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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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Shortening Design Time through Multiplatform Simulations with a Portable OpenCL Golden-model: the LDPC Decoder Case

Shortening Design Time through Multiplatform Simulations wit...

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Annual IEEE symposium on Field-Programmable Custom computing Machines (FCCM)

作者： G. Falcao M. Owaida D. Novo M. Purnaprajna N. Bellas C.D. Antonopoulos G. Karakonstantis A. Burg P. Ienne Instituto de Telecomunicações Department of Electrical and Computer Engineering University of Coimbra Portugal Department of Computer and Communications Engineering University of Thessaly Greece École Polytechnique Fédérale de Lausanne Switzerland

Hardware designers and engineers typically need to explore a multi-parametric design space in order to find the best configuration for their designs using simulations that can take weeks to months to complete. For example, designers of special purpose chips need to explore parameters such as the optimal bit width and data representation. this is the case for the development of complex algorithms such as Low-Density Parity-Check (LDPC) decoders used in modern communication systems. Currently, high-performance computing offers a wide set of acceleration options, that range from multicore CPUs to graphics processing units (GPUs) and FPGAs. Depending on the simulation requirements, the ideal architecture to use can vary. In this paper we propose a new design flow based on Open CL, a unified multiplatform programming model, which accelerates LDPC decoding simulations, thereby significantly reducing architectural exploration and design time. Open CL-based parallel kernels are used without modifications or code tuning on multicore CPUs, GPUs and FPGAs. We use SOpen CL (Silicon to Open CL), a tool that automatically converts Open CL kernels to RTL for mapping the simulations into FPGAs. To the best of our knowledge, this is the first time that a single, unmodified Open CL code is used to target those three different platforms. We show that, depending on the design parameters to be explored in the simulation, on the dimension and phase of the design, the GPU or the FPGA may suit different purposes more conveniently, providing different acceleration factors. For example, although simulations can typically execute more than 3× faster on FPGAs than on GPUs, the overhead of circuit synthesis often outweighs the benefits of FPGA-accelerated execution.

关键词： Kernel Parity check codes Field programmable gate arrays Computational modeling Decoding Hardware Algorithm design and analysis

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Improved Radiation Estimation at high Energy Facility for Public Health

Improved Radiation Estimation at High Energy Facility for Pu...

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

作者： Jerry Cao-Xue Hongfang Zhang Adam Stavola Bence Budavari Chiman Kwan Hongyi Wu Jiang Li William G. Enloe High School Raleigh NC Department of Electrical and Computer Engineering Old Dominion University Norfolk VA Thomas Jefferson National Accelerator Facility Newport News VA Applied Research LLC Rockville MD Force Projection Sector Johs Hopkins University Applied Physics Laboratory Laurel MD Department of Electrical & Computer Engineering University of Arizona Tuscon AZ

ISBN: (数字)9798331540906

ISBN: (纸本)9798331540913

Long-term exposure to ionizing radiation can lead to the development of cancer and other potentially fatal diseases. Estimating radiation levels at high-energy particle accelerators is crucial for ensuring the safety of personnel, the public, and the environment. this paper explores the application of transformer neural networks, focusing on five different attention mechanisms—Full Attention, Prob Attention, AutoCorrelation, Fourier Attention, and Wavelet Attention—for estimating radiation levels at the thomas Jefferson National Accelerator Facility (JLab). the goal is to improve the accuracy of radiation estimation by training models using data from key radiation sensors. Four hundred trials were conducted for each attention mechanism, and performance was evaluated using metrics including R 2 . the results demonstrated that Prob Attention and AutoCorrelation outperformed the other mechanisms, offering substantial improvements over previous models. these findings highlight the importance of selecting and fine-tuning attention mechanisms in transformers for complex time-series analysis tasks in radiation monitoring.

关键词： Wavelet transforms Training Radiation monitoring Attention mechanisms Time series analysis Estimation Transformers Sensors Safety Autocorrelation

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An efficient system for grammatical error correction on mobile devices

An efficient system for grammatical error correction on mobi...

