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15th International Conference on Electronics, computer and Computation (ICECCO)

作者： Apollos, Ezeogu C. Adeshina, Steve A. Nnanna, Nwojo A. Nile Univ Nigeria Dept Comp Sci Abuja Nigeria

ISBN: (纸本)9781728151601

In this paper, memristor based Computation-in-Memory (CiM) architecture is introduced to mitigate today's challenges faced with the conventional CMOS technologies and von Neumann architecture due to the emergence of Big data era. Memristor material has shown through design and simulation as presented in this paper where necessary to have high switching speed, non-volatile capability, compact density using crossbar array, chaotic and non-binary ability, almost zero power and current leakage thus making memristor-based Computation-in-Memory architecture the needed technology revolution to mitigate these Big data computing limits caused by the conventional computer architecture and CMOS process technologies. the CMOS technologies and von Neumann architecture have reached fabrication physical limit as transistor scaling goes below 45nm technology node thus resulting to increasing delays that occur in the metal interconnect for signal propagation in transistors, power leakages, low data reliability, security issues, and high cost of developing and building CMOS chip-fabrication facilities as scaling goes down below 45nm.

关键词： Big Data computer architecture Computation in Memory Memristor von Neumann

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the Impact of a Security Model in a Fog computing Environment 1

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7th International symposium on Security in computing and Communications, SSCC 2019

作者： Fakude, Nhlakanipho C. Tarwireyi, Paul Adigun, Mathew O. Department of Computer Science University of Zululand Private Bag X1001 KwaDlangezwa3886 South Africa

ISBN: (数字)9789811548253

ISBN: (纸本)9789811548246

the Internet of things (IoT) is an emerging technology that has received much attention in recent years due to its ability to process data in real-time, speed up awareness and respond quickly to events. IoT aims to bring every object online, thereby generating increasing amounts of data. this exponential growth of network traffic calls for faster, efficient and scalable ways of processing. Critical IoT applications such as health monitoring, emergency response and industrial control require high availability and very low latency to guarantee good user experiences. Fog computing has been proposed as a solution to decrease latency and network congestion in IoT environments. However, security in the fog-IoT layer is still a very open area that is yet to receive much attention. this paper presents a security model that addresses data security, both at rest and in transit on end-devices and fog devices. the study uses a latency-sensitive online application (EEG tractor game) to evaluate the performance impact of the security model in an IoT based fog environment. iFogSim was used to model IoT and Fog environments and measure the effect of security in terms of latency, network congestion, energy consumption, and cost. Experimental results demonstrate the viability of our model. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Fog computing

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Nodule Generation of Lung CT Images Using a 3D Convolutional LSTM Network 15th

Nodule Generation of Lung CT Images Using a 3D Convolutional...

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15th International symposium on Visual computing, ISVC 2020

作者： Olulana, Kolawole Owolawi, Pius Tu, Chunling Abe, Bolanle Computer Systems Engineering Department Tshwane University of Technology Pretoria South Africa Electrical Engineering Department Tshwane University of Technology Pretoria South Africa

ISBN: (纸本)9783030645588

In the US, the American Cancer Society report for 2020 estimates about 228,820 new cases which could result in 135,720 deaths which translates to 371 deaths per day compared to the overall daily cancer death of 1660. the Cancer Society of South Africa (CANSA) reports that lung cancer and other chronic lung diseases are leading causes of death nationally. Research in this area is necessary in order to reduce the number of reported deaths through early detection and diagnosis. A number of studies have been done using datasets for Computed Tomography (CT) images in the diagnosis and prognosis by oncologists, radiologists and medical professionals in the healthcare sector and a number of machine learning methods are being developed using conventional neural networks (CNN) for feature extraction and binary classification with just a few researches making use of combined (hybrid) methods that have shown the capability to increase performance and accuracy in prediction and detection of early stage onset of lung cancer. In this paper, a combined model is proposed using 3D images as input to a combination of a CNN and long short-term memory (LSTM) network which is a type of recurrent neural network (RNN). the hybridization which often lead to increase need for computational resources will be adjusted by improving the nodule generation to focus only on the search space around the lung nodules, this proposed model requires less computation resources, avoiding the need to adding the whole 3D CT image into the network, therefore only the region of interest near candidate regions with nodules will be pre-processed. the results of previous traditional CNN architecture is compared to this combined 3D Convolutional LSTM for nodule generation. In the experiments, the proposed hybrid model overperforms the traditional CNN architecture which shows how much improvement a hybridization of suitable models can contribute to lung cancer research. © 2020, Springer Nature Switzerland AG.

关键词： Long short-term memory

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Optimal implementation of in-band network management for high-radix switches 13th

Optimal implementation of in-band network management for hig...

