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RAHP: A Redundancy-aware Accelerator for High-performance Hypergraph Neural Network

RAHP: A Redundancy-aware Accelerator for High-performance Hy...

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IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Hui Yu Yu Zhang Ligang He Yingqi Zhao Xintao Li Ruida Xin Jin Zhao Xiaofei Liao Haikun Liu Bingsheng He Hai Jin National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computer Science University of Warwick United Kingdom National University of Singapore Singapore

ISBN: (数字)9798350350579

ISBN: (纸本)9798350350586

Hypergraph Neural Network (HyperGNN) has emerged as a potent methodology for dissecting intricate multilateral connections among various entities. Current software/hardware solutions leverage a sequential execution model that relies on hyperedge and vertex indices for conducting standard matrix operations for HyperGNN inference. Yet, they are impeded by the dual challenges of redundant computation and irregular memory access overheads. This is primarily due to the frequent and repetitive access and updating of a number of feature vectors corresponding to the same hyperedges and vertices. To address these challenges, we propose the first redundancy-aware accelerator, RAHP, which enables high performance execution of HyperGNN inference. Specifically, we present a redundancy-aware asynchronous execution approach into the accelerator design for HyperGNN to reduce redundant computations and off-chip memory accesses. To unveil opportunities for data reuse and unlock the parallelism that existing HyperGNN solutions fail to capture, it prioritizes vertices with the highest degree as roots, prefetching other vertices along the hypergraph structure to capture the common vertices among multiple hyperedges, and synchronizing the computations of hyperedges and vertices in real-time. By such means, this facilitates the concurrent processing of relevant hyperedge and vertex computations of the common vertices along the hypergraph topology, resulting in smaller redundant computations overhead. Furthermore, by efficiently caching intermediate results of the common vertices, it curtails memory traffic and off-chip communications. To fully harness the performance potential of our proposed approach in the accelerator, RAHP incorporates a topology-driven data loading mechanism to minimize off-chip memory accesses on the fly. It is also endowed with an adaptive data synchronization scheme to mitigate the effects of conflicting updates of both hyperedges and vertices. Moreover, RAHP emplo

关键词： Computational modeling Neural networks Memory management Loading Energy conservation Vectors Software Topology Synchronization Standards

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Robust Wireless Power Transfer by Self-Oscillating Controlled Inverter and Circular Pads 3

Robust Wireless Power Transfer by Self-Oscillating Controlle...

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3rd International Conference on Electrical Machines and Drives, ICEMD 2023

作者： Eikani, Alireza Amirkhani, Mohammad Jafari, Hossein Vaez-Zadeh, Sadegh Mirsalim, Mojtaba Khaburi, Davood Arab University of Tehran Advanced Motion Systems Research Lab School of Electrical and Computer Engineering College of Engineering Tehran Iran Department of Electrical Engineering Tehran Iran Iran University of Science and Technology Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350393743

Inductive wireless power transfer (WPT) faces significant challenges in achieving high efficiency and stable output power for charging mobile devices like EVs. Using circular pads (CP) as the transmitter (Tx) and receiver (Rx) and a self-oscillating controlled inverter, this paper demonstrates a robust WPT system that maintains high constant transfer efficiency and output power in a defined region regardless of coupling coefficient variations without communication between Tx and Rx or control circuits for the receiver side. In contrast to couplers with non-identical Tx and Rx pads and multiple decoupled coils in the Rx pad, the compact CP-CP magnetic coupler with a single identical coil on the Tx and Rx is more robust at a wider transfer distance (air gap) and lateral and rotational misalignment between the Tx and Rx. The proposed 1-kW robust WPT system with up to a 20 cm air gap between the Tx and Rx and up to 12 cm lateral misalignment in all directions at a 15 cm air gap and all degrees of rotational misalignment can tolerate coupling variations and achieve constant output power with 93% constant transfer efficiency. © 2023 IEEE.

