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Deadline-Aware Offloading for high-throughput Accelerators 27

Deadline-Aware Offloading for High-Throughput Accelerators

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27th IEEE International symposium on high-performance computer architecture (HPCA)

作者： Yeh, Tsung Tai Sinclair, Matthew D. Beckmann, Bradford M. Rogers, Timothy G. AMD Res Santa Clara CA USA Purdue Univ W Lafayette IN 47907 USA Natl Chiao Tung Univ Hsinchu Taiwan Univ Wisconsin Madison WI 53706 USA

ISBN: (纸本)9781665422352

Contemporary GPUs are widely usedfor throughput-oriented data-parallel workloads and increasingly are being considered for latency-sensitive applications in datacenters. Examples include recurrent neural network (RNN) inference, network packet processing, and intelligent personal assistants. these data parallel applications have both high throughput demands and real-time deadlines (40ps-7ms). Moreover, the kernels in these applications have relatively few threads that do not fully utilize the device unless a large batch size is used. However, batching forces jobs to wait, which increases their latency, especially when realistic job arrival times are considered. Previously, programmers have managed the tradeoffs associated with concurrent, latency-sensitive jobs by using a combination of GPU streams and advanced scheduling algorithms running on the CPU host. Although GPU streams allow the accelerator to execute multiple jobs concurrently, prior state-of-the-art solutions use the relatively distant CPU host to prioritize the latency-sensitive GPU tasks. thus, these approaches are forced to operate at a coarse granularity and cannot quickly adapt to rapidly changing program behavior. We observe thatfine-grain, device-integrated kernel schedulers efficiently meet the deadlines of concurrent, latency-sensitive GPUjobs. To overcome the limitations of software-only, CPU-side approaches, we extend the GPU queue scheduler to manage real-time deadlines. We propose a novel laxity-aware scheduler (LAX) that uses information collected within the GPU to dynamically vary job priority based on how much laxity jobs have before their deadline. Compared to contemporary GPUs, 3 state-of-the-art CPU-side schedulers and 6 other advanced GPU-side schedulers, LAX meets the deadlines of 1.7X - 5.0X more jobs and provides better energy-efficiency, throughput, and 99-percentile tail latency.

关键词： GPGPU job scheduling laxity

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Soft-Clipping Swish: A Novel Activation Function for Deep Learning 15

Soft-Clipping Swish: A Novel Activation Function for Deep Le...

引用

IEEE 15th International symposium on Applied Computational Intelligence and Informatics (SACI)

作者： Mercioni, Marina Adriana Holban, Stefan Politehn Univ Timisoara Dept Comp Sci Timisoara Romania

ISBN: (纸本)9781728195438

this study aims to contribute to the improvement of the network's performance through developing a novel activation function. Over time, many activation functions have been proposed in order to sole the issues of the previous functions. We note here more than 50 activation functions that have been proposed, some of them being very popular such as sigmoid, Rectified Linear Unit (ReLU), Swish, Mish but not only. the main idea of this study that stays behind our proposal is a simple one, based on a very popular function called Swish, which is a composition function, having in its componence sigmoid function and ReLU function. Starting from this activation function we decided to ignore the negative region in the w as the Rectified Linear Unit does but being different than that one mentioned through a nonlinear curse assured by the Swish positive region. the idea has been come up from a current function called Soft Clipping. We tested this proposal on more datasets in computer Vision on classification tasks showing its high potential, here we mention MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100 using two popular architectures: LeNet-5 and ResNet20 version 1.

关键词： activation function classification clipping computer Vision Deep Learning LeNet ReLU residual ResNet Sigmoid soft Swish

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Disease Detection And Severity Prediction Of Pomegranate Fruit

Disease Detection And Severity Prediction Of Pomegranate Fru...

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Poulin Paul S Manishankar R Satheesh Kumar Dept. of Computer Science and Engineering Sahrdaya College of Engineering and Technology Thrissur Kerala Dept. of Computer Science and Engineering Jawaharlal College of Engineering and Technology Palakkad Kerala

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

this paper presents an integrated framework for the comprehensive analysis of diseases affecting pomegranate fruit. the suggested system, which makes use of deep learning techniques, includes semantic segmentation for diseased region localization, multiclass illness classification, and image processing-based severity assessment. Initially, four common diseases—Alternaria, Anthracnose, Bacterial Blight, and Cer-cospora—are successfully identified through the use of a Convolutional Neural Network (CNN) for multiclass classification. then, annotated photos are used as training data for Semantic Segmentation, which uses the UNet architecture to identify the unhealthy spots within the fruit. the area and percentage of the segmented region are determined, and thresholds for high, medium, and low severity levels are then defined. the segmentation results are then used as inputs for severity estimate. Early identification and intervention tactics are made easier by the severity level prediction, which is based on the proportion of the segmented region. In order to improve pomegranate fruit management methods, the suggested framework provides a thorough method of disease analysis by combining categorization, semantic segmentation, and severity prediction.

