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检索条件"任意字段=8th IFIP International Conference on Network and Parallel Computing"

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Traffic Sign Recognition by Image Preprocessing and Deep Learning 15th

Traffic Sign Recognition by Image Preprocessing and Deep Lea...

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15th international conference on Intelligent Human-Computer Interaction (IHCI)

作者： Khamdamov, U. R. Umarov, M. A. Khalilov, S. P. Kayumov, A. A. Abidova, F. Sh. Univ Management & Future Technol Tashkent Uzbekistan Tashkent Univ Informat Technol Samarkand Branch Tashkent Uzbekistan Tashkent Univ Informat Technol Tashkent Uzbekistan

ISBN: (纸本)9783031538292;9783031538308

Due to the improvement in the car manifacture, the rate of road traffic accidents is increasing. To solve these problems, there is loads of attention in research on the development of driver assistance systems, where the main innovation is traffic sign recognition (TSR). In this article, a special convolutional neural network model with high accuracy compared to traditional models is used for TSR. the Uzbek Traffic Sign Dataset (UTSD) applied in the zone of Uzbekistan was created, consisting of 21.923 images belonging to 56 classes. We proposed a parallel computing method for real-time processing of video haze removal. Our utilization can process the 1920 x 1080 video series with 176 frames per second for the dark channel prior (DCP) algorithm. 8.94 times reduction of calculation time compared to the Central Processing Unit (CPU) was achieved by performing the TSR process on the Graphics Processing Unit (GPU). the algorithms used to detect traffic signs are improved YOLOv5. the results showed a 3.9% increase in accuracy.

关键词： Traffic sign recognition deep learning image dehazing UTSD dataset parallel processing data augmentation

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An Intrusion Detection Method for Industrial Internet Fusing Multi-Scale TCN and Transformer network 20th

An Intrusion Detection Method for Industrial Internet Fusing...

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20th international conference on Intelligent computing (ICIC)

作者： Liu, Zhihua Liu, Shenquan Zhang, Jian Xinjiang Univ Sch Comp Sci & Technol Urumqi 830046 Peoples R China

ISBN: (纸本)9789819756056;9789819756063

Capturing more details and features of traffic data by combining information at different scales is one of the practical approaches to address cybersecurity challenges in industrial environments. TCNs can improve the model's ability to extract multi-scale information by adjusting the size of the convolutional kernel or the number of convolutional layers. However, the approach still falls short in capturing anomalous patterns hidden in global dependencies, which leads to the model's accuracy on certain long-duration attack categories with reduced accuracy. this paper proposes a novel industrial intrusion detection based on Multiscale TCN and Transformer fusion (MTTN). A parallel mechanism is included in *** the first branch, the TCN is improved in terms of scale and structure to establish the connection between forward and backward traffic sequences and enhance the extraction of multi-scale temporal features of the model. In the second branch, patches with different sizes and dimensions are fed into the Transformer to capture the global multi-scale information of the network traffic. the features from these two branches are fused to improve the model detection performance. A series of experiments on the proposed method on two public datasets (CICIDS2017 and NSL-KDD) demonstrate the effectiveness of the proposed method.

关键词： Intrusion Detection Multi-scale Features Transformer Temporal Convolutional networks

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A Column-parallel Time-Interleaved SAR/SS ADC for computing in Memory with 2-8bit Reconfigurable Resolution 5

A Column-Parallel Time-Interleaved SAR/SS ADC for Computing ...

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5th IEEE international conference on Artificial Intelligence Circuits and Systems, AICAS 2023

作者： Li, Yuandong Du, Li Du, Yuan Nanjing University Nanjing China

ISBN: (纸本)9798350332674

computing in Memory (CiM), as a computing system with non-von Neumann architecture, has been reported as one of the most promising neural network accelerators in the future. Compared with digital-based computation, CiM uses RAM arrays to calculate and store in the analog domain, avoiding the high delay and energy consumption caused by data transfer. However, the computational results require data converters for quantization, which often limits the development of high-performance CiMs. In this work, we propose a 2-8bit reconfigurable time-interleaved hybrid ADC architecture for high-speed CiMs, including successive approximation and single-slope stages. Reconfigurability introduces a trade-off between resolution and conversion speed for ADCs in different computing scenarios. A prototype was implemented in a 55 nm CMOS technology, which occupies an area of 330μm × 13μm and consumes a power of 1.429mW at 8-bit conversion mode. With a Nyquist frequency input sampled at 350 MS/s, the SNDR and SFDR are 40.93 dB and 51.08 dB, respectively. the resultant Walden figure of merit is 44.8 fJ/conv. © 2023 IEEE.

