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A Bit Level Acceleration of Mixed Precision Neural Network 29

A Bit Level Acceleration of Mixed Precision Neural Network

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29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Qiu, Dehui Wang, Jing Hou, Yi Zhang, Weigong Gao, Lan Capital Normal University Information Engineering College Beijing China Renmin University of China School of Information Beijing China

ISBN: (纸本)9798350330717

With the growth of the convolutional neural network (CNN) parameters, the hardware resources become limited when deploying CNN models. Single bit-width quantization may lead to degradation of accuracy, while mixed-precision quantization models maintain higher accuracy. However, current mixed-precision quantization accelerators only consider the algorithm level quantization and fixed-bit-width processing elements (PEs) without fully utilizing the resources. Therefore, we propose a neural network acceleration architecture based on bit-level computational units to improve resource utilization and throughput of mixed-precision quantization accelerators. The 2-bit and 3-bit low-bit computational units are designed to implement the high-bit quantization. We also propose spatio-temporal fusion to satisfy the unique bit widths in each layer with mixed precision quantization. In particular, we use the 2-bit and 3-bit computational units to achieve dynamic layer-level quantization. We also discuss various combinations of different bit widths, which can be dynamically implemented according to the requirements of accuracy, execution time, etc. The proposed acceleration architecture is implemented in Verilog, verified using three network models: ResNet18, VGG7 and LeNet-5, and tested against three accelerators: Eyeriss, Stripes and Bit Fusion. The experimental results show that our accelerators provide more accuracy and the grouping operation reduces the area overhead. It provides a 3.08 to 3.54 times acceleration ratio and 2.4 to 2.57 times reduction in energy consumption on the three networks. © 2023 IEEE.

关键词： bit level computing unit convolutional neural network FPGA hardware accelerator

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BEW-YOLO: An Improved Method for PCB Defect Detection Based on YOLOv7 29

BEW-YOLO: An Improved Method for PCB Defect Detection Based ...

引用

29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Li, Zhiyao Li, Aimin Zhang, Yuechen Kong, Xiaotong Li, Wenqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350330717

The PCB defect object size is small and the detection process is susceptible to background interference, usually have the problem of missed and false detection. In order to solve the above problems, an improved method based on YOLOv7 is proposed in this paper. Firstly, the bi-level routing attention (BRA) has been added to the header of the original YOLOv7 model to capture global dependencies and ensure the accuracy of small object detection and localization. Secondly, the explicit visual center (EVC) block is introduced before the fusion of mid-level features and high-level features to capture the global remote dependencies of top-level features, extract the features in local corner regions, achieve a comprehensive feature representation. Finally, the loss function is improved by changing the loss function of the original model to Wise-IoU, which allows our model to focus more on ordinary-quality anchor boxes and improve the performance of the detector while decreasing the competitiveness of anchor boxes for high-quality samples and reducing the influence of low-quality samples on the detection results. The experimental results show that the improved model improves 5% in AP and 1.9% in both AR and mAP over the original YOLOv7 model. Meanwhile, comparison experiments are conducted on the data-enhanced PCB dataset, which proves the superiority of our model over other state-of-the-art models. © 2023 IEEE.

关键词： attention mechanism defect detection PCB YOLOv7

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R-RPT-A Reliable Routing Protocol for Industrial Wireless Sensor networks 24th

R-RPT-A Reliable Routing Protocol for Industrial Wireless Se...

引用

24th international conference on parallel and distributed computing, Applications and Technologies, PDCAT 2023

作者： Roy, Kripanita Kim, Myung-Kyun Department of Electrical Electronic and Computer Engineering University of Ulsan Daehak-Ro 93 Nam-Gu Ulsan44610 Korea Republic of

ISBN: (纸本)9789819982103

Wireless Sensor networks (WSN) are extensively used to monitor and control physical environments. Effective energy management and maintaining reliability are key considerations in Wireless Sensor networks and routing plays a crucial role in achieving these objectives. The Routing Protocol for Low Power and Lossy networks (RPL) is being adopted in Low-power and Lossy networks (LLNs) to facilitate the connectivity of Wireless Sensor networks within the Internet of Things (IoT). Although RPL has been significantly used in IoT routing, it still has extensive challenges. One of the most basic challenges is related to the reliability of routing. However, RPL lacks a load balancing mechanism, which is essential for maximizing the lifetime of sensor nodes by preventing the occurrence of overloaded nodes and the potential congestion that can result from it. To improve this issue, a new routing protocol was proposed called the reliable routing protocol (R-RPT) to maximize the reliability of data collection in large scale wireless sensor network. R-RPT aims to establish multiple bidirectional routes between a sensor node and a root node. R-RPT selects the parent node based on the evaluation of various criteria related to reliability. In addition, R-RPT achieves load balancing efficiently by sending data packets via the route with lighter workload. The simulation results obtained through Cooja simulator demonstrated that the proposed R-RPT routing protocol outperforms existing routing protocols in terms of packet delivery ratio, routing packet overhead and end-to-end packet delay. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Routing protocols