关键词： Location awareness Deep learning Microorganisms Semantic segmentation Training data computer architecture Convolutional neural networks Diseases

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ConnectedUNets++: Mass Segmentation from Whole Mammographic Images 17th

ConnectedUNets++: Mass Segmentation from Whole Mammographic...

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17th International symposium on Visual computing, ISVC 2022

作者： Sarker, Prithul Sarker, Sushmita Bebis, George Tavakkoli, Alireza Department of Computer Science and Engineering University of Nevada Reno United States

ISBN: (纸本)9783031207129

Deep learning has made a breakthrough in medical image segmentation in recent years due to its ability to extract high-level features without the need for prior knowledge. In this context, UNet is one of the most advanced medical image segmentation models, with promising results in mammography. Despite its excellent overall performance in segmenting multimodal medical images, the traditional U-Net structure appears to be inadequate in various ways. there are certain U-Net design modifications, such as MultiResUNet, Connected-UNets and AU-Net, that have improved overall performance in areas where the conventional U-Net architecture appears to be deficient. Following the success of UNet and its variants, we have presented two enhanced versions of the Connected-UNets architecture: ConnectedUNets+ and ConnectedUNets++. In ConnectedUNets+, we have replaced the simple skip connections of Connected-UNets architecture with residual skip connections, while in ConnectedUNets++, we have modified the encoder decoder structure along with employing residual skip connections. We have evaluated our proposed architectures on two publicly available datasets, the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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Exploring the Potential of Error-permissive Communication in Multi-FPGA-based Edge computing

Exploring the Potential of Error-permissive Communication in...

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IEEE International symposium on Embedded Multicore Socs (MCSoC)

作者： Akram Ben Ahmed Ryousei Takano Takahiro Hirofuchi Digital Architecture Research Center National Institute of Advanced Industrial Sciences and Technology Koto-ku Tokyo JAPAN

ISBN: (纸本)9781665465007

In recent years, Edge computing has been attracting a lot of attention as it has shown its ability to mitigate some issues found in Cloud computing (e.g., high latency, centralization issues, programmability, etc.), Moving computation from the Cloud to the Edge has resulted in an exponential increase in the number of devices to be connected. With such an increase, interconnection has become a major design and performance factor, especially in terms of power efficiency. thus, the question that a lot of research has been trying to answer is how to connect such devices in a power-efficient way while making sure to maintain high-performance and accuracy at the lowest cost. In this paper, we investigate the Error-permissive communication paradigm and its potential to answer the aforementioned question. In particular, we focus on the importance of using this paradigm on Multi-FPGA systems which have gained significant attention in Edge computing and can be a good candidate for Error-permissive communication. We also present a preliminary evaluation of a novel concept to reduce the power consumption by undervolting the supply voltage of the serial transceivers, and we show its potential to reduce the total FPGA power consumption by up to 26%.

关键词： performance evaluation Electric potential Cloud computing Power demand Multicore processing Voltage Transceivers

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Application of Machine Learning Models for Predictive Analytics on AI4I 2020

Application of Machine Learning Models for Predictive Analyt...

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IEEE Annual Ubiquitous computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Hard Rakesh Shah Brian Haley Eman Abdelfattah School of Computer Science & Engineering Sacred Heart University Fairfield CT

ISBN: (数字)9798331540906

ISBN: (纸本)9798331540913

One of the critical aspects in modern industrial operations is predictive maintenance. It involves leveraging advanced technologies such as machine learning to anticipate equipment failures before they occur. the research not only contributes to the broader understanding of machine learning applications in industrial contexts but also addresses the imperative need for predictive maintenance in optimizing operational efficiency and minimizing downtime and which machine learning model serves best over the dataset selected, incorporating how it can be made more accurate for better performance. the results of the experiments demonstrate that Random Forest and Support Vector Machines achieved comparable, high performance in case of high-quality and Medium-quality machines where the F 1 score reached $85.71 \%$ and $90.32 \%$ respectively.

关键词： Support vector machines Analytical models Accuracy Predictive models Mobile communication Predictive analytics Random forests Predictive maintenance Context modeling

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Informing Static Mapping and Local Scheduling of Stream Programs with Trace Analysis

Informing Static Mapping and Local Scheduling of Stream Prog...

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International symposium on Symbolic and Numeric Algorithms for Scientific computing (SYNASC)

作者： Michail Boulasikis Flavius Gruian Gareth Callanan Jörn Janneck Dept. of Computer Science Lund University Lund Sweden

Due to their natural and inherent way of capturing concurrency, dataflow descriptions of stream programs have seen prevalent usage in fields such as video processing, networks and scientific computing. they are often deployed on manycore, heterogeneous and distributed architectures. Despite robust research on the topic, obstacles still exist in evaluating the performance of stream programs accurately, especially without a complete implementation down to the selected platforms. In this work we introduce and provide a proof of concept for an automated design space exploration flow where causation traces and simulation are used to inform the mapping and scheduling of stream programs running on distributed platforms, using only high-level models of the architecture. the basic idea behind the flow is to profile the designs under well performing mappings and schedules very early in the design process to accurately gauge performance potential.

