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检索条件"任意字段=Conference on Parallel and Distributed Methods for Image Processing"

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Performance Comparison of Julia distributed Implementations of Dirichlet Process Mixture Models

Performance Comparison of Julia Distributed Implementations ...

引用

IEEE International conference on Big Data (Big Data)

作者： Huang, Ruizhu Xu, Weijia Wang, Yinzhi Liverani, Silvia Stapleton, Ann E. Univ Texas Austin Texas Adv Comp Ctr Austin TX 78712 USA Queen Mary Univ London Sch Math Sci London England Univ N Carolina Dept Biol & Marine Biol Wilmington NC USA

ISBN: (纸本)9781728108582

The Dirichlet process mixture model (DPMM), one of the nonparametric Bayesian mixture models, is receiving more and more attentions from the statistical learning community. It has been demonstrated its great potentials in clustering analysis. When computational complexity increases as numbers of observations and features grow, the serial algorithms of DPMM need long processing time and cannot handle large volume of data on a single machine. To improve the computational efficiency, several parallel methods and implementations were proposed and implemented with C++ and Julia programming languages by different authors and publicly available on GitHub or published as a Julia package for users to download. However, the scalability of multi-cores and multi-node has not been thoroughly evaluated and compared among different implementations, even for multiple implementations of the same proposed distributed PPMM method. We selected two recent Julia implementations of parallel sampler via sub-cluster splits method proposed by Change and Fisher and performed a scalability comparison on supercomputer clusters. This paper presents some insights on the applicability of both implementations in terms of increasing number of dimensions of the feature space and provides some potential improvement strategies on multi-node scalability.

关键词： Julia software profiling performance analysis DPMM

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Enhancing 2D Representation via Adjacent Views for 3D Shape Retrieval 17

Enhancing 2D Representation via Adjacent Views for 3D Shape ...

引用

IEEE/CVF International conference on Computer Vision (ICCV)

作者： Xu, Cheng Li, Zhaoqun Qiu, Qiang Leng, Biao Jiang, Jingfei Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Duke Univ Durham NC 27706 USA Beihang Univ Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing Peoples R China Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha Peoples R China

ISBN: (纸本)9781728148038

Multi-view shape descriptors obtained from various 2D images are commonly adopted in 3D shape retrieval. One major challenge is that significant shape information is discarded during 2D view rendering through projection. In this paper, we propose a convolutional neural network based method, Neighbor-Center Enhanced Network, to enhance each 2D view using its neighboring ones. By exploiting cross-view correlations, Neighbor-Center Enhanced Network learns how adjacent views can be maximally incorporated for an enhanced 2D representation to effectively describe shapes. We observe that a very small amount of, e.g., six, enhanced 2D views, are already sufficient for panoramic shape description. Thus, by simply aggregating features from six enhanced 2D views, we arrive at a highly compact yet discriminative shape descriptor. The proposed shape descriptor significantly outperforms state-of-the-art 3D shape retrieval methods on the ModelNet and ShapeNet-Core55 benchmarks, and also exhibits robustness against object occlusion.

关键词： Convolutional neural networks

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Research on differential privacy for medical health big data processing 20

Research on differential privacy for medical health big data...

引用

20th International conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2019

作者： Hu, Yugu Ge, Lina Zhang, Guifen Qin, Donghong College of Information Science and Engineering Guangxi University for Nationalities Key Laboratory of Network Communication Engineering Guangxi University for Nationalities Nanning China College of Information Science and Engineering Guangxi University for Nationalities Nanning China

ISBN: (纸本)9781728126166

As big data, medical digitalization, and wearable devices continue to evolve, these technologies are driving the advancement of clinical medicine, genomics, and wearable health while also posing a risk of privacy breaches. For the disclosure of medical privacy issues, some methods and systems combining differential privacy protection applied in the clinical, genomic and wearable fields have been summarized, analyzed and compared. Finally, through the investigation and research, the assumptions and prospects for future research hotspots are given. © 2019 IEEE.

关键词： Big data

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Medical Computing Visual Synchronization Using WebSocket Protocol and Web Rendering

Medical Computing Visual Synchronization Using WebSocket Pro...

