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

共 1177 条记录，以下是31-40 订阅

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An Efficient parallel CNN Inference Framework for Multi-zone Processor 24

An Efficient Parallel CNN Inference Framework for Multi-zone...

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24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on Data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Chen, Jie Liu, Zhong College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9798350319934

Deep convolutional neural networks have been widely used in various fields, and how to accelerate and optimize the performance of neural network models according to the architectural characteristics of accelerator hardware has become the research hot spot. We propose and design an efficient parallel convolutional neural network inference framework named PCIF for multi-zone processor. Its core is a multi-core parallel and vectorized C++ operator library. PCIF has designed optimized data layout and transmission strategies, layer fusion and multi-image fusion methods, which can automatically parse and efficiently map existing neural network models to multi-core vector accelerators to accelerate inference computation. Users can also build new neural network models through the C++ and Python APIs provided by PCIF to achieve high performance inference computing. the results of the experiment show that the AlexNet, VGG-16, GoogleNet, and ResNet-50 network models achieve efficient computing performance using PCIF. © 2022 IEEE.

关键词： Convolutional neural networks

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Efficient Implementation of the LOBPCG Algorithm on a CPU-GPU Cluster 20th

Efficient Implementation of the LOBPCG Algorithm on a CP...

引用

20th ifip WG 10.3 international conference on network and parallel computing, NPC 2024

作者： Liu, Yang Zhao, Yonghua Wang, Zexin Huang, Rongfeng Zhang, Dingye Zhang, Xinyin Computer Network Information Center Chinese Academy of Science Beijing China University of Chinese Academy of Sciences Beijing China School of Information Science and Technology Shijiazhuang Tiedao University Hebei Shijiazhuang China

ISBN: (纸本)9789819628292

the Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) algorithm is an effective algorithm for solving large-scale sparse eigenvalue problems in various scientific and engineering applications. Based on a distributed CPU-GPU heterogeneous environment, this paper presents an optimized approach for the efficient solution of sparse eigenvalue problems utilizing the LOBPCG algorithm. To conceal intra-node data transfer and reduce the influence of the cross-node MPI communication overhead, we employ a sparse matrix regular two-dimensional partitioning scheme. To enhance GPU data throughput and data reuse, we consolidate multiple sparse matrix-vector multiplications (SPMV) into a single sparse matrix-matrix multiplication (SPMM). Furthermore, for the irregular dense matrix-matrix multiplication (GEMM) involved in the LOBPCG algorithm, we design an adaptive batched GEMM technique for GPU execution. Extensive experimental results demonstrate the computational efficiency and scalability of our approach. Our implementation achieves a significant speedup in solving typical matrices compared to the SLEPc library. the strong scalability performance exceeds 58%. © ifip international Federation for Information Processing 2025.

关键词： Scalability

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FedDeSnowNet: Federated De-snowing network for LiDAR Point Clouds 20th

FedDeSnowNet: Federated De-snowing Network for LiDAR Point ...

引用

20th ifip WG 10.3 international conference on network and parallel computing, NPC 2024

作者： Fang, Zhiyu Li, Boyang Liao, Jiahui Ren, Siheng Huang, Kai School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China

ISBN: (纸本)9789819628636

Although Light Detection and Ranging (Lidar) is widely utilized in the intelligent transportation systems, the susceptibility to data degradation in snowy condition limits the application. Since rule-based methods cannot meet the requirements of real-time inference while central learning-based methods has the limitations of training data collection and privacy, this paper proposes the FedDeSnowNet, a federated de-snowing method for Lidar point clouds. In order to solve the problems arising from the feature shift as well as the client shift caused by the data heterogeneity and the communication constraints in parameter aggregation process, the FedDeSnowNet incorporates a distribution-guided aggregation mechanism as well as an ingeniously designed personalized module, both designed to enhance the training effects. FedDeSnowNet outperforms baseline methods by 14% in F1 score in experiments. © ifip international Federation for Information Processing 2025.

关键词： Federated learning

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FPGA-Based Implementation of the Quantized Neural network for Image Super-Resolution 8

FPGA-Based Implementation of the Quantized Neural Network fo...

引用

8th international conference on Image, Vision and computing, ICIVC 2023

作者： Ruan, Zhihao Xiao, Shanzhu Deng, Qiuqun Tao, Huamin Wu, Jiadong Zhang, Huan College of Electronic Science and Technology National University of Defense Technology Chang Sha China

ISBN: (纸本)9798350335231

the convolutional neural network(CNN)-based method for image super-resolution(SR) reconstruction has been becoming an important part in the fields of image processing such as security monitoring, object tracking. However, the huge computational complexity of neural networks make it shackled to satisfy the requirements for power consumption and complexity of reconstruction systems in specific application circumstances. In this paper, we propose a structure-unsophisticated and parameter-slight CNN model, and design a quantization scheme that allows inference to be performed by using integer-only arithmetic to alleviate parameter storage and communication pressure in hardware. Furthermore, convolution loop unroll and parallel acceleration analysis are performed for the model under the premise of board resources to optimize hardware inference. the results show that when the quantization precision is 8 bits, memory requirement of parameters is reduced by 75%, and the inference peak signal-to-noise ratio(PSNR) value loss is less than 1%. Quantization model was performed on the Xilinx Zynq7035 platform with inference losses not exceeding 2%, while hardware resources consumption is greatly reduced. © 2023 IEEE.

