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检索条件"任意字段=15th Symposium on Computer Architecture and High Performance Computing"

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Quantum Computational Approaches to Achieve Intrinsic Parallelization 15

Quantum Computational Approaches to Achieve Intrinsic Parall...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Soni, Minakshi Sharma, Sanjay Maulana Azad National Institute of Technology Department of Computer Applications Bhopal India

ISBN: (纸本)9798350370249

Quantum computation is the effective and inherent parallelized way of solving real world problems over quantum computer. the real world demands for a parallelization just to achieve the performance speed-up and for obtaining optimized computational results. this paper investigates various quantum computational approaches to achieve intrinsic parallelization, focusing on superposition and entanglement to perform multiple computations simultaneously. Based on a comparative classical-quantum aspect, the emergence and significance of the quantum computing is justified. the key quantum algorithms, including Shor's and Grover's, are analyzed for their parallel processing potential and impact on solving complex problem. the potential of quantum computation in the cryptography, cybersecurity and machine learning generate benefits and enhances performance using the quantum approaches. this paper aims to advance the understanding and the development of quantum computational methods as highly efficient and it contributes to near future of high-performance computing through intrinsic parallelization. © 2024 IEEE.

关键词： Intrinsic Parallelization Quantum Algorithm Quantum Computation Quantum Cryptography Superposition

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Quantum Machine Learning Compiler for Hybrid Quantum-Classical Models 15

Quantum Machine Learning Compiler for Hybrid Quantum-Classic...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Srinidhi, Srivageesh K. Vishal, K.S. Shashank, U. Someswara Belwal, Meena Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

the proposed work aims at designing a composite classical-quantum machine learning architecture for binary classification using VQCs. the goal is to use the power of quantum computing to enhance and improve the efficiency of classical machine learning techniques. this is done through the development of a customized compiler that maps the high-level quantum code to specific quantum circuits that are then incorporated with conventional Neural Networks into what can be termed as Quantum Machine Learning (QML) model. Goaded by the likelihood of quantum computation to enhance computational precision and speed, this proposed work presents an outline of the theoretical quantum circuits, architecture and development of this special compiler and linking of both atomic and classical models. A proof of concept is done using the Breast Cancer Wisconsin dataset to compare the capabilities of the suggested model with a more profound understanding of the strengths and weaknesses of this type of hybrid strategy. © 2024 IEEE.

关键词： Quantum computers

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Forecasting file lifecycles for intelligent data placement in hierarchical storage 35

Forecasting file lifecycles for intelligent data placement i...

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35th IEEE International symposium on computer architecture and high performance computing (SBAC-PAD)

作者： Khelili, Adrian Robert Hayek, Sophie Zertal, Soraya UPSaclay UVSQ Eviden Atos BDS R&D Data Management Li PaRAD Versailles France Eviden Atos BDS R&D Data Management Echirolles France UPSaclay UVSQ Li PaRAD Guyancourt France

ISBN: (纸本)9798350305487

the increasing disparity between computing speed and data access latency poses significant challenges in managing data storage, particularly for massively parallel supercomputers. To address this issue, storage systems have evolved into hierarchical architectures with different tiers offering varying performance, cost, and capacity based on specific hardware technologies. this heterogeneous and hierarchical nature of storage comes with the need for an optimal data placement strategy. Existing strategies have primarily approached this problem from a block perspective, focusing on analyzing application I/O behavior. However, such approaches fail to capture the contextual usage of information. To overcome this limitation, considering file-level usage patterns and adopting a file-centric perspective for data placement can leverage the context and semantics of files, leading to more efficient data placement strategies. this study proposes a novel file-based approach for data placement, focusing on the concept of file re-use and representing files through their life cycles (FLCs). the FLC of a file captures the sequence of operations it undergoes during its lifetime, referred to as FLCevents. By analyzing the time series data associated with the FLCs of actively used files, our algorithm detects repetitive patterns of FLCevents to predict future events using pattern matching and facilitate the anticipation of file movement. this allows for optimal data placement that aligns with the expected near-future usage. To validate our approach, we conducted experiments using traces extracted from representative high-performance computing (HPC) applications, namely NEMO, NAMD, LQCD, and an IO-Benchmark. the results we obtained are highly promising, demonstrating that in term of file prediction accuracy our proposed F-LRU (File-based Least Recently Used) achieves from 77% to 55% precision in most difficult scenarios to 100% for the others. Also, it is at least as effective as tradit