关键词： Energy transfer

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LR-Based Performance Evaluation of MoCs

LR-Based Performance Evaluation of MoCs

引用

International Conference on VLSI Systems, Architecture, technology and Applications (VLSI-SATA)

作者： Pallabi Hazarika Biswajit Bhowmik Department of Computer Science and Engineering Swami Vivekananda NoC Lab BRICS Lab National Institute of Technology Karnataka Surathkal Mangalore Bharat

ISBN: (数字)9798350362268

ISBN: (纸本)9798350362275

In the recent decade, on-chip communication net-works have developed into a potent platform for tackling chal-lenging and significant computation issues. However, many applications cannot achieve high-performance communication needs due to the seamless integration of computing cores in systems-on-chip (SoCs). Subsequently, a network-on-chip (NoC) has emerged as a prominent on-chip communication infrastructure in SoCs. Performance analysis of NoC's is essential for its architectural design and is traditionally evaluated employing a simulator. How-ever, simulation-based performance evaluation is relatively slow and may take a long time with varying architectural NoC sizes. This paper presents an AI-based approach for investigating mesh-based NoC (MoC) performance over the traditional simulation-based performance evaluation. The proposed framework targets to reach two objectives- quickly and accurately evaluation of various NoC performance metrics. Simulations are performed at varying architectural setups on a set of mesh NoCs to generate the training dataset for the proposed framework. Consequently, the framework satisfactorily predicts different performance metrics. For example, network and packet latency; hop count; switch, channel, and total power consumption; and total area are in the range of 58.14-88.49 and 58.69-106.97 cycles; 6.231-6.257; 1.44-13.02, 13.73-129.06, and 25.26- 177.44 $\mu W;$ and 1.35874 $\mu m^{2}$ , respectively while the proposed framework is applied on the 9 x 9 mesh NoC. The metrics are with 94% accuracy and predicted at very significantly less time. The LR model saves 99.45 % evaluation time resulting in the speedup of 260 x than a simulation - based method.

关键词： Performance evaluation Training Power demand Accuracy Systems architecture Switches Network-on-chip

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Outlier Detection in Streaming Data Using Deep Learning Models

Outlier Detection in Streaming Data Using Deep Learning Mode...

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Smart Electronics and Communication Systems (ISENSE), International Conference on

作者： Samagnya Dudipala Shalini Gangavarapu Girish K K Biswajit Bhowmik BRICS Lab Department of Computer Science and Engineering Ishwarchandra Vidyasagar AIT Lab National Institute of Technology Karnataka Surathkal Mangalore Bharat

ISBN: (数字)9798331528126

ISBN: (纸本)9798331528133

In the realm of the Internet of Things (IoT), devices continuously generate a vast and relentless stream of data, providing a real-time representation of digital landscape. The continuous and high-velocity nature of this streaming data poses significant challenges for real-time analysis. Accurate outlier detection within this data is essential, as such anomalies may indicate critical issues, attacks, or errors. Nevertheless, the dynamic and rapidly evolving characteristics of streaming data render traditional outlier detection methods inadequate. This paper investigates the application of Artificial Neural Networks (ANNs), specifically a Multi-Layer Perceptron (MLP), for outlier detection in streaming IoT data. The selection of the MLP from a range of Deep Neural Networks (DNNs) is based on its optimal balance between computational efficiency and model complexity. The model's efficacy is confirmed through rigorous experimentation, demonstrating strong performance across diverse scenarios and data classes. The MLP achieved an accuracy of 99.4%, underscoring its ability to detect even minor deviations from expected patterns. This high level of accuracy establishes the MLP as a robust tool for outlier detection in dynamic IoT environments.

关键词： Training Adaptation models Accuracy Computational modeling Artificial neural networks Real-time systems Data models Complexity theory Internet of Things Anomaly detection

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Money Laundering Detection in Banking Transactions using RNNs and Hybrid Ensemble

Money Laundering Detection in Banking Transactions using RNN...

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International Conference on Computing and Networking technology (ICCNT)

作者： K K Girish Biswajit Bhowmik Department of Computer Science and Engineering Ishwarchandra Vidyasagar AIT Lab BRICS Lab National Institute of Technology Karnataka Surathkal Mangalore Bharat

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The financial sector has witnessed significant transformations due to the emergence of financial technology (FinTech), transitioning from traditional paperbased processes to a dynamic digital ecosystem. Despite the industry’s advancements driven by FinTech innovations, concerns persist, particularly regarding financial fraud, notably money laundering. Perpetrators exploit modern technologies to launder illicitly obtained funds, posing a global threat to economies. Effective detection mechanisms for money laundering are crucial. This paper introduces a novel approach utilizing a recurrent neural network (RNN) for detecting money laundering in banking transactions. The proposed framework exercises standalone RNN models such as LSTM, GRU, BiLSTM, and stacked RNN models for the detection. Additionally, the effectiveness of hybrid ensemble models combining RNNs with XGBoosts is investigated. The evaluation achieves standard performance metrics, with the stacked RNN model achieving 92% accuracy. Surpassing it, the ensemble model achieves an impressive 95%. These results underscore the superiority of hybrid ensemble models over standalone RNNs, particularly in accurately detecting money laundering activities.