关键词： Energy utilization

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8th international parallel Data Systems Workshop (PDSW 2023)

ACM International Conference Proceeding Series

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ACM international conference Proceeding Series 2023年 1194-1194页

作者： Zhou, Amelie Chi Xie, Bing Byna, Suren Hong Kong Baptist University Hong Kong Microsoft United States The Ohio State University United States

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A Hybrid parallelism Framework of SPH for the Applications in Automobile Gearbox 30th

A Hybrid Parallelism Framework of SPH for the Applications i...

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30th international conference on Computational and Experimental Engineering and Sciences

作者： Zhang, Xiang Sun, Peng-Nan Xu, Yang Sun Yat Sen Univ Sch Ocean Engn & Technol Zhuhai 519082 Peoples R China

ISBN: (纸本)9783031774911;9783031774898;9783031774881

the rotating machineries of automobile, such as gears and motors, are involved with complicated interactions between fluids and structures, resulting in flow phenomenon such as free-surface, moving boundary, thermal conduction etc. Smoothed Particle Hydrodynamics (SPH), due to its Lagrangian nature, is preferred to simulate such phenomenon. the complexities of automobile structures require small particle distancing and therefore large number of particles are needed to discretize both fluid and structure. the small time step of SPH simulation is also required due to intense flowsplashing resulting from high-speedmoving boundary. Both two points above lead to large amount of computation duringSPHsimulation. In this paper, a parallelism framework of weakly compressible SPH(WCSPH) is proposed to accelerate SPH simulation by high-performance computing cluster. A hybrid parallelism strategy, with both Message Passing Interface (MPI) and Intel threading Building Blocks (TBB), is used to reduce the total number of processes and therefore reduce the latency due to communication among computing clusters network. METIS is used to decompose the computational domain enabling dynamic domain decomposition and load balancing. the oil motion inside a gearbox is successfully simulated using the proposed framework, showing that the proposed parallelism framework is applicable to complex industry application and can accelerate SPH simulation efficiently.

关键词： SPH parallel computing fluid-structure interaction gearbox

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Multi-scale parallel Fusion Convolution network for Crop Disease Identification

Multi-scale Parallel Fusion Convolution Network for Crop Dis...

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IEEE international conference on Cloud computing and Big Data Analysis (ICCCBDA)

作者： Huyue Ren Wanhui Chen Peishun Liu Ruichun Tang Jinyu Wang Chang Ge School of Computer Science and Technology Ocean University of China Qingdao China

In order to identify crop disease accurately and efficiently, we propose a multi-scale parallel fusion convolution network called MSPFNet for the automatic crop disease identification. It combines multi-scale convolution and attention mechanism. By mixing convolution kernels of different sizes in the same convolution unit, multi-scale convolution can identify crop disease features at different scales better. In this paper, Convolutional Block Attention Module is introduced to improve the representation ability of the model. By analyzing the information of spatial attention and channel attention, model can pay more attention to important features. In addition, we design a feature fusion module to realize the fusion of features at different depths. And feature fusion module can solve the problem of the disappearance of disease features in the process of image feature extraction. We validate the proposed model on the publicly available apple leaf pest data set. Experimental results show that the proposed MSPFNet can achieve higher recognition accuracy.

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High Performance computing for Autonomous Planetary Exploration 8

High Performance Computing for Autonomous Planetary Explorat...

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8th IEEE international conference on Space Mission Challenges for Information Technology (SMC-IT)

作者： Sharif, Khaled Ford, Jordan Whittaker, Red Wong, Uland NASA Ames Res Ctr Intelligent Robot Grp KBR Mountain View CA 94035 USA Carnegie Mellon Univ Elect & Comp Engn Pittsburgh PA 15213 USA Carnegie Mellon Univ Robot Inst Pittsburgh PA USA

ISBN: (纸本)9781728185606

Modern parallel computers could power the perception and compression algorithms small planetary rovers require to navigate long distances, construct detailed terrain maps, and communicate discoveries to Earth. this work identifies and comprehensively characterizes four algorithms important to planetary roving that are well-suited for parallel computing. Multiple implementations of dense stereo matching, multi-view stereo, image compression, and triangle mesh compression are evaluated using the NVIDIA Jetson family of high-performance embedded computers. Image and mesh inputs are derived from simulation and used to evaluate the performance, power consumption, and hardware utilization of each device as a function of time. Our results demonstrate the promising capacity for modern embedded computers to expand the range and pace of planetary rover exploration.