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International Conference on Semantic computing

作者： Sourabh Vasant Gothe Sushant Dogra Mritunjai Chandra Chandramouli Sanchi Barath Raj Kandur Raja Samsung R&D Institute Bangalore Karnataka India

ISBN: (数字)9781728188997

ISBN: (纸本)9781728189000

As per the analysis by lemongrad, among 1,121 million English speakers globally, there are 743 million nonnative and 378 million native English speakers (7:3 Ratio). With the increasing number of non-native English speakers, there has been a lot of ongoing research on automated Grammatical Error Correction (GEC). Despite the advances in this field, a GEC system for mobile devices with a low model size, quick inference time and high accuracy is a pressing need. Existing solutions for mobile devices are server-based, which poses a potential risk of data privacy to the user. To this end, we propose an on-device hybrid system which consists of various components, such as preprocessor with spell-checker, Deep Neural Network (DNN) model, N-gram language model, rule-corrector for GEC. User's input text is passed through the proposed system sequentially to get the grammatically correct, contextually enhanced and profanity checked output sentence. Our novel system is at par with other available models for mobile devices with inference time of 95ms, module size of 11.6 MB and F 0.5 score of 52.3 on CoNLL-2014 [1] (test).

关键词： Data privacy Semantics computer architecture Pressing Mobile handsets User experience Error correction

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Predictive Modeling of Cryptocurrency Prices Using Machine Learning Algorithms

Predictive Modeling of Cryptocurrency Prices Using Machine L...

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International Conference on computing and Networking Technology (ICCNT)

作者： Tanjim Mahmud Tahmina Akter Rishita Chakma Tikle Barua Mohammad Shahadat Hossain Karl Andersson Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Dept. of Computer Science and Engineering Port City International University Chittagong Bangladesh Dept. of Computer Science and Engineering University of Chittagong Chittagong Bangladesh Cybersecurity Laboratory Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Cryptocurrencies, such as Bitcoin, Binance, Ethereum, FTX, and XRP, are decentralized digital assets known for their volatile nature and potential as investment instruments. Accurate price prediction is crucial for informed investment decisions. this study explores the feasibility of various modeling techniques on diverse data structures and features for predicting the prices of these cryptocurrencies. We utilize daily and high-frequency price data to classify and predict prices using deep learning and machine learning techniques, including LSTM, Bi-LSTM, GRU, linear regression, and SGD regression. Our findings indicate that daily price projections achieve an accuracy of 0.99, outperforming more complex deep learning and machine learning models. Compared to benchmark results, our approach demonstrates superior performance, with the highest scores achieved by the applied statistical methods and advanced algorithms. this research highlights the effectiveness of deep learning and machine learning models in cryptocurrency price forecasting, offering a foundation for further exploration in the field and emphasizing the significance of sample size in predictive modeling.

关键词： Deep learning Machine learning algorithms Accuracy Statistical analysis Computational modeling Linear regression Predictive models Prediction algorithms Long short term memory Investment

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Alzheimer’s Disease Detection through Spatially Attentive Enhanced Low-Mid-high Level Feature Modelling using Deep CNN

Alzheimer’s Disease Detection Through Spatially Attentive E...

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International Conference on computing and Networking Technology (ICCNT)

作者： Santosh Kumar Tripathy Poonkuntran Shanmugham Rishabh Sharma Yash Doshi Computer Science and Engineering National Institute of Technology Patna Bihar India School of Computing Science and Engineering VIT Bhopal University Bhopal-Indore Highway Kothrikalan Sehore Madhya Pradesh India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Alzheimer’s disease (AD) severely affects patients and has become a warning concern around the world. Several AI-based solutions that have extensively explored convolutional neural networks (CNN) for diagnosis. Most of these approaches only utilise high-level features for diagnosis. However, to solve any non-linear and complex problem, the low-level and mid-level features are as important as the high-level features. Such an approach is missing in the AD classification. To solve this, we proposed a novel way to utilise low, mid, and high (LMH) features for classification. We design the suggested framework using the backbone of a multilayer atrous CNN. We enhance the LMH features by exploiting features from a multicolumn atrous convolution with varying kernel shapes, which can extract features at multiple scales. We adopt, modify, and apply a spatial attention mechanism over the enhanced LMH features to emphasise important features or attributes in the spatial domain. A multilayer of dense neurons receives the fused and enhanced spatially attentive LMH features for AD classification. We demonstrate extensive experiments using an open-source AD dataset available on Kaggle. the proposed framework achieves better performance than current best practices.