关键词： Acceleration

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FP-ATM: A Flexible Floating Point NOR Adder Tree Macro for In-Memory computing 37

FP-ATM: A Flexible Floating Point NOR Adder Tree Macro for I...

引用

37th international conference on VLSI Design (VLSID) / 23rd international conference on Embedded Systems (ES)

作者： Attuluri, Yathin Kumar Chudasama, Ruchit Prasad, Kailash Mekie, Joycee IIIT Bangalore Bangalore Karnataka India IIT Gandhinagar Gandhinagar India

ISBN: (纸本)9798350384406

In-Memory computing (IMC) has emerged as a promising approach to address the von Neumann bottleneck in deep learning applications. this work proposes FP-ATM, a 6T SRAM-based all-digital design for multiply-accumulate (MAC) operations, featuring a flexible NOR Adder Tree for In-Memory computing. the proposed macro is data-aware and can support input activations and weights for INT8 and BF16 number formats in a convolutional neural network. Using multiple macros in different configurations can support neural networks with different topologies. the proposed macro is based on bit-serial multiplication and parallel adder trees. this architecture can achieve massively parallel MAC operations with high energy efficiency and throughput. the proposed macro achieves a peak energy efficiency of 267.7 TFLOPS/W at 0.65V, 8.5 times the state-of-the-art work. the maximum frequency is 1.67 GHz and achieves throughput of 2.67 GFLOPS/Kb at a voltage of 0.9V.

关键词： Digital In-Memory computing Floating Point NOR Adder Tree Macro

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A parallel and Distributed Data Management Approach for MEC Using the Improved Parameterized Deep Q-network 8th

A Parallel and Distributed Data Management Approach for MEC ...

引用

8th international Joint conference on Web and Big Data and Web-Age Information Management (APWeb-WAIM)

作者： Ren, Bingqing Yang, Peng Yi, Meng Yang, Dongmei Southeast Univ Sch Comp Sci & Engn Nanjing Peoples R China Southeast Univ Minist Educ Key Lab Comp Network & Informat Integrat Nanjing Peoples R China Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing Peoples R China

ISBN: (纸本)9789819772438;9789819772445

With the rapid development of Multi-access Edge computing (MEC), massive distributed collaborative data are generated in realtime at the edge of the network, which brings great challenges to the parallel and distributed data management for compute-intensive and low-latency application requirements. Traditional offloading strategies often neglect signal interference and the critical interdependence between edge servers. Our work combines the parallel and distributed data management system with the operational requirements of MEC. We employ a Partially Observable Markov Game (POMG) framework alongside an Improved Parameterized Deep Q-network (I-PDQN) algorithm, specifically designed for complex decision-making scenarios with task offloading and resource allocation in a discrete-continuous hybrid action space. the collaboration between the POMG framework and the I-PDQN algorithm facilitates data management and analysis across multiple edge servers and it addresses the dual challenges of parallel and distributed data management by ensuring data consistency and availability across distributed nodes, while optimizing computational resources to reduce latency and energy consumption. Experimental results illustrate that compared with the start-of-the-art baselines, our approach achieves competitive improvement in energy consumption and time delay.

关键词： Multi-access Edge computing parallel and distributed data management task offloading and resource allocation deep reinforcement learning

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Intelligent Telemetry: P4-Driven network Telemetry and Service Flow Intelligent Aviation Platform 20th

Intelligent Telemetry: P4-Driven Network Telemetry and Serv...

引用

20th ifip WG 10.3 international conference on network and parallel computing, NPC 2024

作者： Gao, Jing Zhou, Fanqin Dong, Mianxiong Feng, Lei Ota, Kaoru Li, Zijian Fan, Jiawei State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Department of Information and Electronic Engineering Muroran Institute of Technology Muroran050-8585 Japan Beijing University of Posts and Telecommunications Address Beijing Beijing100876 China

ISBN: (纸本)9789819628292

To address the current challenges in network monitoring and management, such as poor real-time performance, high cost, low accuracy, and slow fault detection and recovery, a P4-driven network telemetry and resource scheduling platform is designed and presented in this paper. this platform embeds telemetry information within data packets, achieving real-time monitoring and anomaly detection of network states. It significantly enhances the granularity and timeliness of network monitoring and employs a path rerouting mechanism for rapid network recovery. the platform boasts high scalability and customizability, allowing on-demand adjustment of telemetry strategies and parameters. It provides low-overhead, business-level telemetry information, offering comprehensive support for diverse services and advancing network management and service quality. this work provides a technical foundation for constructing the next generation of intelligent, efficient, and reliable networks through continuous research and practical application. © ifip international Federation for Information Processing 2025.