关键词： I/O Prediction File lifecycles Time Series Data temperature Hierarchical storage high performance computing

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high performance Connected Components Accelerator for Image Processing in the Edge 20th

High Performance Connected Components Accelerator for Image ...

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20th International symposium on Applied Reconfigurable computing (ARC)

作者： Mira, Jose L. Barba, Jesus Caba, Julian de la Torre, Jose A. Rincon, Fernando Escolar, Soledad Lopez, Juan Carlos Paseo Univ Technol & Informat Syst Dept Sch Comp Sci 4 Ciudad Real Castilla La Mancha 13071 Spain

ISBN: (纸本)9783031556722;9783031556739

In image processing, a connected components algorithm is a method used to identify and label the different objects or regions present in a digital image. this algorithm can be useful for a variety of image processing tasks, such as object recognition, image segmentation, and feature extraction. this work presents the implementation of a single-pass algorithm on an FPGA-based device suitable for high-performance edge computing vision applications, the Ultra96-V2 computing board. the design and implementation of the IP core have faced challenges using the AMD-Xilinx HLS workflow and tools, which require efficient and optimized use of resources, as well as the re-engineering of the algorithm to comply with the requirements imposed by the development framework. the performance of the proposed accelerator has been thoroughly analysed using the YACCLAB benchmarking framework against a high-end and a low-end CPU. the results show an expected loss in performance due to memory and clock frequency limitations. However, concerning energy efficiency, the hardware multicore architecture outperforms the software alternatives with an improvement between two and five times, depending on the size and complexity of the images.

关键词： Connected Components high performance Edge Processing computer Vision Field Programmable Gate Arrays

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Using Logging-on-Write to Improve Non-Volatile Memory Checkpoints via Processing-in-Memory 35

Using Logging-on-Write to Improve Non-Volatile Memory Checkp...

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35th IEEE International symposium on computer architecture and high performance computing (SBAC-PAD)

作者： Kruger, Kleber Pannain, Ricardo Azevedo, Rodolfo Univ Estadual Campinas Inst Comp Campinas Brazil

ISBN: (纸本)9798350305487

NVM architectures must keep consistent data in case of failures, a property called crash consistency. A common way to do so is by checkpoint mechanisms. However, most of the strategies developed have performance and usability problems. Among main limitations are non-software-transparent strategies, the addition of logging operations in the critical execution path, and increased writes to NVM, resulting in significant bandwidth usage between processor and memory. DONUTS solves these problems by a hardware mechanism that provides crash consistency via checkpoints integrated into cache replacement policy using Processing-in-Memory to perform logging operations. Its approach reduces writes from the processor's memory controller and the NVM external bandwidth usage but generates unnecessary log entries. this paper expands DONUTS to a multi-core scenario evaluating two processing-in-memory strategies. the first logs during read operations, and the second uses a new lazy strategy to log data exclusively on write operations when these operations are effectively needed. Finally, we compare runtime performance, log rate, energy consumption, and memory space. Results show that our new logging-on-write strategy maintained the DONUTS runtime performance but reduced energy consumption in multi-core applications by around 33% due to an average reduction of 42% in log operations. Also, the new strategy generates a checkpoint size 5x smaller than the previous system, maximizing the use of NVM. Compared to other systems, DONUTS presented an average overhead of 1% to 3% against up to 7% of previous software-transparent better-performing projects.

关键词： processing-in-memory in-memory computing non-volatile memory checkpointing crash consistency

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Exploring the Serverless First Strategy in Cloud Application Development 35

Exploring the Serverless First Strategy in Cloud Application...