关键词： Attention mechanisms Shape Convolution Neurons Feature extraction Nonhomogeneous media Convolutional neural networks Alzheimer's disease Kernel Best practices

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Competitive analysis of on-line randomized call control in cellular networks

Competitive analysis of on-line randomized call control in c...

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International symposium on Parallel and Distributed Processing (IPDPS)

作者： I. Caragiaannis C. Kaklamanis E. Papaioannou Comput. Technol. Inst. Patras Univ. Greece Computer Technology Institute and Department of Computer Engineeringand Informatics University of Patras Rio Greece

ISBN: (纸本)0769509908

In this paper we address an important communication issue arising in cellular (mobile) networks that utilize Frequency Division Multiplexing (FDM) technology. In such networks, many users within the same geographical region can communicate simultaneously with other users of the network using distinct frequencies. the spectrum of the available frequencies is limited; thus, efficient solutions to the call control problem are essential. the objective of the call control problem is, given a spectrum of available frequencies and users that wish to communicate, to maximize the number of users that communicate without signal interference. Using competitive analysis, we study the performance of algorithm p-RANDOM; an intuitive on-line randomized call control algorithm proposed previously for cellular networks. We give upper and lower bounds of its competitive ratio against oblivious adversaries as a function of the parameter p. Optimizing the upper bound function, we prove that there exists a 2.651-competitive randomized call control algorithm. In this way, we significantly improve the best known upper bound on the competitiveness of on-line randomized call control which was 2.934.

关键词： Intelligent networks Land mobile radio cellular systems Base stations Mobile communication Interference Communication system control Frequency division multiplexing Wireless communication high-speed networks Informatics

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Parallel information theory based construction of gene regulatory networks 08

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Proceedings of the 15th international conference on high performance computing

作者： Jaroslaw Zola Maneesha Aluru Srinivas Aluru Department of Electrical and Computer Engineering Iowa State University Ames IA Department of Genetics Cellular and Developmental Biology Iowa State University Ames IA

ISBN: (纸本)9783540898931

We present a parallel method for construction of gene regulatorynetworks from large-scale gene expression data. Our method integratesmutual information, data processing inequality and statisticaltesting to detect significant dependencies between genes, and efficientlyexploits parallelism inherent in such computations. We present a novelmethod to carry out permutation testing for assessing statistical significancewhile reducing its computational complexity by a factor of Θ(n2),where n is the number of genes. Using both synthetic and known regulatorynetworks, we show that our method produces networks of qualitysimilar to ARACNE, a widely used mutual information based *** present a parallelization of the algorithm that, for the first time, allowsconstruction of whole genome networks from thousands of microarrayexperiments using rigorous mutual information based *** report the construction of a 15,147 gene network of the plant Arabidopsisthaliana from 2,996 microarray experiments on a 2,048-CPUBlue Gene/L in 45 minutes, thus addressing a grand challenge problemin the NSF Arabidopsis 2010 initiative.

关键词： gene networks mutual information parallel computational biology systems biology

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Improved Denoising for Multi-frame Image Restoration with Convolutional Neural Networks

Improved Denoising for Multi-frame Image Restoration with Co...

