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IMGA: Efficient In-Memory Graph Convolution Network Aggregation With Data Flow Optimizations

IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF integrated circuitS AND SYSTEMS 2023年第12期42卷 4695-4705页

作者： Wei, Yuntao Wang, Xueyan Zhang, Shangtong Yang, Jianlei Jia, Xiaotao Wang, Zhaohao Qu, Gang Zhao, Weisheng Beihang Univ Sch Integrated Circuit Sci & Engn MIIT Key Lab Spintron Beijing 100191 Peoples R China Beihang Univ Sch Comp Sci & Engn State Key Lab Software Dev Environm BDBC Beijing 100191 Peoples R China Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA Univ Maryland Inst Syst Res College Pk MD 20742 USA

Aggregating features from neighbor vertices is a fundamental operation in graph convolution network (GCN). However, the sparsity in graph data creates poor spatial and temporal locality, causing dynamic and irregular memory access patterns and limiting the performance of aggregation on the Von Neumann architecture. The emerging processing-in-memory (PIM) architecture is based on emerging nonvolatile memory (NVM), like spin-orbit torque magnetic RAM (SOT-MRAM), and demonstrates promising prospects in alleviating the Von Neumann bottleneck. However, the limited memory capacity of PIM medium still incurs non-negligible data movements between PIM architecture and external memory. To solve this challenge, we propose an SOT-MRAM-based in-memory computing architecture, called IMGA, for efficient in-situ graph aggregation. Specifically, we design adaptive data flow management strategies that reuse vertex data in MRAM when processing graphs of different scales and adopt edge data as the control signal source to utilize the graph's structural information. A reordering optimization strategy leveraging hardware-software co-design principle is proposed to further reduce the costly data movement. Experimental results demonstrate that IMGA achieves an average 2523x and 21x speedup, and 1.03E+6 and 1.04E+3 energy efficiency compared with CPU and GPU, respectively.

关键词： Data flow graph aggregation processing in memory (PIM) spin-orbit torque magnetic RAM (SOT-MRAM)

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Accelerating Graph-Connected Component Computation With Emerging Processing-In-Memory Architecture

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF integrated circuitS AND SYSTEMS 2022年第12期41卷 5333-5342页

作者： Chen, Xuhang Wang, Xueyan Jia, Xiaotao Yang, Jianlei Qu, Gang Zhao, Weisheng Beihang Univ Sch Integrated Circuit Sci & Engn MIIT Key Lab Spintron Beijing 100191 Peoples R China Beihang Univ Beihang Hangzhou Innovat Inst Yuhang Hangzhou 310023 Peoples R China Beihang Univ Sch Comp Sci & Engn State Key Lab Software Dev Environm NLSDE BDBC Beijing 100191 Peoples R China Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA

Computing the connected component (CC) of a graph is a basic graph computing problem, which has numerous applications like graph partitioning and pattern recognition. Existing methods for computing CC suffer from memory wall problems because of the frequent data transmission between CPU and memory. To overcome this challenge, in this article, we propose to accelerate CC computation with the emerging processing-in-memory (PIM) architecture through an algorithm-architecture co-design manner. The innovation lies in computing CC with bitwise logical operations (such as AND and OR), and the customized data flow management methods to accelerate computation and reduce energy consumption. As a proof of concept, experimental results with computational spin-transfer torque magnetic RAM (STT-MRAM) arrays demonstrate on average 19.8x and 12.4x speedups compared with the CPU and GPU implementations, and a 35.4x energy efficiency improvement over the CPU implementation. Moreover, we investigate the potential associations between graph computing and bitwise Boolean logic, which could help design more general in-memory graph computing accelerators in the future.

关键词： Bitwise logical operations connected component (CC) computation data flow processing-in-memory (PIM) architecture

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Triangle Counting Accelerations: From Algorithm to In-Memory Computing Architecture

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IEEE TRANSACTIONS ON COMPUTERS 2022年第10期71卷 2462-2472页

作者： Wang, Xueyan Yang, Jianlei Zhao, Yinglin Jia, Xiaotao Yin, Rong Chen, Xuhang Qu, Gang Zhao, Weisheng Beihang Univ Sch Integrated Circuit Sci & Engn MIIT Key Lab Spintron Beijing 100191 Peoples R China Beihang Univ Sch Comp Sci & Engn State Key Lab Software Dev Environm NLSDE BDBC Beijing 100191 Peoples R China Chinese Acad Sci Inst Informat Engn Beijing 100049 Peoples R China Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA Univ Maryland Inst Syst Res College Pk MD 20742 USA

Triangles are the basic substructure of networks and triangle counting (TC) has been a fundamental graph computing problem in numerous fields such as social network analysis. Nevertheless, like other graph computing problems, due to the high memory-computation ratio and random memory access pattern, TC involves a large amount of data transfers thus suffers from the bandwidth bottleneck in the traditional Von-Neumann architecture. To overcome this challenge, in this paper, we propose to accelerate TC with the emerging processingin-memory (PIM) architecture through an algorithm-architecture co-optimization manner. To enable the efficient in-memory implementations, we come up to reformulate TC with bitwise logic operations (such as AND), and develop customized graph compression and mapping techniques for efficient data flow management. With the emerging computational Spin-Transfer Torque Magnetic RAM(STT-MRAM) array, which is one of the most promising PIM enabling techniques, the device-to-architecture co-simulation results demonstrate that the proposed TC in-memory accelerator outperforms the state-of-the-art GPU and FPGA accelerations by 12.2 x and 31.8 x, respectively, and achieves a 34 x energy efficiency improvement over the FPGA accelerator.