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13th Conference on Advanced computer architecture, ACA 2020

作者： Cao, Jijun Lai, Mingche Qi, Xingyun Dai, Yi Pang, Zhengbin College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9789811581342

To manage (such as configuring and monitoring) the numerous network chips and its ports efficiently, the in-band management technology is used in the interconnect network of high performance computing systems. However, with the rapid development of network switching chips towards the higher radix, the traditional in-band management implementation of ring structure faces the problem of delay performance scalability. the work proposed two optimized implementation structures for the in-band management, four-quadrant double-layer ring and four-quadrant star ring to solve the problem. the results of resource consumption assessment and delay performance simulation showed that in the high-radix switching chips with 64, 80, 96, 112, 128, 144, and 160 ports, the occupancies of LUT (Look Up Table) resources of the four-quadrant double-layer ring and star ring structures increased by an average of $$5.46\%$$ and $$1.71\%$$ compared to the traditional ring structure, respectively. Meanwhile, the occupancies of LUTRAM (Look Up Table memory) resources increased by an average of $$30.89\%$$ and $$21.81\%$$;that of FF (Flip Flop) resources by an average of $$3.86\%$$ and $$0.19\%$$;the forward delay of management packets decreased by $$25.75\%$$ and $$21.81\%$$, respectively. Considering both resource consumption and delay performance, the star ring was an ideal structure to deal with the problem of delay performance scalability among the in-band management structures, which can be applied to realize the in-band management for the higher-radix switching chips in the future. © Springer Nature Singapore Pte Ltd 2020.

关键词： Table lookup

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Ontology for Structuring a Digital Databases for Decision Making in Grain Production

Ontology for Structuring a Digital Databases for Decision Ma...

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International Conference on Semantic computing

作者： Ricardo A. Neves Paulo E. Cruvinel Federal Institute of São Paulo São João da Boa Vista SP Brazil Post-Graduation Program in Computer Science - Federal University of São Carlos SP Brazil

ISBN: (数字)9781728188997

ISBN: (纸本)9781728189000

this paper presents an ontology for the structuring of digital databases with the objective of acting in a cloud environment and meeting big data sources in the agricultural context of grain production. Its conception is structured in three stages: the first stage presents an ontological architecture aimed at public and private cloud environments, the second stage deals with a semantic model at process level, and a pseudocode for ontological application is elaborated in the third stage, considering the technologies applied to the cloud. this work combines advanced features to support decision making from Data Lake storage solutions, semantic treatment of big data, as well as the presentation of strategies based on machine learning and data quality analysis to obtain data and metadata organized for application in a decision model. the configuration of the ontology presented meets the diversity of big data projects in the grain production context, the characteristics of which are based on interoperability in the use of heterogeneous data and its integration, elasticity of computational resources, and high availability of cloud access.

关键词： Cloud computing Databases Semantics Decision making Production Ontologies Big Data

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Catch You if You Deceive Me: Verifiable and Privacy-Aware Truth Discovery in Crowdsensing Systems 20

Catch You if You Deceive Me: Verifiable and Privacy-Aware Tr...

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15th ACM Asia Conference on computer and Communications Security, ASIA CCS 2020

作者： Xu, Guowen Li, Hongwei Xu, Shengmin Ren, Hao Zhang, Yinghui Sun, Jianfei Deng, Robert H. University of Electronic Science and Technology of China Chengdu China Singapore University of Technology and Design Singapore Singapore Singapore Management University Singapore Singapore

ISBN: (纸本)9781450367509

Truth Discovery (TD) is to infer truthful information by estimating the reliability of users in crowdsensing systems. To protect data privacy, many Privacy-Preserving Truth Discovery (PPTD) approaches have been proposed. However, all existing PPTD solutions do not consider a fundamental issue of trust. that is, if the data aggregator (e.g., the cloud server) is not trustworthy, how can an entity be convinced that the data aggregator has correctly performed the PPTD? A "lazy"cloud server may partially follow the deployed protocols to save its computing and communication resources, or worse, maliciously forge the results for some shady deals. In this paper, we propose V-PATD, the first Verifiable and Privacy-Aware Truth Discovery protocol in crowdsensing systems. In V-PATD, a publicly verifiable approach is designed enabling any entity to verify the correctness of aggregated results returned from the server. Since most of the computation burdens are carried by the cloud server, our verification approach is efficient and scalable. Moreover, users' data is perturbed with the principles of local differential privacy. Security analysis shows that the proposed perturbation mechanism guarantees a high aggregation accuracy even if large noises are added. Compared to existing solutions, extensive experiments conducted on real crowdsensing systems demonstrate the superior performance of V-PATD in terms of accuracy, computation and communication overheads. © 2020 ACM.

关键词： Cloud computing

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基于软硬件协同加速的关系网络推理优化方法

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高技术通讯 2022年第4期32卷 327-336页

作者：张志超王剑章隆兵肖俊华计算机体系结构国家重点实验室(中国科学院计算技术研究所) 北京100190 中国科学院计算技术研究所北京100190 中国科学院大学北京100049 中国电子科技集团公司第十五研究所北京100083

针对数据中心基于图形处理器(GPU)平台的关系网络推理计算中存在的低效能问题,本文提出了一种基于软硬件协同加速的关系网络优化方法。该方法采用基于GPU提取的支持集特征池与现场可编程门阵列(FPGA)推理异构协同的方式处理关系网络的... 详细信息

