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A Cloud-based Analytics Architecture for the Application of Online Machine Learning algorithms on Data Streams in Consumer-centric Internet of things Domains 5

A Cloud-based Analytics Architecture for the Application of ...

引用

5th international conference on Internet of things, Big Data and Security (IoTBDS)

作者： Zschoernig, theo Windolph, Jonah Wehlitz, Robert Franczyk, Bogdan Inst Appl Informat InfAI Goerdelerring 9 D-04109 Leipzig Germany Univ Leipzig Informat Syst Inst Grimmaische Str 12 D-04109 Leipzig Germany Wroclaw Univ Econ Business Informat Inst Ul Komandorska 118-120 PL-53345 Wroclaw Poland

ISBN: (纸本)9789897584268

the increasing number of smart devices in private households has lead to a large quantity of smart homes worldwide. In order to gain meaningful insights into their generated data and offer extended information and added value for consumers, data analytics architectures are essential. In addition, the development and improvement of machine learning techniques and algorithms in the past years has lead to the availability of powerful analytics tools, which have the potential to allow even more sophisticated insights at the cost of changed challenges and requierements for analytics architectures. However, architectural solutions, which offer the ability to deploy flexible, machine learning-based analytics pipelines on streaming data, are missing in research as well as in industry. In this paper, we present the motivation and a concept for machine learning-based data processing on streaming data for consumer-centric Internet of things domains, such as smart home. this approach was evaluated in terms of its performance and may serve as a basis for further development and discussion.

关键词： Internet of things Stream processing Online Machine Learning Kappa Architecture

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Hardware/Software Co-Design of a control and data acquisition system for Computed Tomography 9

Hardware/Software Co-Design of a control and data acquisitio...

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9th international conference on Modern Circuits and Systems Technologies (MOCAST)

作者： Passaretti, Daniele Pionteck, thilo Otto von Guericke Univ Magdeburg Germany Res Campus STIMULATE Magdeburg Germany

ISBN: (数字)9781728166872

ISBN: (纸本)9781728166872

In the last decades, the continuous advances of computed tomography sensor technology and the different medical use cases have opened new challenges for data acquisition systems: i.e. controlling and synchronising different subsystems, and acquiring and managing the huge amount of data, under real-time requirements. System-on-Chip-FPGAs are perfectly suited for all these tasks that involve hardware and software. In this paper, we propose a new acquisition system architecture for Clinical Computed Tomography (CT) for SoC, based on Hardware/Software Co-Design methodology. We discuss how to partition the different tasks (controlling, acquisition, synchronisation) on Programmable Logic (PL) and processing System (PS) parts. Moreover, we analyse the interactions between these parts for having the performance required from the CT application. this architecture is able to control and synchronise all components inside the CT, acquire data via the optical channel, and process real-time image data in the order of 10 Gb/s, inside a single SoC.

关键词： Computed tomography accelerator architectures high-performance communication Hardware/Software Co-Design System-on-Chip

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Pimiento: A Vertex-Centric Graph-processing Framework on a Single Machine 19th

Pimiento: A Vertex-Centric Graph-Processing Framework on a S...

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19th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Huang, Jianqiang Qin, Wei Wang, Xiaoying Chen, Wenguang Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Qinghai Univ Dept Comp Technol & Applicat Xining 810016 Peoples R China

ISBN: (纸本)9783030389611;9783030389604

Here, we describe a method for handling large graphs with data sizes exceeding memory capacity using minimal hardware resources. this method (called Pimiento) is a vertex-centric graph-processing framework on a single machine and represents a semi-external graph-computing system, where all vertices are stored in memory, and all edges are stored externally in compressed sparse row data-storage format. Pimiento uses a multi-core CPU, memory, and multi-threaded data preprocessing to optimize disk I/O in order to reduce random-access overhead in the graph-algorithm implementation process. An on-the-fly update-accumulated mechanism was designed to reduce the time that the graph algorithm accesses disks during execution. Our experiments compared external this method with other graph-processing systems, including GraphChi, X-Stream, and FlashGraph, revealing that Pimiento achieved 7.5x, 4x, 1.6x better performance on large real-world graphs and synthetic graphs in the same experimental environment.

关键词： Vertex-centric Graph processing Semi-external Passing message Asynchronous update accumulation

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parallel SCC Detection Based on Reusing Warps and Coloring Partitions on GPUs 20th

Parallel SCC Detection Based on Reusing Warps and Coloring P...

