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Attar: RRAM-based in-memory attention accelerator with software-hardware co-optimization

Science China(Information Sciences) 2025年第3期68卷 371-387页

作者： Bing LI Ying QI Ying WANG Yinhe HAN Information Engineering College Capital Normal University Research Center for Intelligent Computing Systems Institute of Computing TechnologyChinese Academy of Sciences

The attention mechanism has become a pivotal component in artificial intelligence, significantly enhancing the performance of deep learning applications. However, its quadratic computational complexity and intricate computations lead to substantial inefficiencies when processing long sequences. To address these challenges, we introduce Attar, a resistive random access memory(RRAM)-based in-memory accelerator designed to optimize attention mechanisms through software-hardware co-optimization. Attar leverages efficient Top-k pruning and quantization strategies to exploit the sparsity and redundancy of attention matrices, and incorporates an RRAM-based in-memory softmax engine by harnessing the versatility of the RRAM crossbar. Comprehensive evaluations demonstrate that Attar achieves a performance improvement of up to 4.88× and energy saving of 55.38% over previous computing-in-memory(CIM)-based accelerators across various models and datasets while maintaining comparable accuracy. This work underscores the potential of in-memory computing to enhance the efficiency of attention-based models without compromising their effectiveness.

关键词： RRAM computing-in-memory attention pruning quantization

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Enhanced electrode-level diagnostics for lithium-ion battery degradation using physics-informed neural networks

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Journal of Energy Chemistry 2025年第5期104卷 618-627页

作者： Rui Xiong Yinghao He Yue Sun Yanbo Jia Weixiang Shen National Engineering Research Center of Electric Vehicles School of Mechanical Engineering Beijing Institute of Technology School of Science Computing and Engineering Technologies Swinburne University of Technology

For the diagnostics and health management of lithium-ion batteries, numerous models have been developed to understand their degradation characteristics. These models typically fall into two categories:data-driven models and physical models, each offering unique advantages but also facing ***-informed neural networks(PINNs) provide a robust framework to integrate data-driven models with physical principles, ensuring consistency with underlying physics while enabling generalization across diverse operational conditions. This study introduces a PINN-based approach to reconstruct open circuit voltage(OCV) curves and estimate key ageing parameters at both the cell and electrode *** parameters include available capacity, electrode capacities, and lithium inventory capacity. The proposed method integrates OCV reconstruction models as functional components into convolutional neural networks(CNNs) and is validated using a public dataset. The results reveal that the estimated ageing parameters closely align with those obtained through offline OCV tests, with errors in reconstructed OCV curves remaining within 15 mV. This demonstrates the ability of the method to deliver fast and accurate degradation diagnostics at the electrode level, advancing the potential for precise and efficient battery health management.

关键词： Ageing diagnosis Convolutional neural networks Electrode level Lithium-ion batteries Physics-informed neural network

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Exploring & exploiting high-order graph structure for sparse knowledge graph completion

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Frontiers of Computer Science 2025年第2期19卷 31-42页

作者： Tao HE Ming LIU Yixin CAO Zekun WANG Zihao ZHENG Bing QIN Research Center for Social Computing and Information Retrieval Harbin Institute of TechnologyHarbin 150001China Peng Cheng Laboratory Shenzhen 518000China SMU School of Computing and Information Systems Singapore Management UniversitySingapore 178902Singapore

Sparse Knowledge Graph(KG)scenarios pose a challenge for previous Knowledge Graph Completion(KGC)methods,that is,the completion performance decreases rapidly with the increase of graph *** problem is also exacerbated because of the widespread existence of sparse KGs in practical *** alleviate this challenge,we present a novel framework,LR-GCN,that is able to automatically capture valuable long-range dependency among entities to supplement insufficient structure features and distill logical reasoning knowledge for sparse *** proposed approach comprises two main components:a GNN-based predictor and a reasoning path *** reasoning path distiller explores high-order graph structures such as reasoning paths and encodes them as rich-semantic edges,explicitly compositing long-range dependencies into the *** step also plays an essential role in densifying KGs,effectively alleviating the sparse ***,the path distiller further distills logical reasoning knowledge from these mined reasoning paths into the *** two components are jointly optimized using a well-designed variational EM *** experiments and analyses on four sparse benchmarks demonstrate the effectiveness of our proposed method.