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35th IEEE International symposium on computer architecture and high performance computing (SBAC-PAD)

作者： Cavalheiro, Adriano Prado Schepke, Claudio Fed Univ Pampa Alegrete PPGES Grad Program Software Engn Alegrete Brazil

ISBN: (纸本)9798350381603

this paper explores the Serverless First strategy in cloud application development. Serverless computing has gained popularity due to its flexibility and scalability. In our work, we provide a systematic review of the literature about the Serverless paradigm in cloud computing and an evaluation of the advantages of this approach by performing a comparative analysis among three ways for the implementation of an application: AWS Lambda, AWS Lambda with Chalice framework, and the traditional form using the Flask framework. the literature review results show the gains in scaling, cost reduction, and ease of maintenance achieved with the Serverless First strategy. However, some limitations and challenges were also highlighted, such as the greater complexity of the environment, less control over resources, resource limitations imposed by the cloud provider, and difficulties in debugging and managing the infrastructure. the case study demonstrates in practice that the Chalice framework provided the most straightforward and rapid implementation, the AWS Lambda without Chalice offered greater flexibility and control, and the Flask version allowed local testing and total control but required more manual setup and lacked automatic scalability.

关键词： Serverless First Function as a Service Serverless computing Serverless architecture Serverless Applications Software Development Cloud computing Cloud Services

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ANSAN: Data Interpretability based analysis of Deep Learning Model 15

ANSAN: Data Interpretability based analysis of Deep Learning...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Harish, S. Karthie Krishna, K. Bindu, Avadhani thangavel, Senthil Kumar Somasundaram, K. Parthasaradhi, Sathyan Shanmugam, Selvanayaki Kolandapalayam Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Amrita Vishwa Vidyapeetham Amrita School of Physical Sciences Department of Mathematics Coimbatore India Sathyan Eye Hospital and Coimbatore Glaucoma Foundation Ramanathapuram Coimbatore India Ashland University Department of Mathematics and Computer Science OH United States

ISBN: (纸本)9798350370249

Glaucoma, an irreversible neurodegenerative disease leading to vision loss, remains a significant public health concern due to challenges in early detection and resource-intensive diagnostic procedures. Leveraging advancements in artificial intelligence (AI), particularly Convolutional Neural Networks (CNNs), presents a promising avenue for automating glaucoma detection, especially in regions lacking access to ophthalmologists. this study, explores the efficacy of glaucoma detection systems based on MobileNet architecture for mobile deployment, focusing on key factors such as dataset variability, size, and training time. this paper presents findings from two distinct datasets, namely chaksu and g1020, each trained on separate models termed the chaksu model and g1020 model, respectively. these models, evaluated against the Akshi dataset, demonstrate impressive performance, suggesting the feasibility of utilizing complete fundus images for Glaucoma detection, circumventing the complexities of segmentation tasks involving disc, cup, and blood vessels. In this study, LIME (Local Interpretable Model-agnostic Explanations) was integrated to enhance interpretability and shed light on the decision-making process of our glaucoma detection system based on MobileNet architecture. this research underscores the potential of mobile technologies coupled with AI in democratizing glaucoma screening, potentially mitigating the challenges posed in resource-constrained settings. © 2024 IEEE.

关键词： Convolutional neural networks

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Yolo-based Deep Learning Techniques for Identifying Floating Bottles in Inland Water: A Comprehensive Analysis 15

