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A comprehensive survey on graph neural network accelerators

Frontiers of computer Science 2025年第2期19卷 11-29页

作者： Jingyu LIU Shi CHEN Li SHEN School of Computer National University of Defense TechnologyChangsha 410073China Key Laboratory of Advanced Microprocessor Chips and Systems Changsha 410073China

Deep learning has gained superior accuracy on Euclidean structure data in neural *** a result,nonEuclidean structure data,such as graph data,has more sophisticated structural information,which can be applied in neural networks as well to address more complex and practical ***,actual graph data obeys a power-law distribution,so the adjacent matrix of a graph is random and *** processing accelerator(GPA)is designed to handle the problems ***,graph computing only processes 1-dimensional *** graph neural networks(GNNs),graph data is ***,GNNs include the execution processes of both traditional graph processing and neural network,which have irregular memory access and regular computation,*** obtain more information in graph data and require better model generalization ability,the layers of GNN are deeper,so the overhead of memory access and computation is *** present,GNN accelerators are designed to deal with this *** this paper,we conduct a systematic survey regarding the design and implementation of GNN ***,we review the challenges faced by GNN accelerators,and existing related works in detail to process ***,we evaluate previous works and propose future directions in this booming field.

关键词： graph neural network accelerators graph convolutional networks design space exploration deep learning domain-specific architecture

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ISOAcc: In-situ Shift Operation-based Accelerator For Efficient in-SRAM Multiplication

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ACM Transactions on Design Automation of Electronic systems 2025年第2期30卷 1-24页

作者： Zhao, Gaoyang Shen, Junzhong Lin, Rongzhen Li, Hua Wang, Yaohua National University of Defense Technology College of Computer Science and Technology Changsha China Key Labtoratory of Advanced Microprocessor Chips and Systems National University of Defense Technology Changsha China

Digital SRAM-based CIM architectures must balance three critical factors: quantized neural network bitwidth, accuracy loss, and computational efficiency, each crucial to optimizing performance and efficiency. In Domain Specific Accelerators (DSAs), flexible and specific hardware design, when incorporated with tailored Power-of-2 (P-2) quantization schemes, addresses this issue. However, in CIMs, the absence of flexible and specific hardware to support dynamic switching between general and tailored quantization schemes hinders the adoption of efficient quantization methods. In this article, we propose the In-situ Shift Operation based Accelerator (ISOAcc) for efficient SRAM-based multiplication. The key idea is to introduce transmission gates near the SRAM array to enable the selection of bits from either the same or the neighbor line when data flows from one row to another. This functionally equals a shift operation. By configuring the transmission gates array in a cascade manner, ISOAcc can support 0 to 15-bit shift with a negligible overhead. The ISOAcc can directly leverage P-2 quantization schemes in hardware, thereby greatly reducing multiplication cycles. We have chosen five well-known neural networks to evaluate ISOAcc. The evaluations show that ISOAcc achieves an average performance improvement of 3.24× and an energy reduction of 75%, compared with the state-of-the-art (SOTA) SRAM-based CIM design, Bit-Parallel. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Static random access storage

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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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RGB Pixel Combination Method for Underwater Camouflaged Image Categorization using Graph Neural Networks

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IEEE Access 2025年 13卷 84341-84356页

作者： Dhamodharan, K. Pandi, Thanapal Vellore Institute of Technology School of Advanced Sciences Department of Mathematics Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems (SCORE) Vellore India

Underwater imaging applications require sophisticated image processing techniques aided by computer Vision (CV). The intensity and light variations under the water demand camouflaged object, plant, and living organism detection in deep seas. This article introduces the Edge-Recognition Method using Graph Neural Networks (ERM-GNN) to address visual differentiations of such particles with precise categorization. This method is reliable in detecting camouflaged object edges and classifying their foregrounds and backgrounds. In the edge detection and object classification process, color features are utilized, relying on RGB variants observed near the edges of the foreground and background images. The edge depth intensity feature is trained repeatedly using the GNN, forming layers between the initial and final pixels of the regions. This approach imposes an xC_y pixel combination to analyze depth intensity until the foreground and backgrounds are categorized. The neural network trains the hidden layers by differentiating categorized and non-categorized RGB variants for in-depth detection. Consequently, this combination increases the chances of camouflaged underwater object categorization with better precision. This method outperforms state-of-the-art approaches such as G+L-CET, FEGM, and MFEM by a wide margin, improving recognition accuracy by 14.48%, precision by 12.81%, and object classification by 9.82%. These noteworthy improvements highlight the method's efficacy and potential use in marine biology and environmental surveillance, necessitating accurate underwater object detection. © 2013 IEEE.

