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ScaMP: Scalable Meta-Parallelism for Deep Learning Search

ScaMP: Scalable Meta-Parallelism for Deep Learning Search

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IEEE/ACM International symposium on Cluster computing and the Grid (CCGRID)

作者： Quentin Anthony Lang Xu Aamir Shafi Hari Subramoni Dhabaleswar K. DK Panda Department of Computer Science and Engineering The Ohio State University

Deep Learning (DL) models are growing exponentially and require increasingly powerful high performance computing (HPC) systems to train them. Achieving state-of-the-art results requires carefully tuning the DL model architecture and training settings, which is a time-consuming process commonly relegated to distributed search frameworks and trial-and-error. However, search frameworks don't provide a flexible parallelism scheme within and among the chosen DL framework for modern out-of-core DL models. In this paper, we propose Scalable Meta-Parallelism for Deep Learning Search (ScaMP): a distributed Hyperparameter Optimization (HPO) and Neural architecture Search (NAS) framework that supports out-of-core models with flexible parallelism schemes. SCaMP is integrated into the modern DL ecosystem, and enables both efficient parallel training of concurrent candidate architectures and aggregate device memory saturation via a powerful load balancing engine. SCaMP estimates the memory requirements of each candidate architecture and automatically applies the appropriate model-parallel degree and maximum batch size supported for the given candidate. Further, HPO and NAS with SCaMP are highly customizable via flexible configuration options. We evaluate the benefits of our designs on synthetic training benchmarks and in training a state-of-the-art vision transformer model. We select transformers as a candidate DL model type and demonstrate a 29% improvement in end-to-end HPO time on 32 V100 GPUs on the Lassen and thetaGPU HPC systems. Further, we demonstrate a reduction in the proportion of NAS time spent in communication from 28% to 15%. Finally, we thoroughly verify the correctness of SCaMP by training a state-of-the-art SwinIR model.

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A Data-Driven Approach to Heart Disease Prognosis using XG Boost Algorithm

A Data-Driven Approach to Heart Disease Prognosis using XG B...

引用

International Conference on computing and Networking Technology (ICCNT)

作者： K Kavitha K Gopalakrishnan S Kavitha K Sobiya M Suveetha R Sridhar CSE Kongu engineering college perundurai India IT Velalar College of engineering and Technology perundurai India CSE Nandha engineering college perundurai India Department of computer science and engineering Kongu engineering college perundurai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Heart disease remains a critical public health challenge, responsible for a high mortality rate and significant socio-economic burdens, including substantial healthcare costs and productivity losses. this study explores the application of advanced computational techniques, specifically deep learning and machine learning models, to enhance the prediction accuracy of heart disease. the focus is on the XGBoost algorithm, renowned for its superior performance in classification tasks. By leveraging a comprehensive dataset comprising diverse patient information, this research aims to improve the precision and efficacy of early heart disease detection. We rigorously evaluate XGBoost’s predictive capabilities against various other machine learning and deep learning models to identify the most effective approach for early diagnosis. the findings of this study have the potential to contribute significantly to the development of reliable predictive tools for heart disease, ultimately aiding in better clinical decision-making and patient outcomes.

关键词： Heart Deep learning Machine learning algorithms Computational modeling Predictive models Prediction algorithms Risk management Reliability Socioeconomics Diseases

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Exploring Intelligent UAV Route Planning in Autonomous Telecommunication Systems

Exploring Intelligent UAV Route Planning in Autonomous Telec...

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Rakhi Chakraborty Deepak Kumar K Preetham Department of Computer Science & IT ARKA JAIN University Jamshedpur Jharkhand India Department of Mathematics Vivekananda Global University Jaipur India Department of Management School of Management - UG JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

this paper gives a singular approach to permitwise unmanned aerial car (UAV) direction planning in self-sufficient telecommunication systems. the proposed technique utilizes the aggregate of heuristic algorithms, artificial intelligence, and dynamic programming strategies for unmanned airborne vehicles to determine the most useful route to supply the services correctly. the proposed technique is evaluated on simulated 3-D surroundings. It has proven its effectiveness in offering high-acting direction-making plans with quick flight routes and offers excellent real-time performance. the heuristics and synthetic intelligence strategies used in this method can efficiently examine the environment for higher route planning and allow the UAVs to evolve to extraordinary terrains, climates, and boundaries, which can affect the conversation shipping. the principle application of this technique is in offering reliable verbal exchange offerings via UAVs in antagonistic and indoor environments. thru the proposed approach, this paper immediately explores the green routing solutions for practical deployment eventualities.

