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检索条件"任意字段=9th International Conference on Algorithms and Architectures for Parallel Processing"

共 2826 条记录，以下是271-280 订阅

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A Low-Complexity, Conflict-Free Memory Access Scheme for Flexible NTT /INTT Architecture

A Low-Complexity, Conflict-Free Memory Access Scheme for Fle...

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international conference on Integrated Circuits and Microsystems (ICICM)

作者： Chiayi Kuo Jingwen Huang Yuqiao Huang Tao Su School of Electronics and Information Technology (School of Microelectronics) Sun Yat-sen University Guangzhou China

ISBN: (数字)9798331509453

ISBN: (纸本)9798331520243

Number theoretic Transform (NTT) and inverse NTT (INTT) are widely used to accelerate polynomial multi-plications in lattice-based cryptosystems. this paper proposes a low-complexity coefficient memory access pattern for an in-place NTT/INTT design. It simplifies the data path between the processing elements (PEs) and the memory banks. Moreover, to avoid read-after-write conflicts and achieve minimal (I)NTT computing cycles, we present a strategy that dynamically calculates and inserts necessary idle cycles during (I)NTT processing. Based on the proposed low-complexity and conflict-free memory access scheme, we develop a flexible NTT/INTT unified architecture. Our design allows the configuration of various parameter sets and parallelism setups at compile-time, while supporting polynomials of multiple degrees and moduli of different sizes at run-time. Experimental results on FPGA show that our design achieves better area efficiency and flexibility compared with previous works.

关键词： Micromechanical devices Memory management Transforms parallel processing Polynomials Complexity theory Logic Cryptography Field programmable gate arrays

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An MPI-based Algorithm for Mapping Complex Networks onto Hierarchical architectures 27th

An MPI-based Algorithm for Mapping Complex Networks onto Hie...

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27th international European conference on parallel and Distributed Computing (Euro-Par)

作者： Predari, Maria Tzovas, Charilaos Schulz, Christian Meyerhenke, Henning Humboldt Univ Berlin Germany Heidelberg Univ Heidelberg Germany

processing massive application graphs on distributed memory systems requires to map the graphs onto the system9;s processing elements (PEs). this task becomes all the more important when PEs have non-uniform commun... 详细信息

ISBN: (纸本)9783030856656;9783030856649

processing massive application graphs on distributed memory systems requires to map the graphs onto the system's processing elements (PEs). this task becomes all the more important when PEs have non-uniform communication costs or the input is highly irregular. Typically, mapping is addressed using partitioning, in a two-step approach or an integrated one. parallel partitioning tools do exist;yet, corresponding mapping algorithms or their public implementations all have major sequential parts or other severe scaling limitations. In this paper, we propose a parallel algorithm that maps graphs onto the PEs of a hierarchical system. Our solution integrates partitioning and mapping;it models the system hierarchy in a concise way as an implicit labeled tree. the vertices of the application graph are labeled as well, and these vertex labels induce the mapping. the mapping optimization follows the basic idea of parallel label propagation, but we tailor the gain computations of label changes to quickly account for the induced communication costs. Our MPI-based code is the first public implementation of a parallel graph mapping algorithm;to this end, we extend the partitioning library PARHIP. To evaluate our algorithm's implementation, we perform comparative experiments with complex networks in the million- and billion-scale range. In general our mapping tool shows good scalability on up to a few thousand PEs. Compared to other MPI-based competitors, our algorithm achieves the best speed to quality trade-off and our quality results are even better than non-parallel mapping tools.

关键词： Load balancing Process mapping Hierarchical architectures parallel label propagation

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Towards a Privacy, Secured and Distributed Clinical Data Warehouse Architecture 9th

Towards a Privacy, Secured and Distributed Clinical Data War...

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9th international conference, FDSE

作者： thantilage, Ranul Deelaka Le-Khac, Nhien-An Kechadi, M-Tahar Univ Coll Dublin Sch Comp Sci Dublin Ireland Insight Ctr Data Analyt Dublin Ireland

ISBN: (纸本)9789811980688;9789811980695

Reputed organisations are always prompting Data Warehouses (DWs), which are essential for storing and mining their historical datasets. When it comes to the healthcare industry, DWs are becoming ever so imperative, as efficient storage for medical data is vital for one's health while mining it and seeking new insights. While clinical datasets are very complex, their timely integration and analysis are crucial to providing excellent care for patients. this research aims to provide an efficient data warehousing solution with multiple privacy and security measures integrated by design. Securing the data at all stages: during data input, exploration, pre-processing, selection, analysis, and presentation, is very challenging. this research explores data security from a holistic perspective and possible distributed analysis mechanisms while streamlining data sharing between healthcare centres to increase efficiency and better patient treatments. this study also considers security and privacy issues at all stages of the data warehousing process (data lifecycle) to ensure its correct handling and use. We focus on distributed clinical data warehouse architectures. We also describe the main requirements of a clinical data warehouse for the whole data lifecycle, Data Capture, Acquisition Management, Archiving, Sharing, Reporting, Analysis, and Privacy and Security. the proposed architecture is evaluated considering existing state-of-the-art concerning data analysis and sharing capabilities while ensuring data security and privacy.