关键词： Measurement Technological innovation Recurrent neural networks Biological system modeling Refining computer architecture Banking Stability analysis Security Standards

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Enhancing MPI Communication Efficiency for Grid-Based Stencil Computations

Enhancing MPI Communication Efficiency for Grid-Based Stenci...

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Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications (COSMIC), International Conference on

作者： Santosh Iranagouda Goudar Premkumar SJ Nayaka K K Girish Biswajit Bhowmik Department of Computer Science and Engineering Maharshi Sushrut CAS Lab BRICS Lab National Institute of Technology Karnataka Surathkal Mangalore Bharat

ISBN: (数字)9798331517892

ISBN: (纸本)9798331517908

In parallel computing, where efficiency and speed are crucial, the Message Passing Interface (MPI) is a fundamental paradigm for managing large-scale distributed memory systems. MPI is critical to complex computational tasks, particularly in grid-based computations that solve intricate numerical problems by discretizing spatial domains into structured grids. However, MPI Cartesian communicators exhibit limitations in handling these computations effectively, especially when managing large-scale data exchanges and complex stencil patterns. This paper addresses these challenges by presenting an integrated approach that combines MPI collective and Cartesian communication methods. The proposed solution simplifies data distribution, eliminates redundant interfaces, and enhances communication efficiency. Experimental results show a 43% reduction in execution time and a 40% decrease in communication overhead, with scalability improvements achieving 12.5x speedup using 64 processes. These quantitative outcomes demonstrate the advan-tages of the proposed method over conventional MPI Cartesian approaches, establishing it as a reliable framework for advancing High-Performance Computing (HPC) capabilities in grid-based applications.

关键词： Mechatronics Scalability Message passing Memory management Reinforcement learning Parallel processing Real-time systems Computational efficiency Reliability Intelligent systems

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Design of Intelligent Decision Support System With Ensembled Machine Learning to Predict Students' Performance

Design of Intelligent Decision Support System With Ensembled...

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2024 International Conference on Communication, computer sciences and Engineering, IC3SE 2024

作者： Deepan, S. Rajendran, Dineshkumar Dhaliwal, Navdeep Praveena, K. Lourens, Melanie Anandhi, R.J. School Of Cs & Ai Sr University Telangana Warangal India School Of Computing And Innovative Technologies British University Computer Games Design And Programming Viet Nam Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Dundigal Hyderabad India Durban University Of Technology Faculty Of Management Sciences South Africa New Horizon College Of Engineering Department Of Information Science Engineering Bangalore India

ISBN: (纸本)9798350366846

Evaluating student performance is important for universities and institutions in the current student education landscape because it helps them create models that work better for students. The automation of various features related to fundamental student traits and behaviours that manage massive amounts of data efficiently processes these. To handle student records that included information about students' behaviour and how it related to their academic performance, the companies employed models of classification with mining concepts. Additionally, the quality of result classification can be substantially improved by using learning analytics and Educational Data Mining (EDM). The educational establishments are making an effort to lower the low student performance. To address this issue, numerous methods for assessing student performance have been devised, allowing the relevant faculties to intervene and enhance the final product. Three classes - Low Performance Student, Average Student, and Smart Student - were created using the K-Mean Clustering methodology for classifying student records. Features including grade point, number of deficits, student attendance, medium of education, and board of education are taken into account when classifying the data. In this case, the WEKA tool is also utilized for implementing the model and outcome assessments. © 2024 IEEE.

关键词： Decision support systems

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Content Based Email Spam Filtering and Detection using Hybrid Supervised Learning Approach

Content Based Email Spam Filtering and Detection using Hybri...

