inter-operator reuse. © 2024 IEEE.

关键词： data flow graphs

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Equivalence Checking for flow-Based Computing using Iterative SAT Solving 24

Equivalence Checking for Flow-Based Computing using Iterativ...

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43rd International Conference on Computer-Aided Design, ICCAD 2024

作者： Thijssen, Sven Rashed, Muhammad Rashedul Haq Ahmed, Md Rubel Singireddy, Suraj Jha, Sumit Kumar Ewetz, Rickard Department of Electrical Engineering and Computer Science Florida Atlantic University Boca RatonFL United States Department of Computer Science and Engineering University of Texas at Arlington ArlingtonTX United States Department of Electrical and Computer Engineering University of Central Florida OrlandoFL United States Department of Electrical and Computer Engineering University of Texas at San Antonio San AntonioTX United States Knight Foundation School of Computing and Information Sciences Florida International University MiamiFL United States Department of Electrical and Computer Engineering University of Florida GainesvilleFL United States

ISBN: (纸本)9798400710773

Processing in-memory is projected to shatter the von Neumann bottleneck and enable acceleration of data-intensive applications. flow-based computing is an efficient in-memory computing paradigm for accelerating the execution of Boolean logic. While recent synthesis algorithms can map complex functions into flow-based computing circuits, the functional correctness cannot be verified using state-of-the-art equivalence checking techniques. The challenge is that non-volatile memory devices are intrinsically bi-directional, which introduces cycles in the computational graph. These cycles break traditional equivalence checking methods that are based on SAT formulations. In this paper, we propose a framework for equivalence checking of flow-based computing circuits that is called flowSAT. The framework captures each circuit using an undirected computational graph. The key idea of flowSAT is to introduce helper variables, in the form of arrows, that dynamically convert the undirected graph into a directed graph. This facilitates equivalence checking to be performed using traditional SAT formulations. However, it is prohibitively expensive to ban all possible cycles using arrow variables. Therefore, we propose to eliminate cycles by iteratively adding constraints to the SAT formulation. Our experimental evaluation demonstrates that flowSAT is up to an order of magnitude faster than state-of-the-art methods. The framework is capable of verifying all 20/20 benchmark circuits, while the previous state-of-the-art technique is only capable of verifying 12/20 circuits within a time limit of one hour. © 2024 Copyright is held by the owner/author(s).

关键词： data flow graphs

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Enhanced Literature Review Visualization: A Novel Sorted Stream graphs with Integrated Word Elements 3rd

Enhanced Literature Review Visualization: A Novel Sorted Str...

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3rd International Conference on Advances in Information and Communication Technology, ICTA 2024

作者： Nguyen, The-Vinh Phung, Trung-Nghia Thai Nguyen University of Information and Communication Technology Thai Nguyen Viet Nam

ISBN: (纸本)9783031809422

Extracting meaningful insights from temporal data through visualization plays a crucial in decision-making process. Conventional visualization methods such as stream graphs and stacked area charts suffer from clarity when applying to show trends of cross-categories over time. To alleviate this limitation, we propose the Sorted Stream Graph with Embedded Word Elements (SSGEW), an innovative approach that sorts stream segments and incorporates embedded word elements into a stream graph. Through an authored algorithmic development, our method enhances the arrangement of data categories and strategically places words to improve data exploration. We compare our visual design with two traditional techniques and demonstrate our approach through a case study on the evolution of automatically generated data visualization from 2017 to 2024. Our results show a clear distinction between SSGEW and the two other designs, especially when there are many fluctuations in cross-categories. Future work could be focusing on refining the design to overcome occlusion. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data flow graphs

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