Yolo-based Deep Learning Techniques for Identifying Floating...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Devi, B Saranya Gupta, Deepa Singh, Rimjhim Padam Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Floating trash like bottles are one of the main causes of the serious environmental threat posed by the growing pollution of inland water bodies. Conservation efforts are hampered by the lack of effective and affordable techniques for locating and retrieving these bottles. Real-time object detection algorithms in conjunction with unmanned boats can offer ways to remove floating trash from rivers. A popular real-time object detection model is the You Only Look Once (Yolo) Series. Based on efficacy and accuracy rate, each YOLO model gets better than the other. the study presents an extensive analysis of YOLO designs, such as YOLOv5, YOLOv7, YOLOv8, and YOLO-NAS, for bottle identification in inland water. the dataset used is a floating trash in the river, and the performance metrics such as precision, recall and mean average precision are employed, to compare and experimentally validate each algorithm's detection efficiency and accuracy. the methodology trains each architecture for 200 epochs. Regarding the precision of detection, the experimental results shows that YOLOv8-x model has high value of 85.5% and mAP @[IoU=0.5: 0.95] of 43.5%. YOLO-NAS variations have high recall, they are applicable for minimizing missed detection. YOLO-NAS m has a higher recall value of 90%. © 2024 IEEE.

关键词： bottle detection computer vision deep learnings floating trash Flow-Img object detection YOLONAS YOLOv8

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Explainable AI for Glaucoma Detection: Integrating Alternet-k and LIME 15

Explainable AI for Glaucoma Detection: Integrating Alternet-...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sri Kailaash Kumar, S. Harshni Sri, K.S. Bindu, Avadhani thangavel, Senthil Kumar Somasundaram, K. Parthasaradhi, Sathyan Shunmugapriya, M.C. Shanmugam, Selvanayaki Kolandapalayam Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India Amrita School of Physical Sciences Amrita Vishwa Vidyapeetham Department of Mathematics Coimbatore India Sathyan Eye Hospital Coimbatore Glaucoma Foundation Ramanathapuram Coimbatore India Ashland University Department of Mathematics and Computer Science OH United States

ISBN: (纸本)9798350370249

Glaucoma, a progressive eye disease known as the 'Silent thief of sight,' poses a significant challenge in early detection and treatment. the conventional method of diagnosing glaucoma involves manual evaluation of retinal fundus images by trained ophthalmologists, which is not only time-consuming but also prone to human error (inter-observer variability) the aim of this study is to propose a novel approach for glaucoma detection utilizing a deep learning architecture called AlterNet-k. the key innovation lies in leveraging the AlterNet-k model, which is specifically designed to capture intricate patterns in medical images. By leveraging deep learning techniques, we pre-process raw retinal images and apply data augmentation to enhance model performance. the segmented AlterNet-k model effectively distinguishes between healthy and glaucomatous retinas, achieving a remarkable accuracy of 96.67%. this demonstrates the potential of deep learning models, such as AlterNet-k, in revolutionizing glaucoma diagnosis and improving patient outcomes by enabling early intervention strategies. © 2024 IEEE.

关键词： Deep learning

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Enhancing Gesture Recognition performance using Optimized Event-Based Data Sample Lengths and Crops 15

Enhancing Gesture Recognition Performance using Optimized Ev...

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15th Latin American symposium on Circuits and Systems (LASCAS)

作者： Schechter, Celio Roni Gomes, Jose Gabriel R. C. Univ Fed Rio de Janeiro COPPE Programa Engn Eletr Rio De Janeiro Brazil

ISBN: (纸本)9798350381221;9798350381238

In recent years, event-based cameras have found their use in a growing number of real-time and mobile computer vision applications. this technology has the potential of improving sign language gesture recognition, a complex visual recognition task that benefits from capturing high-speed motion. Focusing on three spiking neural network (SNN) recognition systems - STBP, SLAYER and DECOLLE - applied on the SL-Animals-DVS dataset, this study investigates novel approaches for preprocessing Dynamic Vision Sensor input signals and modifying network architectures to enhance training speed, reduce memory load, and improve recognition performance. the experiments reveal that optimizing sample lengths and employing random crop techniques during training significantly enhance SNN performance. While optimizing computational resources, this work emphasizes the importance of exposing SNNs to more information from training samples, highlighting the trade-off between sample length, performance, memory usage, and training time.

关键词： Supervised Learning Event-Based Camera Gesture Recognition high Speed Recognition Spiking Neural Networks SNN Dynamic Vision Sensors DVS Sign-Language

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