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World Congress on Computing and Communication Technologies (WCCCT)

作者： Najam U Saquib Waqar Khalid Sahibzadi Annum Shaheen Muhammad Asim Naveed Ahmad Department of Computer Science Abasyn University Islamabad Pakistan Department of Computer Science Institute of Management Sciences Peshawar Pakistan Department of Computer Science Capital University of Science & Technology Islamabad Pakistan EIAS Data Science and BlockChain Lab College of Computer and Information Science Prince Sultan University Riyadh Saudi Arabia College of Computer and Information Science Prince Sultan University Riyadh Saudi Arabia

ISBN: (数字)9798350372328

ISBN: (纸本)9798350372335

In today’s world, email is used widely for communication purposes globally. Email spam are unwanted emails that are sent to many recipients receivers. It is usually used for commercial purposes More likely spam enail is also used for malicious attempts to gain access to receiver computer/device. This article presents a novel hybrid architecture for spam emails filtering using mails content-based approach. For filtering, Word2Vec (W2V) word embedding procedure is used for feature selection with bio-inspired “Particle Swarm Optimization (PSO)” and “Support Vector Machine (SVM)” combined with “Convolutional Neural Network (CNN)”. Moreover, our proposed hybrid architecture is simulated in open-source tool and the performance is evaluated using standard confusion matrix. Afterw ards, the results are compared with various previous algorithms/models. Experimental results show that our proposed hybrid scheme enhances detection accuracy of spam emails.

关键词： Support vector machines Filtering Unsolicited e-mail Computational modeling Supervised learning computer architecture Receivers

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A Horizontal Federated Learning Approach to IoT Malware Traffic Detection: An Empirical Evaluation with N-BaIoT Dataset 26

A Horizontal Federated Learning Approach to IoT Malware Traf...

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26th International Conference on Advanced Communications technology, ICACT 2024

作者： Do, Phuc Hao Le, Tran Duc Vishnevsky, Vladimir Berezkin, Aleksandr Kirichek, Ruslan The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Saint-Petersburg Russia Danang Architecture University Da Nang Viet Nam University of Telecommunications Department of Communication Networks and Data Transmission at Bonch-Bruevich Saint-Petersburg State Russia University of Science and Technology The University of Danang Da Nang Viet Nam The University of Danang University of Science and Technology Information Technology Faculty Danang Viet Nam V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia Institute of Control Sciences of Russian Academy of Sciences Russia Bonch Bruevich Saint Petersburg State University of Telecommunications Department of Programming Engineering and Computer Science Russia

ISBN: (纸本)9791188428120

The increasing prevalence of botnet attacks in IoT networks has led to the development of deep learning techniques for their detection. However, conventional centralized deep learning models pose challenges in simultaneously ensuring user data privacy and detecting botnet attacks. To address this issue, this study evaluates the efficacy of Federated Learning (FL) in detecting IoT malware traffic while preserving user privacy. The study employs N-BaIoT, a dataset of real-world IoT network traffic infected by malware, and compares the effectiveness of FL models using Convolutional Neural Network, Long Short-Term Memory, and Gated Recurrent Unit models with a centralized approach. The results indicate that FL can achieve high performance in detecting abnormal traffic in IoT networks, with the CNN model yielding the best results among the three models evaluated. The study recommends the use of FL for IoT malware traffic detection due to its ability to preserve data privacy. © 2024 Global IT Research Institute - GIRI.

关键词： Malware

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Intelligent Fraud Detection Techniques in Credit Card and Internet Banking

Intelligent Fraud Detection Techniques in Credit Card and In...

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Communication, Control and Intelligent Systems (CCIS)

作者： Santosh Iranagouda Goudar Biswajit Bhowmik BRICS Lab Department of Computer Science and Engineering Ishwarchandra Vidyasagar AIT Lab National Institute of Technology Karnataka Surathkal Mangalore Bharat

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

In an era where financial services are easily accessible, financial fraud has emerged as a widespread threat, aimed at unlawfully seizing assets from individuals, businesses, and institutions. Crimes like credit card and internet banking fraud present serious risks to the financial ecosystem. Criminals exploit card information using techniques such as identity theft, skimming, and phishing, causing substantial economic losses each year. Similarly, the rise of internet banking fraud, where unauthorized access to accounts often results from weaknesses in authentication systems, has further disrupted the financial sector. Detecting such fraudulent activities is especially difficult due to the ever-changing tactics of perpetrators, the large volume of transaction data, and technical challenges like data imbalance and concept drift. This paper examines the landscape of financial fraud, with a specific emphasis on credit card and internet banking fraud. It begins by providing an overview of the issue and its impact on the modern economic environment, followed by an exploration of various fraud detection methods. Additionally, the paper addresses key challenges in fraud detection and highlights the need for innovative approaches to effectively combat the growing threat of financial crime.

关键词： Economics Online banking Computational modeling Biological system modeling Scalability Concept drift Finance Credit cards Fraud Systematic literature review

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