关键词： knowledge graph completion graph neural networks reinforcement learning

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Revealing the effect of external pressure on solid-electrolyte interphase and lithium plating in lithium-ion batteries

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Journal of Energy Chemistry 2025年第3期102卷 734-744页

作者： Rui Xiong Haocheng Li Bing-Ang Mei Hongwen He Weixiang Shen National Engineering Research Center of Electric Vehicles School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 00081China School of Science Computing and Engineering TechnologiesSwinburme University of TechnologyHawthornVictoria 3122Australia

Lithium plating is a detrimental phenomenon in lithium-ion cells that compromises both functionality and *** study investigates electro-chemo-mechanical behaviors of lithium plating in lithium iron phosphate pouch cells under different external *** force microscopy nanoindentation is performed on the graphite electrode to analyze the influence of external pressure on solid-electrolyte interphase(SEI),revealing that the mechanical strength of SEI,indicated by Young's modulus,increases with the presence of external ***,an improved phase field model for lithium plating is developed by incorporating electrochemical parameterization based on nonequilibrium *** results demonstrate that higher pressure promotes lateral lithium deposition,covering a larger area of ***,electrochemical impedance spectroscopy and thickness measurements of the pouch cells are conducted during overcharge,showing that external pressure suppresses gas generation and thus increases the proportion of lithium deposition among galvanostatic overcharge *** integrating experimental results with numerical simulations,it is demonstrated that moderate pressure mitigates SEI damage during lithium plating,while both insufficient and excessive pressure may exacerbate *** study offers new insights into optimizing the design and operation of lithium iron phosphate pouch cells under external pressures.

关键词： Lithium-ion batteries Overcharge Lithium plating Electrochemical mechanics Synergistic effects

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Improving Multi-task GNNs for Molecular Property Prediction via Missing Label Imputation

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Machine Intelligence research 2025年第1期22卷 131-144页

作者： Fenyu Hu Dingshuo Chen Qiang Liu Shu Wu University of Chinese Academy of Sciences Beijing100190China Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence SystemsInstitute of AutomationChinese Academy of SciencesBeijing100190China

The prediction of molecular properties is a fundamental task in the field of drug ***,graph neural networks(GNNs)have been gaining prominence in this *** a molecule tends to have multiple correlated properties,there is a great need to develop the multi-task learning ability of ***,limited by expensive and time-consuming human annotations,collecting complete labels for each task is *** a result,most existing benchmarks involve many missing labels in training data,and the performance of GNNs is impaired due to the lack of sufficient supervision *** overcome this obstacle,we propose to improve multi-task molecular property prediction by missing label ***,a bipartite graph is first introduced to model the molecule-task co-occurrence ***,the imputation of missing labels is transformed into predicting missing edges on this bipartite *** predict the missing edges,a graph neural network is devised,which can learn the complex molecule-task co-occurrence *** that,we select reliable pseudo labels according to the uncertainty of the prediction *** with enough and reliable supervision information,our approach achieves state-of-the-art performance on a variety of real-world datasets.

关键词： Graph classification imbalance learning prediction bias mixture of experts multiview representations

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Optimizing Maize Cultivation: A Vision-Based AI-Driven Methodology for Automated Seedling Thinning

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 224-232页

作者： Wang, Zijian An, Xiaofei Wang, Ling Tang, Jinshan Harbin Institute of Technology Department of Mathematics Harbin150001 China Beijing Academy of Agriculture and Forestry Sciences Intelligent Equipment Technology Research Center Beijing100097 China Harbin Institute of Technology Faculty of Computing Harbin150001 China George Maon University FairfaxVA22031 United States

To address the challenges of traditional manual maize seedling thinning, this article proposes an innovative approach that utilizes computer vision and deep learning for automated thinning. A zero-shot keypoint annotation algorithm, leveraging segment for anything model, is designed to label large datasets of maize seedling centers without requiring training samples. We also propose an improved hourglass network that significantly enhances seedling center positioning accuracy, enabling precise thinning decisions. Furthermore, a novel automatic thinning decision algorithm is devised to determine optimal removal strategies, ensuring ideal plant-to-plant spacing. The system's performance was evaluated against manually annotated data from 1020 images encompassing 2756 individual maize seedlings collected from farms. Impressively, the algorithm achieved a precision rate of 98.84%, confirming its ability to identify seedlings for removal while preserving healthy plants accurately. Evaluations of the keypoint detection network at a threshold of 0.2 yielded a percentage of correct keypoints of 97.66% and an object keypoint similarity of 0.87, surpassing existing methods and demonstrating the model's superior performance. © 2023 IEEE.