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20th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Hou, Junteng Wang, Shupeng Wu, Guangjun Ma, Bingnan Zhang, Lei Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Natl Comp Network Emergency Response Tech Team Coordinat Ctr China Beijing Peoples R China

ISBN: (纸本)9783030602451;9783030602444

Detecting strongly connected components (SCCs) in directed graphs is crucial for analyzing the structure of graphs. It can accelerate the process of large-scale graphs with applying GPUs to graph computation. However, GPU-based SCC detection still faces the challenge from graphs, especially real-world graphs, skewing and strict synchronization requirement. In this paper, we proposed a new paradigm of identifying SCCs with reusing warps and coloring partitions. the scheme of reusing warps assigns multiple vertices to each virtual warp at one time, which greatly reduces the number of warps that are applied but assigned few tasks. Furthermore, we proved that the partitions formed by coloring independently contains all SCCs in them, so we use coloring to identify more partitions and parallel detect SCCs in each partition to get more SCCs. Finally, the colored partitions are deeply combined with the following traversal to optimize the algorithm. We conduct extensive theoretical and experimental analysis to demonstrate the efficiency and accuracy of our approach. the experimental results expose that our approach can achieve 7.23x, 30.55x, 1.75x and 1.26x speedup for SCC detection using NVIDIA K80 compared with algorithms of Tarjan, Barnat, Hong and Slota respectively.

关键词： Strongly connected components detection GPUs Reusing warps Coloring partitions Partitions combined with traversal

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Various DNN-HMM architectures Used in Acoustic Modeling with Single-Speaker and Single-Channel 9th

Various DNN-HMM Architectures Used in Acoustic Modeling with...

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9th international conference on Statistical Language and Speech processing, SLSP 2021

作者： Psutka, Josef V. Vaněk, Jan Pražák, Aleš Department of Cybernetics University of West Bohemia Pilsen Czech Republic NTIS - New Technologies for the Information Society UWB Pilsen Czech Republic

ISBN: (纸本)9783030895785

In this paper, we discuss some interesting features of training a special acoustic model for only one speaker with a constant acoustic background (acoustic channel). Currently, the LF-MMI method achieves the best results in many speech recognition tasks. A typical LF-MMI training procedure uses a special 1-state HMM topology that has different pdfs at the self-loop and forward transitions. We would like to discuss the replacement of this typical LF-MMI HMM by different types of HMM topologies (1-, 2- and 3-state HMM topologies that have outputs associated with states). Next, we discuss the advantages of using biphone context modeling over using the triphone context or even simpler context-free monophone. We also address the effect of the amount of training data and the context of DNN on WER, and all this with regard to a special acoustic model with one speaker and an almost constant acoustic channel. © 2021, Springer Nature Switzerland AG.

关键词： Topology

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Rise the Momentum: A Method for Reducing the Training Error on Multiple GPUs 19th

Rise the Momentum: A Method for Reducing the Training Error ...

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19th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Tang, Yu Yin, Lujia Zhang, Zhaoning Li, Dongsheng Natl Univ Def Technol Sci & Technol Parallel & Distributed Lab Changsha Peoples R China

ISBN: (纸本)9783030389611;9783030389604

Deep neural network training is a common issue that is receiving increasing attention in recent years and basically performed on Stochastic Gradient Descent or its variants. Distributed training increases training speed significantly but causes precision loss at the mean time. Increasing batchsize can improve training parallelism in distributed training. However, if the batchsize is too large, it will bring difficulty to training process and introduce more training error. In this paper, we consider controlling the total batchsize and lowering batchsize on each GPU by increasing the number of GPUs in distributed training. We train Resnet50 [4] on CIFAR-10 dataset by different optimizers, such as SGD, Adam and NAG. the experimental results show that large batchsize speeds up convergence to some degree. However, if the batchsize of per GPU is too small, training process fails to converge. Large number of GPUs, which means a small batchsize on each GPU declines the training performance in distributed training. We tried several ways to reduce the training error on multiple GPUs. According to our results, increasing momentum is a well-behaved method in distributed training to improve training performance on condition of multiple GPUs of constant large batchsize.

关键词： Multiple GPUs Batchsize Distributed training Momentum

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Reconfigurable HW-SW Co-design Platform for Lung Cancer Detection and Classification

Reconfigurable HW-SW Co-design Platform for Lung Cancer Dete...

引用

international conference on VLSI Design

作者： Ayushparth Sharma Kusum Lata The LNM Institute of Information Technology Jaipur India

High-performance electronics has fueled the rich emergence of medical imaging applications that led to the exponential growth in treatment and diagnostic solutions of various medical problems. High-throughput and Energy-efficient systems are required to enable the development of complex medical imaging applications. this article presents an energy-efficient hardware-software (HW-SW) co-design of a scalable and reconfigurable image segmentation/classification streaming-based processing platform explored at various design abstraction levels. Optimized algorithms and architectural techniques achieve significant savings in energy consumption and operational time. the proposed platform has been implemented on Xilinx Spartan-6 FPGA board and co-simulated with Xilinx system generator, enabled real-time processing of CT scans for pulmonary nodule detection. Optimized pipelining and scheduling have minimized the memory requirements to few kB. parallel architecture has been employed achieving 10× higher energy-efficiency compared to serial counterpart and reduced execution period by 70 ×. Clinical validation shows that parallel architecture introduces 5-7 % error in nodule characteristic determination in comparison to serial one.