关键词： Triangle counting acceleration processing-in-memory algorithm-architecture co-design graph computing

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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Paas-platform for mobile health

Paas-platform for mobile health

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5th International Conference on Computational Intelligence and Communication Networks, CICN 2013

作者： Chang, Long Guan, Cong Xu, Jihui Xu, Huaiyu Integrated Circuit Applied Software Lab Software College Northeastern University Shenyang 110004 China Service Science Research Center Shanghai Advanced Research Institute Chinese Academy of Sciences China

ISBN: (纸本)9780768550695

With the development of communication technology, every industry has change lot. The medical industry has the same trend at this time. In order to create an environment for different social department, this paper presents a new solution which PaaS is used for mobile health. In the PaaS-Platform, distributed storage and cluster technology have been used for providing better service. The PaaS-Platform gives some permission for some departments to develop their own SaaSPlatform. And different mobile clients will be developed based on different departments' needs. Means of communication take Web Service which is the idea of SOA so that the platform is compatible with a variety of platforms. As a validation of our framework, a mobile client is presented in this paper. © 2013 IEEE.

关键词： Web services

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The simulation of traction and braking performance for high-speed railway virtual reality system

The simulation of traction and braking performance for high-...

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5th International Conference on Computational Intelligence and Communication Networks, CICN 2013

作者： Guan, Cong Chang, Long Xu, Huaiyu Integrated Circuit Applied Software Lab Software College Northeastern University Shenyang 110004 China Service Science Research Center Shanghai Advanced Research Institute Chinese Academy of Sciences China

ISBN: (纸本)9780768550695

A high-speed railway virtual reality system can vividly show the train running process, traction and braking control performance using 3D computer graphics technology. As a major part of virtual reality system, the traction and braking performance have great significance to the effect of the rain running. This paper researches the basic theory of train traction and braking calculation, which can be used to simulate the train running in the virtual reality system. It also introduces the realization process of the virtual simulation based on Direct3D. © 2013 IEEE.

关键词： Braking

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Based on fiber-optic sensor and the light intensity changes vehicle dynamic weighing system

Based on fiber-optic sensor and the light intensity changes ...

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5th International Conference on Computational Intelligence and Communication Networks, CICN 2013

作者： Wang, Linlin Hu, Xue Huang, Yongjian Xu, Huaiyu Integrated Circuit Applied Software Lab Software College Northeastern University Shenyang 110004 China Service Science Research Center Shanghai Advanced Research Institute Chinese Academy of Sciences China

ISBN: (纸本)9780768550695

This paper mainly discusses the structures in the laboratory of a fiber-optic WIM system. This system is based on the feature of the fiber that when it vertically pressed by heavy objects the light intensity of the fiber change with them. This experiment use CCD camera, splitter and other professional optical equipment to capture the light intensity changes video. And then, use computer to obtain the image from the video stream, grayscale process, binary process, corrosion process and expansion process the image to calculate the light intensity. The experimental results reflect some regular relationship of the experimental vehicle weight and the intensity of the fiber and speed of the vehicle. © 2013 IEEE.

关键词： Fibers

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A LOD algorithm for terrain-load in the scene of high-speed railway virtual reality based on quad-tree

A LOD algorithm for terrain-load in the scene of high-speed ...

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5th International Conference on Computational Intelligence and Communication Networks, CICN 2013

作者： Li, Yang Sun, Shiyao Xu, Huaiyu Integrated Circuit Applied Software Lab Software College Northeastern University Shenyang 110004 China Service Science Research Center Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai China

ISBN: (纸本)9780768550695

LOD (Levels of Details) is a technology often applied in real-time photo realistic, which reduces the complexity of scene preferably. At the same time, the distortion is lessened and the request of real time is met. A frequency used algorithm of LOD in lots of literatures is based on quad tree. This paper discusses the development of LOD technology, introduces LOD algorithm based on quad-tree, and realizes the loading terrain module in the scene of high-speed railway virtual reality. In the end, an ulterior development of LOD is referred. © 2013 IEEE.

关键词： Trees (mathematics)

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Micro-blogging system based on geographic information in google map

Micro-blogging system based on geographic information in goo...

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4th International Conference on Computational Intelligence and Communication Networks, CICN 2012

作者： You, Zhiqiang Xu, Huaiyu Xu, Jihui Xu, Jinfeng Shi, Dayu Integrated Circuit Applied Software Lab. Software College Northeastern University Shenyang 110004 China

ISBN: (纸本)9781467329811

This paper discussed the development of domestic Micro-blogging and analyzed the status in the future. Moreover, it involves a number of technical difficulties encounters, including how to make further development based on the open source framework Status Net, and how to create HTML language based on object-oriented programming using PHP and how to use Google Map API. Then, we combine the requirements of Micro-blogging and Google map service to build the system architecture and implement this system. In this system you can view information, distribute information, reply information, forward information and search information according to Google Map service. And the most important is that our system provides a platform that enables users to communicate with the Google map, and allows users to locate any place to send messages to increase the users' experience and flexibility. © 2012 IEEE.

关键词： Object oriented programming

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