关键词： Zero-shot learning

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PIMCOMP: A Universal Compilation Framework for Crossbar-Based PIM DNN Accelerators 23

PIMCOMP: A Universal Compilation Framework for Crossbar-Base...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Xiaotian Sun Xinyu Wang Wanqian Li Lei Wang Yinhe Han Xiaoming Chen Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences

ISBN: (纸本)9798350323481

Crossbar-based PIM DNN accelerators can provide massively parallel in-situ operations. A specifically designed compiler is important to achieve high performance for a wide variety of DNN workloads. However, some key compilation issues such as parallelism considerations, weight replication selection, and array mapping methods have not been solved. In this work, we propose PIMCOMP - a universal compilation framework for NVM crossbar-based PIM DNN accelerators. PIMCOMP is built on an abstract PIM accelerator architecture, which is compatible with the widely used Crossbar/IMA/Tile/Chip hierarchy. On this basis, we propose four general compilation stages for crossbar-based PIM accelerators: node partitioning, weight replicating, core mapping, and dataflow scheduling. We design two compilation modes with different inter-layer pipeline granularities to support high-throughput and low-latency application scenarios, respectively. Our experimental results show that PIMCMOP yields improvements of 1.6× and 2.4× in throughput and latency, respectively, relative to PUMA.

关键词： NVM

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AmgR: Algebraic Multigrid Accelerated on ReRAM 23

AmgR: Algebraic Multigrid Accelerated on ReRAM

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Mingjia Fan Xiaotian Tian Yintao He Junxian Li Yiru Duan Xiaozhe Hu Ying Wang Zhou Jin Weifeng Liu Super Scientific Software Laboratory China University of Petroleum-Beijing Institute of Computing Technology Chinese Academy of Sciences and SKLP Institute of Computing Technology CAS Tufts University USA Institute of Computing Technology Chinese Academy of Sciences and SKLP Institute of Computing Technology CAS and Research Center for Intelligent Computing Systems

ISBN: (纸本)9798350323481

Solving systems of linear equations is a fundamental problem in scientific computing, which has been extensively researched for decades. One of the most well-known solvers is Algebraic Multigrid (AMG), which is widely used in high performance computing due to its good scalability. But currently accelerating AMG relies on the traditional von Neumann architecture of storage and computation separation, which leads to a large data transmission overhead. In this work, we propose a ReRAM-based processing-in-memory (PIM) architecture named AmgR, which overcomes the limitations of the traditional von Neumann architecture for AMG ***, accelerating AMG on ReRAM is non-trivial, because (1) AMG has many computing kernels of various types; (2) there are irregular operations that cannot be directly performed using matrix-vector multiplication suitable for ReRAM, i.e., aggregation operation; (3) ReRAM has poor write endurance, and a lot of data during AMG acceleration needs to be rewritten into ReRAM, resulting in high write cost. To address these issues, firstly, we propose a flexible architecture, which can realize each kernel of AMG and is reused by many kernels to improve resource utilization. Secondly, we propose a dedicated unit to realize the aggregation operation. Finally, we present a new mapping strategy to greatly reduce the number of data handling and writes. The experimental results show that the performance of AmgR is improved by an average of one and two orders of magnitude compared to HYPRE on the CPU and AmgX on the GPU, respectively, while the energy consumption is reduced by an average of two and three orders of magnitude.

关键词： AMG

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Time-Varying Formation Tracking Control of Heterogeneous Multi-Agent systems With Intermittent Communications and Directed Switching Networks

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 294-296页

作者： Yuhan Wang Zhuping Wang Hao Zhang Huaicheng Yan the Shanghai Research Institute for Intelligent Autonomous Systems Tongji University the National Key Laboratory of Autonomous Intelligent Unmanned Systems the Frontiers Science Center for Intelligent Autonomous System of Ministry of Education the College of Electronic and Information Engineering the Department of Control Science and Engineering Tongji University the Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education the School of Information Science and Engineering East China University of Science and Technology

Dear Editor,This letter is concerned with the problem of time-varying formation tracking for heterogeneous multi-agent systems(MASs) under directed switching networks. For this purpose, our first step is to present so... 详细信息

关键词： Switches Topology Observers Formation control Event detection Switched systems Multi-agent systems Control theory Autonomous systems Stability

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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