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GA-SFS: A Two-Stage Hybrid Algorithm for Feature Selection in Biomedical Data

GA-SFS: A Two-Stage Hybrid Algorithm for Feature Selection i...

引用

IEEE Annual computing and Communication Workshop and Conference (CCWC)

作者： Brian Bales Monitha Davuluri Rashida Hasan Department of Computer Science California State University Northridge California USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

In biomedical data analysis, feature selection is crucial, particularly for high-dimensional datasets where redundant or irrelevant features might affect model performance. Biomedical datasets introduce additional challenges, such as high dimensionality, small sample sizes, and complex data structures. Addressing these issues often requires hybrid, adaptive, or ensemble-based algorithms that can improve model robustness and interpretability. To meet these demands, this paper introduces GA-SFS, a two-stage hybrid algorithm that combines Genetic Algorithms (GA) with Sequential Forward Selection (SFS) to select informative features in complex biomedical datasets effectively. Experimental results on multiple datasets show that GA-SFS outperforms traditional feature selection methods in classification accuracy.

关键词： Measurement Adaptation models Accuracy Biological system modeling Feature extraction Robustness Classification algorithms Bioinformatics Time complexity Genetic algorithms

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Beyond the Surface: the Necessity for Detailed Metrics in Corporate Sustainability Reports

Beyond the Surface: The Necessity for Detailed Metrics in Co...

引用

International Green computing Conference

作者： Chetan Choppali Sudarshan Aman Arora Vidya A. Chhabria Arizona State University

ISBN: (数字)9798331507862

ISBN: (纸本)9798331507879

the smartphone industry is advancing rapidly, emphasizing the development of smaller, energy-efficient devices with enhanced features. However, this progress often overlooks the significant environmental impacts of these ubiquitous devices. While corporate sustainability reports from mobile product companies aim to address these concerns, they typically provide high-level overviews that obscure critical details, particularly regarding the carbon footprints (CFP) of individual components such as processors and memories. this paper investigates the discrepancy between the overall CFP trends reported and the actual increasing CFP contributions of processors over the years. through a detailed analysis, the inadequacies in current reporting practices are highlighted, underscoring the necessity for more granular data and metrics in corporate sustainability reports. Specific, detailed metrics are proposed for inclusion in these reports to enhance transparency and help align the chip architecture, design, and manufacturing communities toward addressing relevant sustainability-related challenges.

关键词： Measurement Industries Program processors Green products computer architecture Market research Energy efficiency Manufacturing Sustainable development Carbon footprint

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OpenUVR: an Open-Source System Framework for Untethered Virtual Reality Applications 27

OpenUVR: an Open-Source System Framework for Untethered Virt...

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30th IEEE International symposium on Software Reliability Engineering (ISSRE)

作者： Rohloff, Alec Allen, Zackary Lin, Kung-Min Okrend, Joshua Nie, Chengyi Liu, Yu-Chia Tseng, Hung-Wei Appl Res Associates Berkeley CA 94720 USA Red Hat Inc Berkeley CA USA Univ Calif Berkeley Berkeley CA 94720 USA Riverside Technol Inc Riverside CA USA SUNY Stony Brook Stony Brook NY 11794 USA Univ Calif Riverside Riverside CA 92521 USA

ISBN: (纸本)9781665403863

Advancements in heterogeneous computing technologies enable the significant potential of virtual reality (VR) applications. To offer the best user experience (UX), a system should adopt an untethered, wireless-network-based architecture to transfer VR content between the user and the content generator. However, modern wireless network technologies make implementing such an architecture challenging, as VR applications require superior video quality-with high resolution, high frame rates, and very low latency. this paper presents OpenUVR, an open-source framework that uses commodity hardware components to satisfy the demands of interactive, real-time VR applications. OpenUVR significantly improves UX through a redesign of the system stack and addresses the most time-sensitive issues associated with redundant memory copying in modern computing systems. OpenUVR presents a cross-layered VR datapath to avoid redundant data operations and computation among system components, OpenUVR customizes the network stack to eliminate unnecessary memory operations incurred by mismatching data formats in each layer, and OpenUVR uses feedback from mobile devices to remove memory buffers. Together, these modifications allow OpenUVR to reduce VR application delays to 14.32 ms, meeting the 20 ms minimum latency in avoiding motion sickness. As an open-source system that is fully compatible with commodity hardware, OpenUVR offers the research community an opportunity to develop, investigate, and optimize applications for untethered, high-performance VR architectures.

关键词： Wireless networks System performance Systems architecture computer architecture Virtual reality Hardware Real-time systems

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