关键词： Camouflaged Image CV Depth Intensity GNN Underwater Objects

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SSC: An SRAM-Based Silence Computing Design for On-chip Memory 24th

SSC: An SRAM-Based Silence Computing Design for On-chip Mem...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Chen, Ziming Deng, Quan Hu, Yiyue He, Xiaowei Huang, Libo Wang, Yongwen College of Computer National University of Defense Technology Changsha China Key Laboratory of Advanced Microprocessor Chips and Systems Changsha China

ISBN: (纸本)9789819615445

The rapid development of emerging intelligent applications leads to a surge in computational demands and memory capacity requirements. Compute-in-memory (CIM) is a promising paradigm to alleviate the data movement bottleneck of emerging intelligent applications. SRAM-based CIM technology is employed to enhance the performance of general-purpose processors (e.g., CPU) by reusing on-chip memory (e.g. Cache) for computational tasks. In recent SRAM-based CIM works, peripheral compute circuits, e.g., adder trees, are introduced to improve system performance, as SRAM cells are hard to support arithmetic operations efficiently. However, the excessive peripheral circuit with a large area overhead degrades the memory density, which breaks the balance of computation and memory. To improve the computing capability and memory capability simultaneously of digital-based CIM (DCIM), we propose SSC, an SRAM-based silence computing CIM design, which leverages logic-in-memory operations and peripheral circuits to achieve parallel computing within the SRAM array. We propose 8+T (8T, 9T, and 11T) SRAM bitcells to support majority-of-three (MAJ3), COPY, and NOT logic, which is used to achieve a 3:2 compressor. Furthermore, a silence computing design, facilitated by a local connection between SRAM cells, is proposed to improve system parallelism, which supports parallel logic-in-memory operations and memory access. Our experiment results show that SSC achieves 3.7× memory density improvement compared to prior DCIMs normalized to 12nm. Additionally, the SWaP (TOPS/W×Kb/mm2) figure-of-merit emphasizes the importance of memory density and energy efficiency, showing that this work achieves a higher SWaP of 119.89, which is 1.84× more than prior DCIMs normalized to 12 nm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： computer circuits

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Nanoimprint lithography-assisted block copolymer self-assembly for hyperfine fabrication of magnetic patterns based on L10-FePt nanoparticles

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Science China Chemistry 2025年第5期68卷 2027-2034页

作者： Jie Zhang Yi Chen Guijun Li Zelin Sun Zhengong Meng Wai-Yeung Wong Department of Applied Biology and Chemical Technology and Research Institute for Smart Energy The Hong Kong Polytechnic University The Hong Kong Polytechnic University Shenzhen Research Institute Division of Integrative Systems and Design and Department of Electronic and Computer EngineeringThe Hong Kong University of Science and Technology School of Flexible Electronics (Future Technology) and Institute of Advanced Materials (IAM) Nanjing Tech University

L10-FePt-type bit-patterned media has provided a promising alternative for ultrahigh-density magnetic recording systems in the current digital era, but rapid fabrication of magnetic patterns with hyperfine bit islands is still challenging, especially with the target for miniaturization and scalable production simultaneously. Herein, Fe,Pt-containing block copolymers were utilized as single-source precursors for solution-processable patterning and subsequent generation of the demanding magnetic FePt dots by in situ pyrolysis. High-throughput nanoimprint lithography was initially employed to fabricate the predefined bit cells precisely,and then the intrinsic self-assembly of phase-separated block copolymers further drove the formation of accurate bit *** from the synergistic effect of top-down lithographic approach and bottom-up self-assembly, the customizable patterns could be achieved for large-scale mass production in targeted areas, but high-density isolated dots could also be accurately aligned along the patterned features after subsequent self-assembly. This reliable strategy would provide a good avenue to precisely construct ultrahigh-density magnetic data storage devices.