关键词： Data warehouse Clinical data Cardiology architectures Data security Data privacy Clinical data warehouse requirements

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Invisible and Multi-triggers Backdoor Attack Approach on Deep Neural Networks through Frequency Domain

Invisible and Multi-triggers Backdoor Attack Approach on Dee...

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IEEE international conference on Signal and Image processing (ICSIP)

作者： Fengxue Sun Bei Pei Guangyong Chen School of Cyber Science and Engineering Southeast University Nanjing China National Engineering Research Center of Classified Protection and Safeguard Technology for Cybersecurity Shanghai China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

In recent years, the security of deep neural networks (DNNs) has become a research hotspot with widespread deployments of machine learning models in our daily life. Backdoor attack is an emerging security threat to DNNs, where the infected model will output malicious targets for the images containing specific triggers. However, most existing backdoor attack approaches have only single trigger, and the triggers are often visible to human eyes. In order to overcome these limitations, in this paper, we propose an invisible and multi-triggers backdoor attack (IMT -BA) approach to simultaneously generate four invisible triggers. Firstly, in our IMT-BA approach, we divide the whole images into four blocks and apply Discrete Cosine Transform (DCT) algorithm to generate four invisible triggers aiming at four targets. Secondly, our IMT -BA approach can be easily deployed in real world without any knowledge of the hyperparameters and architectures of the DNNs models. Finally, we do the experiments with MNIST and CIFAR-10 datasets and the experiment results show our IMT -BA approach can fool both DNNs models and Human Visual System (HVS) with high success rate.

关键词： Image processing Training data Artificial neural networks Transforms Machine learning Visual systems Outsourcing

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Hybrid Feature Based 6D Pose Tracking under Binocular Vision for Automated Micro-assembly

Hybrid Feature Based 6D Pose Tracking under Binocular Vision...

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Mechatronics and Robotics Engineering (ICMRE), international conference on

作者： Tianyu Xie Xianmin Zhang Hai Li Jianqin Zhang Guangdong Key Laboratory of Precision Equipment and Manufacturing Technology South China University of Technology Guangzhou China

6D tracking is important in robotic based assembly of the MEMS. this paper presents a 6D pose tracking method using hybrid feature for automated micro-assembly. Firstly, the micro-assembly system which mainly comprises of a parallel positioning robot that can largely rotate around three axes, a serial 6-axis robot for manipulation, and a binocular microvision modular is built. To realize tracking of the 6D pose under large rotation, a hybrid feature based tracking scheme that can be used under multi-view is developed. Experiments are conducted to verify the performance of the proposed method. the results show that the tracking performance can be effectively improved by simultaneously using the point feature and line feature. Besides, the averaged translation error and rotation error are smaller than 0.031 mm and 0.3 degree, respectively.

关键词： Micromechanical devices Visualization Mechatronics Simulation Image processing Robot vision systems Manipulators

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LEARNED PARAMETERIZED CONVOLUTIONAL APPROXIMATION OF IMAGE FILTERS 36

LEARNED PARAMETERIZED CONVOLUTIONAL APPROXIMATION OF IMAGE F...

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36th international ECMS conference on Modelling and Simulation, ECMS 2022

作者： Chaganova, Olga Grigoryev, Anton Moscow Institute of Physics and Technology National Research University Institutskiy per. 9 Dolgoprudny141701 Russia Institute for Information Transmission Problems RAS Bolshoy Karetny per. 19 Moscow127051 Russia

ISBN: (纸本)9783937436777

Multilayer neural networks are considered universal approximators applicable to a wide range of problems. there are quite detailed theoretical and applied studies for fully connected networks, while for convolutional networks the results are more scarce. In this paper, we tested the approximating capability of deep neural networks with typical architectures like ConvNet, ResNet, and UNet as applied to classical image processing algorithms. Canny edge detector and grayscale morphological dilation with the disk structuring element were selected as target algorithms. As we have seen, even relatively lightweight neural models are able to approximate a filter with fixed parameters. Since image processing algorithms are parameterized, we considered different approaches to the parameterization of the neural networks and discovered that even the simplest one, which is an adding parameters in the input image channels, works well for filter with low parameter count. Also, we measured an inference time of a neural network approximation and a classical implementation of the grayscale dilation with the disk structuring element. Starting from a certain radius, a neural network works faster than the algorithm even on one core of the CPU without fine-tuning the architecture for performance, thus confirming the viability of ConvNets as a differentiable approximation technique for optimization of classical-based methods. ©ECMS Ibrahim A. Hameed, Agus Hasan, Saleh Abdel-Afou Alaliyat (Editors) 2022

关键词： Computational efficiency

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Comparison of Microsoft Azure, Google Cloud, and Amazon Web Services for High-Performance Computing

Comparison of Microsoft Azure, Google Cloud, and Amazon Web ...