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Lazy Read: Asynchronous Execution of Synchronous File I/O

Lazy Read: Asynchronous Execution of Synchronous File I/O

引用

2023 IEEE international conference on Big Data, BigData 2023

作者： Loser, Ansgar Raskob, Florian Scheuermann, Bjorn Communication Networks Tu Darmstadt Darmstadt Germany

ISBN: (纸本)9798350324457

In the realm of High-Performance computing (HPC), the disparity between I/O and processing capabilities can hinder application performance. One source of this I/O gap is the latency of the devices, especially in case of distributed filesystems (DFS). Prior solutions to minimize such negative effects (e.g. using asynchronous operations or separate I/O-Threads), have to be considered when designing an application. This work aims to execute these operations asynchronously for existing applications without necessitating modifications or even recompilation. To attain this objective, we introduce the Lazy Read approach, which alters how the operating system handles read requests. This modification enables the application to start multiple reads or do CPU work in parallel instead of waiting for completion of the read operation. For evaluation, we implemented the Lazy Read approach in the Linux kernel. We then showed that the approach adds overhead, however, multiple read operations can be executed in parallel. In our specific evaluation the maximum possible speedup was achieved, reducing the execution time by up to 50%. © 2023 IEEE.

关键词： Asynchronous I/O Filesystem I/O-Gap Linux Kernel Operating Systems parallelism

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A Physics and Data-Driven Hybrid PINNs Intelligent computing Method for Nuclear Engineering Simulation 4

A Physics and Data-Driven Hybrid PINNs Intelligent Computing...

引用

4th international conference on Electronic Information Engineering and Computer Science, EIECS 2024

作者： Xie, Yufei Wang, Wenlin Wu, Guohua Yu, Yang An, Ping Sun, Zibin Zhang, Haichuan Luo, Shengfeng Li, Yue School of Automation Wuhan University of Technology Wuhan China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China Nuclear Power Institute of China Chengdu China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha China

ISBN: (纸本)9798331531409

In the field of nuclear energy, the Loss of Coolant Accident (LOCA) is recognized as one of the most severe types of nuclear reactor accidents, characterized by its complex physical processes and potentially catastrophic consequences. These challenges impose stringent requirements on safety analysis and emergency response. Accurate prediction and analysis of fluid behavior within pipelines under LOCA conditions are critical for evaluating accident outcomes and formulating response strategies. This paper introduces an innovative intelligent computing approach - a Physics-Informed Neural networks (PINNs) model driven by both physical data and simulation data, specifically tailored for LOCA conditions. To address the challenges posed by multivariable and complex physical relationships, the six-equation two-fluid model is first simplified to represent the physical processes. Subsequently, a dual-driven PINNs network is developed, integrating both simulation data and physical constraints. The proposed model demonstrates a root mean square error (RMSE) of 0.02, a mean absolute error (MAE) of 0.044, and an R2 of 0.81 in predicting the outcomes under the six-equation model. © 2024 IEEE.

关键词： Nuclear energy

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Response Time Estimate for a Fork-Join System with Pareto distributed Service Time as a Model of a Cloud computing System Using Neural networks 24th

Response Time Estimate for a Fork-Join System with Pareto Di...

引用

24th international conference on distributed Computer and Communication networks, DCCN 2021

作者： Gorbunova, A.V. Lebedev, A.V. V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences 65 Profsoyuznaya Street Moscow117997 Russia Lomonosov Moscow State University GSP-1 Leninskie Gory Moscow119991 Russia

ISBN: (纸本)9783030971090

A cloud computing system that receives complex user tasks involving several subtasks is studied from the point of view of the response time. In order to reduce the service time, the tasks are divided into smaller components and processed in parallel. As a cloud center model we use a fork-join queuing system with Pareto distribution of the service time on the servers. To analyze the mean response time and its standard deviation, a new approach is used combining simulation modeling with one of the machine learning methods. The estimates obtained are much more accurate than the earlier analytical results on fork-join systems. © 2022, Springer Nature Switzerland AG.