关键词： organometallic precursor magnetic nanoparticles block copolymer self-assembly nanoimprint lithography

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Integrating GIS-Remote Sensing: A Comprehensive Approach to Predict Oceanographic Health and Coastal Dynamics

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Remote Sensing in Earth systems Sciences 2025年第1期8卷 200-212页

作者： Krishnamoorthy, R. Tanaka, Kazuaki Begum, M. Amina Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai India Department of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Department of ECE Sri Venkateswaraa College of Technology Tamilnadu Chennai India

The monitoring of oceanographic and coastal dynamics is essential for understanding the effects of climate change, predicting natural disasters, and managing coastal resources. Remote sensing technology, particularly through satellite imagery, has revolutionized the ability to collect vast amounts of environmental data over large spatial and temporal scales. However, the complexity of these datasets, which often include intricate spatial patterns and long-term temporal trends, requires advanced machine learning models capable of handling both dimensions effectively. In this study, we present a Hybrid GIS-Transformer Network for analyzing oceanographic and coastal dynamics using remote sensing data. The proposed model integrates the spatial feature extraction capabilities of the GIS network with the temporal analysis strengths of the transformer model. This approach enables a comprehensive understanding of both spatial and temporal patterns in high-resolution satellite imagery, allowing for more accurate detection and prediction of environmental changes. Our methodology was tested across multiple tasks, including coastal erosion detection, sea surface temperature (SST) prediction, wave height prediction, and ocean currents detection. The Hybrid GIS-Transformer model consistently outperformed existing models such as CNN, ResNet50, MobileNet, EfficientNet, and LSTM. For coastal erosion detection, the hybrid model achieved an accuracy of 96.12%, while for SST prediction, the accuracy was 95.43%. In wave height prediction, the model achieved an accuracy of 93.02%, and for ocean currents detection, an accuracy of 91.88% was recorded. These results demonstrate the model’s superiority in capturing both fine spatial details and long-term temporal dynamics compared with individual models. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Coastal dynamics Deep learning Environmental monitoring Hybrid GIS-Transformer Machine learning Oceanographic analysis Remote sensing Sea surface temperature prediction Spatial feature extraction Temporal analysis Wave height prediction

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EFS-XGBoost: A robust framework for precision classification of COVID-19 cases

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Intelligent Data Analysis 2025年第1期29卷 171-185页

作者： Abidi, Mustufa Haider Khare, Neelu D, Preethi Alkhalefah, Hisham Umer, Usama Advanced Manufacturing Institute King Saud University Riyadh Saudi Arabia School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Ramapuram Chennai India

The emergence of the novel COVID-19 virus has had a profound impact on global healthcare systems and economies, underscoring the imperative need for the development of precise and expeditious diagnostic tools. Machine learning techniques have emerged as a promising avenue for augmenting the capabilities of medical professionals in disease diagnosis and classification. In this research, the EFS-XGBoost classifier model, a robust approach for the classification of patients afflicted with COVID-19 is proposed. The key innovation in the proposed model lies in the Ensemble-based Feature Selection (EFS) strategy, which enables the judicious selection of relevant features from the expansive COVID-19 dataset. Subsequently, the power of the eXtreme Gradient Boosting (XGBoost) classifier to make precise distinctions among COVID-19-infected patients is *** EFS methodology amalgamates five distinctive feature selection techniques, encompassing correlation-based, chi-squared, information gain, symmetric uncertainty-based, and gain ratio approaches. To evaluate the effectiveness of the model, comprehensive experiments were conducted using a COVID-19 dataset procured from Kaggle, and the implementation was executed using Python programming. The performance of the proposed EFS-XGBoost model was gauged by employing well-established metrics that measure classification accuracy, including accuracy, precision, recall, and the F1-Score. Furthermore, an in-depth comparative analysis was conducted by considering the performance of the XGBoost classifier under various scenarios: employing all features within the dataset without any feature selection technique, and utilizing each feature selection technique in isolation. The meticulous evaluation reveals that the proposed EFS-XGBoost model excels in performance, achieving an astounding accuracy rate of 99.8%, surpassing the efficacy of other prevailing feature selection techniques. This research not only advances the field of COVI

关键词： classification COVID-19 ensemble-based feature selection machine learning XGBoost

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CoDPoC IP: A Configurable Data Protection Circuit to Support Multiple Key Agreement Scheme 26th

CoDPoC IP: A Configurable Data Protection Circuit to Suppor...