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International Conference on computing and Networking Technology (ICCNT)

作者： Hitesh Kalra Kiran Lokesh Maney Omkaresh S. Kulkarni K. Ranjith Singh Gunveen Ahluwalia T. Surulivel Rajan Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement - UG Mangement JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science and Engineering (AI & ML) Vishwakarma Institute of Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Multi-body photograph restoration is a key application of state-of-the-art image processing. With the improvement of cutting-edge convolutional neural networks (CNNs), it has come to be modern-day viable to repair photos with improved denoising performance. CNNs allow the present-day modern-day complicated non-linear features with high accuracy and fast inference. A CNN-based totally multi-body image healing (MFIR) technique is proposed that is able to seize the spatial correlations between noise and texture in multi-body snapshots. Experiments on benchmark datasets show that our technique achieves overall performance, demonstrating the effectiveness of modern-day CNNs in this challenge. Our approach is powerful to extraordinary multi-frame noise levels and works higher than CNNs with the best unmarried-body entry. Additionally, our proposed technique will be extended to different picture healing responsibilities with diverse today’s corpora.

关键词： Correlation Accuracy Noise reduction Noise Maintenance engineering Benchmark testing Image restoration Convolutional neural networks Noise measurement Noise level

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Constructing Suffix Tree of Endogenous Feature on FPGA for high-performance Identification of DNA Signatures

Constructing Suffix Tree of Endogenous Feature on FPGA for H...

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IEEE symposium on Bioinformatics and Bioengineering (BIBE)

作者： Lin Feng Angela Jean Chong Poh Leng Lai Danbo School of Computer Engineering Nanyang Technological University Singapore

Identification of DNA signatures is an important application which has been empowered by the increasing availability of thousands of bacterial and viral genomes through the next-generation sequencing (NGS) technologies. It is computationally challenging. In exploration for the solution, the suffix tree has been proposed as a data structure well suited to analyzing genomic sequences because it enables the storage of long strings in a logical, indexed manner for fast retrieval. We propose a new algorithm for identification of DNA signatures, based on exploitation of the endogenous features of a genomic sequence. these features are revealed in a construction of suffix array (an implementation of the generalized suffix tree). Furthermore, to greatly speed up the time-consuming process, the core algorithm is mapped and routed on to a Field-Programable Gate Array (FPGA) for parallel implementation. In this paper, we will focus primarily on the relationship of the data structure and the features associated. We will then describe the deployment of the hybrid computing system on a Hyper Transport compliant architecture. Illustrations are always given to clarify the technical details.

关键词： Genomics Arrays Vegetation DNA Bioinformatics Field programmable gate arrays Algorithm design and analysis

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Multi-Path Routing in IoT Network Using a Hybrid Optimization Technique

Multi-Path Routing in IoT Network Using a Hybrid Optimizatio...

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International Conference on computing and Networking Technology (ICCNT)

作者： Nitesh Chouhan Awanit Kumar Naresh Kumar Sangam University Bhilwara Department of IT MLV Textile & Engineering College Bhilwara India Computer Science and Engineering Department Sangam University Bhilwara India Computer Science and Engineering Department UIET Kurukshetra University Kurukshetra India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Multipath routing in IoT networks represents a sophisticated approach to enhance the efficiency and reliability of communication by leveraging multiple paths for data transmission. In the context of 5G and 6 G, which is expected to deliver ultra-high data rates and support a massive number of interconnected devices, multipath routing becomes crucial for optimizing network performance. Hence in this Paper a multipath routing technique is devised that utilize newly developed optimization model, namely Tunicate swarm grey wolf optimization (TSGWO) in IoT Network. Initially, the simulation of nodes is done in IoT Network. thereafter, the choice of cluster head (CH) is performed with Fractional Gravitational Search algorithm (FGSA). Furthermore, the process of multipath routing is performed using developed TSGWO wherein the routing path is chosen by employing the fitness attributes such as trust factors and QoS parameters. the QoS parameters involve link lifetime, delay, energy and distance. the path having less distance is chosen as optimum path with fitness. the developed technique effectually performs multipath routing by combining the benefits of both optimization techniques. thereafter, the maintenance of route is done in IoT network for recovering fissure of link with DRINA. the proposed TSGWO outperformed other methods with maximal average Residual energy of 2.17 J, maximal link lifetime of 0.077 sec, maximal PDR of $\mathbf{9 7. 9 8 \%}$, and maximal throughput of 430.50 Kbps.

关键词： performance evaluation Wireless sensor networks Optimization models Quality of service Routing throughput Maintenance Delays Data communication Optimization

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