关键词： autonomy benchmark compression parallel computing perception rover

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BDFC:A New Flow Control Mechanism for Torus networks 24th

BDFC:A New Flow Control Mechanism for Torus Networks

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24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

作者： Zhang, Haofei Li, Youmeng Deng, Yu Fang, Qi College of Intelligence and Computing Tianjin University Tianjin300072 China Phytium Technology Co. Ltd. Changsha410073 China

ISBN: (纸本)9789819615506

FBFC (Flit Bubble Flow Control) and Dateline flow control are two commonly used flow control mechanisms in Torus networks. However, each of these two flow control mechanisms has its own performance limitations. the bubble flow control mechanism can lead to starvation, and dateline flow control can decrease channel cache utilization. We propose BDFC (Bubble Dateline Flow Control) technology, which combines the above two flow control mechanisms. It greatly alleviates the hunger problem of bubble flow control technology without increasing costs, while also increasing the cache utilization of Dateline technology, thereby improving the performance of the entire network. In 8×8Torus, its saturated throughput has increased by a maximum of 44% compared to FBFC. Improved by a maximum of 68% compared to Dateline. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fiber optic networks

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A Non-deterministic Training Approach for Memory-Efficient Stochastic Neural networks 36

A Non-deterministic Training Approach for Memory-Efficient S...

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36th IEEE international System-on-Chip conference (SOCC)

作者： Golbabaei, Babak Zhu, Guangxian Kan, Yirong Zhang, Renyuan Nakashima, Yasuhiko Nara Inst Sci & Technol Div Informat Sci Ikoma Japan

ISBN: (纸本)9798350300116

We proposed a non-deterministic training approach for memory-efficient stochastic computing neural networks (SCNN). Conventional SCNN simply convert the trained network parameters into stochastic bit-streams at the inference phase. Although stochastic bit-streams can simplify binary multiplication and addition using the principle of probability, memory cost and computation delay are consumed due to extremely long bit-streams. Different from methods that rely on long bit-streams to convert full-precision NN to SCNN during inference, the proposed approach also introduces the concept of non-deterministic computation during training to alleviate the memory requirement growth caused by long bit-streams. To this end, we probabilize NN parameters in the feed-forward process of the training phase, and convert them into 1/4/8-bit stochastic number representations according to the probability, which greatly reduces the memory requirements in SC. In order to alleviate the training instability problem caused by low-bit encoding, we propose a multiple parallel training strategy (MPTS) during the training process to improve the stability of the results. the proposed MPTS achieves a stable training process through a voting mechanism. We evaluate the performance of the proposed training approach on fully connected NN and the MNIST dataset. Compared with the baseline training method with 97.77% accuracy using 32-bit floating point values, the proposed non-deterministic training approach achieves a reasonable accuracy of 90.34% while using 4-bit stochastic number representations to represent layer weights and biases.

关键词： Non-determinism Neural network Stochastic Encoding Image Classification Machine Learning Voting Mechanism

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A Novel Task-Allocation Framework Based on Decision-Tree Classification Algorithm in MEC 18th

A Novel Task-Allocation Framework Based on Decision-Tree Cl...

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18th ifip WG 10.3 international conference on network and parallel computing, NPC 2021

作者： Liu, Wenwen Yu, Zhaoyang Yan, Meng Wang, Gang Liu, Xiaoguang TJ Key Lab of NDST College of Computer Science Nankai University Tianjin China

ISBN: (纸本)9783030935702

Mobile-edge computing (MEC) has emerged as a promising paradigm to extend the cloud computing tasks to the edge mobile devices for improving the quality of service. this paradigm addresses the problems in cloud computing architecture by enabling lower latency, higher bandwidth, and better privacy and security. Previous studies of task allocation in MEC systems generally only consider the single influence factor, such as the distance between the mobile device and cloudlets, to select the suitable cloudlets for tasks. However, there are various types of tasks with complex individual requirements about which we should consider, such as transmission bandwidth, computing capacity and storage capacity of cloudlets, and so on. In this paper, we propose an on-demand and service oriented task-allocation framework based on machine learning technology, called Edgant. It classifies tasks into three types using a decision-tree model according to the tasks’ characteristics including requirement on server resources and user’s requirements. For each type, we provide a selection strategy to allocate task to the most suitable cloudlet based on characteristics of the task and the current state of cloudlets. Simulated evaluations demonstrate that Edgant achieves lower latency and better accuracy compared to other task allocation methods, as well as high reliability under high mobility of the mobile devices. © 2022, ifip international Federation for Information Processing.

关键词： Mobile edge computing

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