关键词： Memory management Lung cancer Very large scale integration Streaming media Energy efficiency Real-time systems parallel architectures

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Automatic stereo disparity search range detection on parallel computing architectures 17th

Automatic stereo disparity search range detection on paralle...

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17th international conference on Image Analysis and Recognition, ICIAR 2020

作者： Perera, Ruveen Low, Tobias University of Southern Queensland Toowoomba Australia

ISBN: (纸本)9783030503468

From the earliest to the state-of-the-art algorithms, stereo depth estimation techniques often require a disparity search range (DSR) value to be chosen manually. However, the optimal DSR varies from one scene to another making the results depend on the operator input and operator having to optimize the configuration by using trial-and-error. In this paper we present a novel technique suitable for parallel computing architectures which detects the optimum DSR for a given scene without requiring operator input or prior knowledge of the scene. Experiments on stereo images from Middlebury, KITTI and Sceneflow bench-mark datasets indicate that our technique can automatically extract a suitable DSR value from different scenes, which leads to better consistency in matching. the technique presented here can be used with existing stereo algorithms to limit the size of the cost volume as it is being built (without requiring pre-processing or operator input). A CUDA based implementation of our method can deliver real-time performance on consumer grade GPUs at high frame rates. © Springer Nature Switzerland AG 2020.

关键词： Graphics processing unit

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Power Profile Measurement and System Design Analysis for a Wearable Photovoltaic Application 9

Power Profile Measurement and System Design Analysis for a W...

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IEEE 9th international Power Electronics and Motion Control conference (IPEMC-ECCE Asia)

作者： Bagci, F. Selin Kim, Katherine A. Lin, Ting-Yu Liu, Yu-Chen Natl Taiwan Univ Elect Engn Dept Taipei Taiwan Natl Ilan Univ Elect Engn Dept Yilan Taiwan

ISBN: (纸本)9781728153018

As wearable devices are gaining popularity for applications, like fitness trackers and health monitoring devices, an important aspect is how to most effectively power them. Solar photovoltaic (PV) power has great potential for powering wearable devices, but faces some challenges. the PV panels used in wearable applications will face different angles and capture different amounts of sunlight, which is challenging to model. To determine a realistic power profile for PV wearables, the power generated from each PV panel is measured for a variety of usage scenarios. A measurement device was developed that can simultaneously measure and log data of four identical PV panels used in a wearable bag application under realistic conditions. After data was collected, a modeling and fitting procedure was used to calculate the irradiance level on each PV panel. the resulting model was used to determine the PV profiles over time for the maximum power point (MPP) voltage, current, and power. these realistic profiles were then used to properly design, simulate, and compare three different converter architectures that transfer power from the wearable PV panels to the load.

关键词： differential power processing energy harvesting irradiance measurements photovoltaic systems solar photovoltaics wearables

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HDF5-Based I/O Optimization for Extragalactic HI Data Pipeline of FAST 19th

HDF5-Based I/O Optimization for Extragalactic HI Data Pipeli...

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19th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Ji, Yiming Yu, Ce Xiao, Jian Tang, Shanjiang Wang, Hao Zhang, Bo Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Chinese Acad Sci CAS Key Lab FAST NAOC Beijing Peoples R China

ISBN: (纸本)9783030389611;9783030389604

the Five-hundred-meter Aperture Spherical Radio Telescope (FAST), which is the largest single-dish radio telescope in the world, has been producing a very large data volume with high speed. So it requires a high performance data pipeline to covert the huge raw observed data to science data product. However, the existing solutions of pipelines widely used in radio data processing cannot tackle this situation efficiently. the paper proposes a pipeline architecture for FAST based on HDF5 format and several I/O optimization strategies. First, we design the workflow engine driving the various tasks efficiently in the pipeline;second, we design a common radio data storage specification on the top of HDF5 format, and also developed a fast converter to map the original FITS format to the new HDF5 format;third, we apply several concrete strategies to optimize the I/O operations, including chunks storage, parallel reading/writing, on-demand dump, and stream process etc. In the experiment of processing 700 GB of FAST data, the results show that HDF5 based data structure without other optimizations was 1.7 times faster than original FITS format. If chunk storage and parallel I/O optimization are applied, the overall performance can reach 4.5 times as the original one. Moreover, due to the good expansibility and flexibility, our solution of FAST pipeline can be adapted to other radio telescopes.

关键词： FAST FITS HDF5 Pipeline in parallel High performance I/O

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