关键词： Oral communication Autonomous aerial vehicles Routing Real-time systems Planning Telecommunications Maintenance Indoor environment Optimization Meteorology

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An Area Efficient Superconducting Unary CNN Accelerator

An Area Efficient Superconducting Unary CNN Accelerator

引用

IEEE International symposium on Quality Electronic Design

作者： Patricia Gonzalez-Guerrero Kylie Huch Nirmalendu Patra thom Popovici George Michelogiannakis Lawrence Berkeley National Laboratory Berkeley USA

In superconducting circuits, information is carried by ps-wide, µV-tall, Single Flux Quanta (SFQ) pulses. these circuits can operate at frequencies of hundreds of GHz with orders of magnitude lower switching energy than complementary-metal-oxide-semiconductors (CMOS). However, under the stringent area constraints of modern superconductor technologies, fully-fledged, CMOS-inspired superconducting architectures cannot be fabricated at large scales. Unary SFQ (U-SFQ) is an alternative computing paradigm that addresses these area constraints. In U-SFQ, information is mapped to a combination of streams of SFQ pulses and in the temporal domain. In this work, we propose a U-SFQ Convolutional Neural Network (CNN) hardware accelerator capable of comparable peak performance with state-of-the-art superconducting binary (B-SFQ) approaches in 32× less area. CNNs can operate with 5 to 8 bits of resolution with no significant degradation in classification accuracy. the proposed CNN accelerator effortlessly supports this variable resolution and, for less than 7 bits, yields 5×-63× better performance than CMOS and 15×-173× better area efficiency than B-SFQ.

关键词： Degradation Superconducting logic circuits Machine learning computer architecture Josephson junctions Convolutional neural networks Junctions

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ASMAMC: A Specific Microprocessor architecture for Monte Carlo Method

ASMAMC: A Specific Microprocessor Architecture for Monte Car...

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IEEE International Conference on high performance computing and Communications (HPCC)

作者： Jianmin Zhang Ziming Chen Li Zhang College of Computer National University of Defense Technology Changsha China

Monte Carlo (MC) method, known as a statistical test method, is widely used in many different physical and engineering fields, such as particle transport simulation, aerody-namic calculation, financial engineering, etc. the MC numerical simulation is huge memory usage, time-consuming and uncertain convergence. the application specific architecture processor has been addressed rather frequently in the last few years, owing to its increasing importance in numerous fields including AI and scientific computation. therefore, a specific microprocessor architecture for MC method (ASMAMC) is proposed. Based on the program features of the non-deterministic particle transport simulation derived from experiments on the architecture simula-tor, we explored a specific RISC-V microprocessor architecture including pipeline structure, branch prediction, Cache hierarchy and main memory. the experimental results on gem5 and McPAT show that the ASMAMC achieves 4.6 times increase on performance per watt, as compared with ARM Cortex-A15 processor,

关键词： Monte Carlo methods Multicore processing Microprocessors Computational modeling Pipelines computer architecture Predictive models

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Improved Squeeze and Excitation-Based YOLOv8-SE for Object Detection

Improved Squeeze and Excitation-Based YOLOv8-SE for Object D...