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2019 IEEE Canadian conference of Electrical and Computer Engineering, CCECE 2019

作者： Zhang, Qi School of Information Technology Illinois State University Normal IL61761 United States

ISBN: (纸本)9781728103198

Web based 3D medical data computing and visual synchronization can provide clinical users with critical information for distributed diagnosis and treatment. However, due to the limited internet bandwidth, high computational cost and increasing data size, real-time medical data exploration and information sharing on internet is still a challenging task. Therefore, we developed a new internet based medical data computing and information visual distribution system, in which graphics processing units were utilized for parallel medical data computing and visualization, cloud was used for data storage, Apache HTTP Server and MySQL were employed for medical image management and analysis, and WebSocket Protocol was used for medical information visual synchronization. The major contributions include algorithms developed for high performance medical computing, and medical information sharing and visual synchronization on distributed web browsers. The developed system can provide medical users with intuitive feedback for collaborative diagnosis and remote therapy. © 2019 IEEE.

关键词： Graphics processing unit

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CNNP-v2:An Energy Efficient Memory-Centric Convolutional Neural Network Processor Architecture 1

CNNP-v2:An Energy Efficient Memory-Centric Convolutional Neu...

引用

1st IEEE International conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Choi, Sungpill Bong, Kyeongryeol Han, Donghyeon Yoo, Hoi-Jun Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon South Korea

ISBN: (纸本)9781538678848

An energy efficient memory-centric convolutional neural network (CNN) processor architecture is proposed for smart devices such as wearable devices or internet of things (IoT) devices. To achieve energy-efficient processing, it has 2 key features: First, 1-D shift convolution PEs with fully distributed memory architecture achieve 3.1TOPS/W energy efficiency. Compared with conventional architecture, even though it has massively parallel 1024 MAC units, it achieve high energy efficiency by scaling down voltage to 0.46V due to its fully local routed design. Next, fully configurable 2-D mesh core-to-core interconnection support various size of input features to maximize utilization. The proposed architecture is evaluated 16mm(2) chip which is fabricated with 65nm CMOS process and it performs real-time face recognition with only 9.4mW at 10MHz and 0.48V.

关键词： convolutional neural network (CNN) memory-centric architecture low-power image processing energy efficient digital circuit

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Lattice Boltzmann Model based 3D image Segmentation 2

Lattice Boltzmann Model based 3D Image Segmentation

引用

2nd International Symposium on Application of Materials Science and Energy Materials, SAMSE 2018

作者： Chen, Yu Lu, Dongxiang School of Information Engineering Yancheng Teachers University Yancheng Jiangsu China

image segmentation plays a very important role in three-dimensional volume data processing. In three-dimensional segmentation, the curve evolution method based on geometric active contour model and level set method is one of the most widely concerned methods. Among these methods, the CV model is a hot research topic. Based on the Munfor-Shah model, CV model is proposed by Chan and Vese. Because of the huge amount of data in three-dimensional images, the computation of solving CV model is very large, which consumes a lot of time. In this study, a 3D image segmentation algorithm based on Lattice Boltzmann model is proposed. The experiment shows our method is effective. The algorithm is simple and efficient. More importantly, the LB model has natural parallelism. Especially, our method can be implemented on massively parallel image processing platform, for example, FPGAs, DSPs and GPUs. © Published under licence by IOP Publishing Ltd.

关键词： image segmentation

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A parallel programming model research based on heterogeneous multi-core embedded processor

A parallel programming model research based on heterogeneous...

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International conference on Applications and Techniques in Cyber Intelligence, ATCI 2018

作者： Cai, YuXin Tang, ZhiWei The Third Research Institute of Ministry of Public Security BiSheng Road Number 339 Pudong Shanghai China

ISBN: (纸本)9783319987750

Today is the era of information explosion, efficient methods for massive data processing are becoming a hotspot. MapReduce is a popular parallel programming model and widely used in large-scale data parallel computing. This paper proposes a parallel programming model based on heterogeneous multi-core embedded processor. On the base of ordinary computer completing computing, we make optimization and improvement, and use the method of combining embedded dual-core ARM processors with multiple high performance FPGAs to complete complex processing. Xilinx Zynq device is used as the hardware platform. By selecting Sobel image processing, histogram, matrix multiply and illumination enhancement cases, we test the parallel programming model. The results show that the speedup is 84.62x versus CPU-based implementation. In addition, it is proven that this parallel programming model is suitable for the CPU-FPGA heterogeneous system which can be used as cluster cloud computing node. Meanwhile, we make a comparison of our model with other references and verify the efficient of the programming model in this paper. © Springer Nature Switzerland AG 2019.