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Information Technology and Digital Applications (ICITDA), IEEE international conference on

作者： Sonia Tisa Putri I Gusti Lanang Agung Oka Cahyadi Pradipta Alringga Rizky Widangsa Irnayanti Dwi Kusuma Ghulam Alvi Rahmat Ary Mazharuddin Shiddiqi Department of Informatics Institute Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798331510732

ISBN: (纸本)9798331510749

this study investigates computing-optimized instances from Microsoft Azure, Google Cloud, and Amazon Web Services (AWS). Each platform is rigorously evaluated using established high-performance computing (HPC) benchmarks, including HPCC and HPCG, to assess their computational efficiency comprehensively. these benchmarks aim to demonstrate that the optimal cloud solution is application-specific, varying based on workload characteristics. this study offers insights into optimizing cloud resource allocation and management strategies through a comprehensive analysis. the findings are expected to guide organizations in achieving optimal high-performance computing configurations within cloud environments for more efficient computational resources and big data analytics.

关键词： Measurement Web services Clouds High performance computing Organizations Benchmark testing parallel processing Computational efficiency Resource management Meteorology

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A Spatial Alignment Method for Medical Ear Canal Based on Simplified Transformer Module and MultiScale Iterative Structure

A Spatial Alignment Method for Medical Ear Canal Based on Si...

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Robotics, Control and Automation (ICRCA), international conference on

作者： Chaokun Luan Qinglei Zhang Ying Zhou Weiping Liu Biao Shu Zhijian Li Xiaoyao Liu Haopeng Yang Logistics Engineering College Shanghai Maritime University Shanghai China Shanghai Smart Surgery Center Shanghai MicroPort MedBot (Group) Co. Ltd Shanghai China Key Accounts Department Shanghai MicroPort MedBot (Group) Co. Ltd Shanghai China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

Alignment of deformed medical images is a challenging task in which the images are subject to large deformations due to some constraints in the image acquisition process. In the field of deformation image alignment, convolutional neural network-based methods have a local acceptance domain and cannot handle deformed images. In this regard, we propose a new multi-scale parallel architecture model of Transformer-CNN, which takes advantage of Transformer's goodness at modeling remote dependencies, while simplifying the module and reducing the number of model parameters. the multiscale iterative structure of CNN can acquire local information and connect feature information at different scales. Experimental results on the medical ear canal dataset show that the method has excellent performance in terms of alignment accuracy, outperforming existing deep learning and some traditional alignment methods, while reducing the number of model parameters to some extent, making it lightweight.

关键词： Irrigation Accuracy Deformation Ear parallel processing Transformers Iterative methods Convolutional neural networks Robots Biomedical imaging

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HyperDMA: Enhancing High-Performance Computing and AI Workflows with Advanced Data Transfer Capabilities

HyperDMA: Enhancing High-Performance Computing and AI Workfl...

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international conference on Integrated Circuits and Microsystems (ICICM)

作者： Minghao Peng Haiyan Chen Yang Zhang Sheng Liu National University of Defense Technology Changsha China

ISBN: (数字)9798331509453

ISBN: (纸本)9798331520243

In the field of high-performance computing (HPC) and artificial intelligence (AI), the performance and flexibility of direct memory access (DMA) controllers are crucial. this paper proposes a novel DMA architecture: HyperDMA, which achieves efficient data transfer and processing through a hierar-chical structure and a multi-core cooperative mechanism. Within HyperDMA, we design a data processing module that supports N -dimensional tensor transfer, gather transfer of non-contiguous source data, as well as broadcast and segmented transfer in a multi-core context. Finally, HyperDMA is integrated into the V-DSP computational core of the multi-core accelerator Fusion, and RTL simulation is performed using a simulation platform to evaluate its performance under different transfer modes and data granularities. the results show that HyperDMA significantly outperforms traditional DMA in data transfer efficiency and bandwidth utilization. Compared with not using HyperDMA, we achieve at least 39% performance improvement of GEMM algorithm. Compared with not using HyperDMA, we achieve at least 39% performance improvement of GEMM algorithm. this research provides an efficient and reliable solution for data transfer in HPC and AI applications.

关键词： Tensors Multicore processing High performance computing System performance Computer architecture parallel processing Data transfer Hardware Reliability Artificial intelligence

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Proceedings of EduHPC 2021: Workshop on Education for High-Performance Computing, Held in conjunction with SC 2021: the international conference for High Performance Computing, Networking, Storage and Analysis

Proceedings of EduHPC 2021: Workshop on Education for High-P...

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9th IEEE/ACM Workshop on Education for High-Performance Computing, EduHPC 2021

ISBN: (纸本)9781665411165

the proceedings contain 7 papers. the topics discussed include: experience and practice teaching an undergraduate course on diverse heterogeneous architectures;the hour of cyberinfrastructure (hour of ci): early findings from pilot study to build cyber literacy for GIScience;educating HPC users in the use of advanced computing technology;visualizing parallel dynamic programming using the thread safe graphics library;automating testing of visual observed concurrency;lightning talks of EDuHPC 2021;and peachy parallel assignments (EDuHPC 2021).

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