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Riya Chauhan Maroti Deshmukh Akshay Singh Dept. of Computer Science and Engineering NIT Uttarakhand Srinagar Garhwal India Managing Director Evigway Technologies

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

the rapid advancement of deep learning has made major advancements surveillance applications, yet detecting targets remains challenging due to size variations and environmental factors. In this paper, we integrated the Squeeze-and-Excitation (SE) module into various blocks of YOLOv8 to enhance feature discrimination and detection performance. the SE module, added to the backbone, neck, and head of YOLOv8, adaptively recalibrates channel-wise feature responses. this SE-enhanced YOLOv8 (YOLOv8-SE) model improves object detection on the challenging MS-COCO dataset, crucial for real-time surveillance across diverse day-night scenarios. Experimental results show that YOLOv8-SE attains high precision and recall, with a Mean Average Precision at 50 Intersection over Union (mAP50) of 0.5199 and mAP50-95 of 0.3647. Despite the increased number of parameters, the model maintains real-time inference speeds with reduced PyTorch inference time (in ms) and unchanged GFLOPs, establishing it as a reliable solution for real-time object detection tasks.

关键词： Deep learning Adaptation models Surveillance Computational modeling Object detection Feature extraction Real-time systems Environmental factors Neck Reliability

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SMART: Serverless Module Analysis and Recognition Technique for Managed Applications

SMART: Serverless Module Analysis and Recognition Technique ...

引用

IEEE/ACM International symposium on Cluster computing and the Grid (CCGRID)

作者： Adi Ashkenazi Edita Grolman Aviad Elyashar Dudu Mimran Oleg Brodt Yuval Elovici Asaf Shabtai Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel Cyber Labs@BGU Ben-Gurion University of the Negev Israel Department of Computer Science Shamoon College of Engineering Beer-Sheva Israel

ISBN: (数字)9798350395662

ISBN: (纸本)9798350395679

Serverless Function-as-a-Service (FaaS) environments enable developers to build and run cloud applications without the need to manage the underlying servers and computing infrastructure, allowing them to focus on implementing the application logic. Such environments contain numerous functions and dynamic resources, e.g., APIs and databases, making it challenging to gain insight and context of internal events i.e., recognize modules. Module in a serverless application is a set of functions and resources, that represents a functional unit that shares logical context. this paper presents SMART, a method for automatic analysis and recognition of modules for managed serverless applications. the proposed method creates an event-based graph by analyzing the standard serverless logs that document events involving the application’s functions and resources and utilizes well-known community detection algorithms (such as Louvain), with graph centrality metrics (such as degree centrality) to recognize the modules. SMART enables high-level visibility of the application’s structure and logical context which can facilitate security analysis and contribute to improved decision-making of incident response handlers, who typically do not have direct access to the application’s design and code, which can lead to challenges in fully understanding the system’s intricacies. We focused on the popular Amazon Web Services (AWS) Lambda serverless computing platform and evaluated the proposed method on three different demo applications (Airline Booking, VOD, and E-commerce). We compared SMART’s performance to four overlapping community detection algorithms and showed that it outperformed them in the task of module recognition, with a maximum improvement of 61% on the omega index metric compared to the Speaker-Listener Label Propagation algorithm. In addition, we demonstrate that the use of large language models (LLMs) with the knowledge gained by SMART can enrich security analysis insights.

关键词： Measurement Web services Serverless computing computer architecture Security Indexes Electronic commerce Detection algorithms Standards Airline industry

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Enhanced Sports performance Analysis: CNNLSTM Based Detection of Tennis Serve Variations

Enhanced Sports Performance Analysis: CNNLSTM Based Detectio...

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Vikrant Sharma Shiva Mehta Computer Science and Engineering Graphic Era Hill University Graphic Era Deemed to be University Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

this study looks at the applicability of a special network structure referred to as Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) in identifying minor changes in motion in the handling of the tennis racket during serves. the desired performance in rare movements, such as the incorporation of spatial feature extraction capabilities of CNNs and temporal sequence learning of LSTMs, attains serve. this model was trained and tested using a video-based dataset containing videos of professional/semi-professional and amateur tennis players. Next, a considerable number of small motion changes were extracted and labelled for many samples in the dataset. the observation made from the current research analysis shows that the CNN-LSTM model achieves an accuracy of 94%.5% on the detection of the disease with a precision of 92.3%, a recall of 93 - it is noticed that the above result is that which consolidates the fundamental constancy between the recall and the result it portrays. 8%, precision 93%, and F1-score 93%. But even in this regard, it stands at 0%, which is even higher than many state-of-the-art models. the analysis of the performance at each epoch shows gradual improvements; consequently, the training and validation sets attain high accuracy after 50 epochs. the optimization of the form of the batch size implies that an ideal batch size has been estimated as 32, with a corresponding classification accuracy estimate of 94 with a standard error of 0. Per cent: 5% and the decrease on 0. 25. the heatmap of the confusion matrix shows high classification rates for several of the motion variation classes with very little confusion, and the ROC curve analysis yields an average AUC of 0.93 for all classes.