关键词： Telemetering

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Stereo network for Blind Image Super-Resolution 24

Stereo Network for Blind Image Super-Resolution

引用

8th international conference on Machine Learning and Soft computing, ICMLSC 2024

作者： Ji, Guangyi Hu, Xiao School of Mechanical and Electrical Engineering Guangzhou University China

ISBN: (纸本)9798400716546

Single image super-resolution method using neural networks has achieved remarkable strides. However, most existing works rely on the architecture of Convolutional Neural networks (CNNs) with shared kernel and the increasing of vertical depth, as result, super-image would loss high-frequency information. In every layer of human retina exists huge of neurons and some neurons in the same layer connect each other by horizontal neurons. Inspired by the neural network of human retina, a Stereo Neural network (SterNet) is designed for blind image super-resolution. As the basic block of SterNet, Dynamic Filter Block (DFB) performs through unshared kernel, hence SterNet is able to easily obtain more spatial features and high-frequency semantic information. To expand the network width, one DFB with unshared kernels and one RRDB with shared kernels connect in parallel to construct Dynamic Filter and Rense Residual Blocks (DFDRB) and two DFDRBs are in parallel to form a Stereo feature extraction Block (SterB). At last, several SterBs are in series into a SterNet. Extensive experiments on several benchmarks show the effectiveness of the proposed method. this indicates that simulating the structure and operation of real neural networks is beneficial for improving vision application system. © 2024 ACM.

关键词： Neurons

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SAGIN-CAIN: A 3D Routing Protocol for Post-Disaster SAGIN 6G network 20

SAGIN-CAIN: A 3D Routing Protocol for Post-Disaster SAGIN 6G...

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20th Annual international conference on Distributed computing in Smart Sensor Systems and the Internet of things (DCOSS-IoT)

作者： Marinho, Ricardo Pagoto Vieira, Luiz F. M. Vieira, Marcos A. M. Loureiro, Antonio A. F. Univ Fed Minas Gerais Dept Comp Sci Belo Horizonte MG Brazil

ISBN: (纸本)9798350369458;9798350369441

the 6G wireless networks are already being studied and their main novelties are beginning to emerge. the 6G network promises 100% network coverage in every area, including wilderness areas such as the ocean, desert, or remote areas, as it will be a fully 3D network, i.e., it will integrate space, air, and terrestrial networks. Also, with all these types of devices, the complexity of 6G networks increases, and Artificial Intelligence (AI) techniques will be mandatory for all the 6G's potential data and parameters. Hence, a Space-Air-Ground Integrated network (SAGIN) is a potential alternative to enable all the network coverage as drones and satellites can cover large areas and fill possible network coverage gaps. the 6G network coverage also includes post-disaster regions with no network infrastructure and no network coverage, making SAGIN very suitable for them. thus, in this work, we expand CAIN, a post-disaster 6G routing protocol, so it can also be used in SAGIN scenarios, increasing even more network coverage. With drones as relays, SAGIN-CAIN routes the CAIN's Cluster Head's messages until it reaches a satellite.

关键词： 6G SAGIN RL network coverage area

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Enhancing Trace Representation Using Public Transportation Data 20

Enhancing Trace Representation Using Public Transportation D...

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20th Annual international conference on Distributed computing in Smart Sensor Systems and the Internet of things (DCOSS-IoT)

作者： Gaspar, Stefany Da Cunha, Felipe Domingos Loureiro, Antonio A. F. Pontifical Catholic Univ Minas Gerais PUC MG Dept Comp Sci Belo Horizonte MG Brazil Fed Univ Minas Gerais UFMG Dept Comp Sci Belo Horizonte MG Brazil

ISBN: (纸本)9798350369458;9798350369441

Simulation is the most commonly used method for evaluating protocols and algorithms within vehicular networks (VANETs). Typically, simulation tools employ mobility traces to reconstruct the network topology, which relies on existing interactions among mobile nodes. However, the quality of these traces, particularly their spatial and temporal resolution, plays a critical role in accurately shaping the network topology. Consequently, the validity of simulation outcomes heavily depends on the mobility model's ability to mirror the real network topology accurately. We demonstrate that actual bus mobility traces exhibit gaps, which lead to outcomes that fail to represent reality accurately. In this study, we introduce a method to address these gaps, resulting in enhanced characteristics that contribute to more trustworthy simulation outcomes. Moreover, we present evaluation results comparing the communication metrics of both the original and adjusted traces. these findings indicate that the gaps create network topologies that deviate from the actual scenario, diminishing the credibility of the evaluation results. Although mobility data for vehicular networks is beneficial, it necessitates a process of quality enhancement.

关键词： Vehicular networks (VANETs) Mobility Traces network Topology Spatial and Temporal Resolution Mobility Model

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