关键词： Field programmable gate arrays (FPGA)

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Continual Local Training For Better Initialization Of Federated Models

Continual Local Training For Better Initialization Of Federa...

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IEEE International conference on image processing

作者： Xin Yao Lifeng Sun BNRist Tsinghua University

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Federated learning (FL) refers to the learning paradigm that trains machine learning models directly in the decentralized systems consisting of smart edge devices without transmitting the raw data, which avoids the heavy communication costs and privacy concerns. Given the typical heterogeneous data distributions in such situations, the popular FL algorithm Federated Averaging (FedAvg) suffers from weight divergence and thus cannot achieve a competitive performance for the global model (denoted as the initial performance in FL) compared to centralized methods. In this paper, we propose the local continual training strategy to address this problem. Importance weights are evaluated on a small proxy dataset on the central server and then used to constrain the local training. With this additional term, we alleviate the weight divergence and continually integrate the knowledge on different local clients into the global model, which ensures a better generalization ability. Experiments on various FL settings demonstrate that our method significantly improves the initial performance of federated models with few extra communication costs.

关键词： Training Data models Servers Task analysis Computational modeling distributed databases Optimization

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Deep Spatio-Temporal Attention Model for Grain Storage Temperature Forecasting

Deep Spatio-Temporal Attention Model for Grain Storage Tempe...

引用

International conference on parallel and distributed Systems (ICPADS)

作者： Shanshan Duan Weidong Yang Xuyu Wang Shiwen Mao Yuan Zhang Henan Key Laboratory of Grain Photoelectric Detection & Control Key Laboratory of Grain Information Processing & Control College of Information Science & Engineering Henan University of Technology Zhengzhou Henan China California State University Sacramento CA USA Auburn University Auburn AL USA

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

Temperature is one of the major ecological factors that affect the safe storage of grain. In this paper, we propose a deep spatio-temporal attention mode to predict stored grain temperature, which exploits the historical temperature data of stored grain and the meteorological data of the region. In this proposed model, we use the Sobel operator to extract the local spatial factors, and leverage the attention mechanism to obtain the global spatial factors of grain temperature data and temporal information. In addition, a convolutional neural network (CNN) is used to learn features of external meteorological factors. Finally, the spatial factors of grain pile and external meteorological factors are combined to predict future grain temperature using long short-term memory (LSTM) based encoder and decoder models. Experiment results show that the proposed model achieves higher predication accuracy compared with the traditional methods.

关键词： Temperature distribution Meteorological factors Biological system modeling Predictive models Feature extraction Data models Forecasting

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image Recognition with MapReduce Based Convolutional Neural Networks 10

Image Recognition with MapReduce Based Convolutional Neural ...

引用

IEEE 10th Annual Ubiquitous Computing, Electronics and Mobile Communication conference (UEMCON)

作者： Leung, Jackie Chen, Min SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

ISBN: (纸本)9781728138855

Convolutional neural networks (CNNs) have gained global recognition in advancing the field of artificial intelligence and have had great successes in a wide array of applications including computer vision, speech and natural language processing. However, due to the rise of big data and increased complexity of tasks, the efficiency of training CNNs have been severely impacted. To achieve state-of-art results, CNNs require tens to hundreds of millions of parameters that need to be fine-tuned, resulting in extensive training time and high computational cost. To overcome these obstacles, this work takes advantage of distributed frameworks and cloud computing to develop a parallel CNN algorithm. Close examination of the implementation of MapReduce based CNNs as well as how the proposed algorithm accelerates learning are discussed and demonstrated through experiments. Results reveal high accuracy in classification and improvements in speedup, scaleup and sizeup compared to the standard algorithm.

关键词： Convolutional neural networks Spark MapReduce

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