关键词： Training Heating systems Analytical models Accuracy Sports equipment Feature extraction Convolutional neural networks Standards Sports Videos

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An Approach to Analyze the Permanent Magnet Motor Torque Ripple for Improving Torque Power Curves used in Electric Vehicles

An Approach to Analyze the Permanent Magnet Motor Torque Rip...

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International Conference on computing and Networking Technology (ICCNT)

作者： Varatharaj Neelakandan B Prabakaran D Balaji P Suresh S Maheswaran N Indhumathi Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore TamilNadu India Department of Electronics and Communication Engineering Kongu Engineering College Erode TamilNadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

An electric car’s permanent magnet synchronous motor (PMSM) will be designed and evaluated for performance in this research article. In order to satisfy the unique needs of the electric car and improve its overall performance, the goal is to achieve the highest torque density and the lowest torque ripple. the study’s goal is to increase the electric vehicle’s power density to do this, which calls for the use of a high-power-density motor. this is addressed by conducting an optimization procedure to increase the generated torque while making sure that the chosen motor stays within the defined size and volume restrictions. the efficient use of permanent magnet (PM) materials with strong thermal limiting capacity, which can enhance the power, is a critical component of this improvement. the efficient use of permanent magnet (PM) materials with high thermal limiting capacity, which may boost the power output under both peak and continuous loads, is a vital component of this optimization. the most important motor design parameters are carefully chosen throughout the optimization process to have the greatest influence on motor performance. Using the finite element software program “CAE Software,” the research team tests and executes the suggested motor design. the overall goal of this project is to build a PMSM that is highly effective and adapted to the needs of electric vehicles. the goal of the study is to optimize critical motor parameters and use modern PM materials with higher thermal properties in order to increase torque density and reduce torque ripple of the motor.

关键词： Torque Limiting Motors Electric vehicles Permanent magnet motors Software Permanent magnets Torque measurement thermal loading Optimization

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Analyzing the Effect of Spatial Correlation on performance of Multi-User Multiple Access Techniques

Analyzing the Effect of Spatial Correlation on Performance o...

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International Conference on computing and Networking Technology (ICCNT)

作者： I K Kantharaj Ankita Agarwal Bhawna Saraswat Department of Mechanical Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science Engineering Sanskrit University Mathura Uttar Pradesh India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Analyzing the impact of spatial correlation on the overall performance of multi-consumer more than one access strategy has long been a topic of hobby inside the communications field. Spatial correlation, that is, the quantity to which the sign outcomes in an area converge to one another, is an aspect that impacts the overall performance of conversation techniques within that identical place. the performance of those multi-person couples to get the right of entry to techniques alternates considerably depending on the extent of spatial correlation. For instance, spatial correlation can decrease the lateral right of entry to interference and consequently increase the system’s ability when compared to a non-correlated kind of device. On the other hand, high spatial correlation can result in low rejection of more than one access interference, railway rejection, and other severe overall performance constraints. Research approximately the effect of spatial correlation on multi-person a couple of get entry to techniques returned as a long way as 1983, in which diverse simulation analysis has been conducted. these simulations, in most cases, brought approximately the benefits and disadvantages of four essential multiple access strategies together with FDMA, TDMA, CDMA, and Daveigh these simulations have proven brilliant results, with the capability of a machine utilizing finite length are AO codes the usage of FDMA. A positive variety of spatial correlation claimed to be two times that of a conventional code-division a couple of get right of entry to machine. Extra specifically.

关键词： performance evaluation Time division multiple access Correlation Frequency division multiaccess Noise Insurance Interference Oral communication Distortion Antenna arrays

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