针对数据中心基于图形处理器(GPU)平台的关系网络推理计算中存在的低效能问题,本文提出了一种基于软硬件协同加速的关系网络优化方法。该方法采用基于GPU提取的支持集特征池与现场可编程门阵列(FPGA)推理异构协同的方式处理关系网络的推理计算,在高效能计算的同时保持关系网络的推理计算与GPU平台一致的准确率。利用基于高级综合(HLS)优化浮点卷积神经网络的计算方式,提高关系网络的处理能效。利用多运算单元异构多核处理的方式,满足FPGA时序收敛的同时,提升FPGA片上吞吐能力。本文在FPGA平台上实现了关系网络推理运算单元,在Omniglot数据集上构建的加速器功耗为15.867W,相对于GPU加速比为1.4~17.2;在miniImageNet数据集上构建的加速器功耗为12.359W,相对于GPU加速比为1.5~3.4。本文方法与同类FPGA加速浮点卷积神经网络相比,达到了最优的计算效能。实验数据表明,该方法有效利用了软硬件协同计算以及FPGA可重构计算的优势,降低了软硬件协同开发的耦合度,在保持关系网络推理计算准确率的同时,提升了关系网络推理的计算效能。

关键词：关系网络软硬件协同加速卷积神经网络异构多核

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Evaluating Single Image Dehazing Methods Under Realistic Sunlight Haze 15th

Evaluating Single Image Dehazing Methods Under Realistic Sun...

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15th International symposium on Visual computing, ISVC 2020

作者： Anvari, Zahra Athitsos, Vassilis VLM Research Lab Computer Science and Engineering Department University of Texas at Arlington ArlingtonTX United States

ISBN: (纸本)9783030645557

Haze can degrade the visibility and the image quality drastically, thus degrading the performance of computer vision tasks such as object detection. Single image dehazing is a challenging and ill-posed problem, despite being widely studied. Most existing methods assume that haze has a uniform/homogeneous distribution and haze can have a single color, i.e. grayish white color similar to smoke, while in reality haze can be distributed non-uniformly with different patterns and colors. In this paper, we focus on haze created by sunlight as it is one of the most prevalent types of haze in the wild. Sunlight can generate non-uniformly distributed haze with drastic density changes due to sun rays and also a spectrum of haze color due to sunlight color changes during the day. this presents a new challenge to image dehazing methods. For these methods to be practical, this problem needs to be addressed. To quantify the challenges and assess the performance of these methods, we present a sunlight haze benchmark dataset, Sun-Haze, containing 107 hazy images with different types of haze created by sunlight having a variety of intensity and color. We evaluate a representative set of state-of-the-art image dehazing methods on this benchmark dataset in terms of standard metrics such as PSNR, SSIM, CIEDE2000, PI and NIQE. Our results provide information on limitations of current methods, and on the practicality of their underlying assumptions. © 2020, Springer Nature Switzerland AG.

关键词： Image reconstruction

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Poster: Revisiting linpack algorithm on large-scale CPU-GPU heterogeneous systems 25

Poster: Revisiting linpack algorithm on large-scale CPU-GPU ...

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25th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming, PPoPP 2020

作者： Shui, Chaoyang Yu, Xianzhi Yan, Yujin Wang, Yinshan Meng, Ke Tan, Guangming State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

ISBN: (纸本)9781450368186

As the widening gap between GPU computing capability and other components (CPU, PCIe bus and communication network), it’s increasingly challenging to design high performance parallel algorithms for large CPU-GPU heterogeneous systems. there are mainly two reasons. Firstly, simply offloading the kernel library to GPU incurs large volume data transfer through low-speed PCIe bus. Secondly, communication overheads through network severely affects scalability. To solve the above issues, we advocate a paradigm shift to GPU-centric and fine-grained pipelining algorithm design. By taking Linpack benchmark as a case study, the new algorithm design paradigm shows its effectiveness. Our optimized Linpack program achieves 63.79PFlops on 16384 GPUs. Its floating-point efficiency outperforms the NVIDIA proprietary counterparts by 5% on average. © 2020 Copyright held by the owner/author(s).

关键词： Graphics processing unit

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Ordered Access Memory Based Programmable Hardware Accelerator Parallel architecture 15

Ordered Access Memory Based Programmable Hardware Accelerato...

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15th IEEE International Conference on the Experience of Designing and Application of CAD Systems (CADSM)

作者： Melnyk, Anatoliy Melnyk, Viktor Lviv Polytech Natl Univ Dept Comp Engn S Bandery Str 12 Lvov Ukraine John Paul II Catholic Univ Lublin Fac Math IT & Landscape Architecture Konstantynow 1H Lublin Poland Lviv Polytech Natl Univ Dept Informat Technol Secur S Bandery Str 12 Lvov Ukraine

ISBN: (纸本)9781728100531

Programmable hardware accelerator architecture and information processing method based on a new model of computation with parallel ordered data and command access is proposed in a paper. the principles of the formation, compilation and execution of the computer program in the hardware accelerator according to the proposed model are given. the advantages of the proposed hardware accelerator architecture over traditional ones are highlighted.

关键词： programmable hardware accelerator parallel architecture ordered access memory model of computation high-performance computing

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