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26th International Conference on Information and Communications Security, ICICS 2024

作者： Peng, Yijing Wang, Junhui Wang, Zhenyu Guo, Zhenbin Wang, Yaohua Deng, Ding Li, Shaoqing Guo, Yang Key Laboratory of Advanced Microprocessor Chips and Systems National University of Defense Technology Changsha China School of Computer National University of Defense Technology Changsha China College of Electronic Science and Technology National University of Defense Technology Changsha China College of Semiconductors Hunan University Changsha China

ISBN: (纸本)9789819788002

Physical Unclonable Function(PUF)-based security protocols are effective in mitigating various security threats, including eavesdropping, interception, and cloning. Nevertheless, most existing protocols are implemented in software, with the primary drawback being their inability to integrate with actual PUF and their inefficiency. Although some strategies incorporate PUF into their design, these systems do not allow for configuration changes and are deficient in flexibility. In this paper, we propose the CoDPoC IP, a configurable data protection circuit that supports PUF-based various key agreement authentication schemes and allows for flexible scheme design adjustments through configurable registers. The CoDPoC IP offers two advantages: 1) hardware-level implementation incorporates PUF, leading to higher resource utilization, efficiency, and security;and 2) the IP supports flexible configuration of schemes. Experimental results indicate that the hardware execution time is 0.03504 milliseconds. The proposed scheme is successfully implemented and verified with low-overhead hardware. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data privacy

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An Empirical Comparison of Machine and Deep Learning Algorithms for Predicting Maternal Health Risk 5th

An Empirical Comparison of Machine and Deep Learning Algorit...

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5th EAI International Conference on Cognitive Computing and Cyber Physical systems, IC4S 2024

作者： N, Srinivasa Gupta Valarmathi, B. Kavitha, A. Gopini, Sai Bhuvan Seela, Hari Kumar Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Mathematics School of Advanced of Sciences Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9783031770777

Maternal health is among the greatest challenges in the world, especially in rural areas as there lack medical practitioners, they do not have easily accessible publics clinics and transport is difficult. Therefore, high rates of maternal as well as infant morbidity and mortalities are recorded. This research utilizes Artificial Intelligence (AI) with machine learning algorithms to forecast and address maternal health hazards right at their onset stage. The current research utilizes the concept of AI along with many Machine Learning (ML) methods like the Ensemble Learning Model (ELM), Random Forest (RF), K-Nearest Neighbour (KNN), Decision-Tree (DT), XG-Boost (XGB), Cat Boost (CB), and Gradient Boosting (GB), along with Synthetic Minority Over-sampling Technique (SMOTE) algorithm used for dealing with the problem class imbalance within the data set. SMOTE algorithm is utilized for the dataset balancing process. The handling system involves refining data preprocessing with the help of feature engineering and robust data cleaning which makes sure that anomalies do not erode the reliability of the predictive model. The existing methods [1] used RF (90%), DT (87%), XGB (85%), CB (86%), and GB (81%) algorithms and were compared with the accuracies of the proposed models like Logistic Regression (LR), Ensemble Learning Bagging (ELB), Ensemble Learning Stacking (ELS), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The existing methods used only imbalance dataset. The accuracies of the proposed models with using SMOTE algorithm (balanced dataset) are LR (61.33%), KNN (81%), ELB (92.33%), ELS (90.66%) CNN (40.67%), RNN (59.67%), LSTM (54%), GRU (56%) respectively. Among these methods, ELB achieved 92.33% of accuracy with using SMOTE algorithm using imbalanced dataset. Whereas the accuracies of the proposed models without using SMOTE algorithm (imbalanced dataset) are LR (66.09%), KNN (68.47%)

关键词： Convolutional neural networks

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