关键词： Neural networks

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Large Scale Machine Translation Language Model Training System Based on Spark

Large Scale Machine Translation Language Model Training Syst...

引用

Mobile networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Yan Hu Department of English Education Shandong Vocational College of Light Industry Zibo Shandong China

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

When faced with large language datasets, machine translation language models are difficult to process, resulting in low accuracy and efficiency of translation. Therefore, this study constructs a framework based on Firstly, by installing Apache Spark and configuring its integration with Hadoop distributed File System (HDFS), a distributed computing environment capable of parallel data processing is established to support rapid processing of large-scale data. Then, the distributed storage system HDFS is adopted to optimize data access efficiency and reduce IO (Input/Output) bottlenecks. Finally, a new iterative training strategy is implemented to gradually improve the translation accuracy of the model through incremental learning. Under the optimal configuration, the combination of a learning rate of 0.1 and batch processing of 2048 achieves a BLEU score of up to 30.8%, with training lasting only 4 hours. This study demonstrates the effectiveness of a large-scale machine translation language model training system based on Spark in improving translation efficiency and accuracy.

关键词： Training Translation Accuracy Computational modeling distributed databases Cluster computing parallel processing Data models Machine translation Sparks

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Securing Cyber Infrastructure of IoT-Based networks using AI and ML 7

Securing Cyber Infrastructure of IoT-Based Networks using AI...

引用

7th international conference on parallel, distributed and Grid computing, PDGC 2022

作者： Mehta, Kapil Sood, Vandana Mohindru Singh, Jashanpreet Sharma, Divyam Chhabra, Pratham Chandigarh Group of Colleges Department of Computer Science and Engineering Landran Punjab Mohali India Chitkara University Chitkara University Institute of Engineering and Technology Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9781665454018

The Internet of Things (IoT) has emerged as one of the most advanced technologies, a subject of interest or field of study to researchers, and a sector with promising economic prospects. Numerous gadgets must be connected to one another and people in order for them to work. To handle its data interchange and processing, IoT needs a cloud computing environment. To achieve the automation process in many applications, artificial intelligence (AI) is required to evaluate the data stored in cloud infrastructure and make quick, accurate judgments. IoT paired with Machine Learning (ML) and Deep Learning (DL) models are used to increase the functionality of complicated applications. Cloud vulnerability and IoT device networking remain significant dangers, although AI is presently playing a significant role in enhancing traditional cybersecurity. Huge volumes of data are generated by the rapidly evolving IoT devices and networks in many different ways, which calls for careful authentication and security. One of the most efficacious methods for confronting cybersecurity breaches and encrypting data is artificial intelligence (AI). In this paper, we have investigated the effectiveness of Artificial Intelligence (AI), deep learning (DL) and machine learning (ML) techniques for IoT security. Additionally, this investigation sheds light on the AI roadmap for identifying risks based on attack types. © 2022 IEEE.

关键词： Internet of things

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Quad based Sub-Cluster Head Selection for Energy Efficiency in Wireless Sensor networks 7

Quad based Sub-Cluster Head Selection for Energy Efficiency ...

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7th international conference on parallel, distributed and Grid computing, PDGC 2022

作者： Kumar, Bhawnesh Kumar, Ashwani Negi, Harendra Singh Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Shri Ram Group of Colleges Department of Computer Science and Engineering Muzaffarnagar India

ISBN: (纸本)9781665454018

Cluster heads are the primary consideration used in Wireless Sensor networks (WSN) to transfer data from one point to another point. A wireless sensor network's energy usage can be decreased by minimizing the nodes to communicate with final point. Organizing the network into different clusters, each of which elects a node as the head of cluster, is one method for allowing information exchange between sensors. Nodes are frequently clustered into mainly non-overlapping clusters to assist scalability. In this paper, a quad based sub-clustering innovative architecture for an effective clustering in wireless sensor heads is proposed. In a quad based sub-clustering strategy using K-means clustering, each sensor node delivers data to its sub-cluster head before sending data to the base station, as opposed to directly sending data to cluster heads. This strategy is used to reduce node power usage. The framework for a wireless sensor network's energy conservation is the subject of this research article. The framework is designed so that the sub-cluster head will be chosen after the nodes have been grouped together. The energy consumption of quad based sub-clustering is relatively less than single clustering. Through this approach, can increase the energy level of sensor networks. © 2022 IEEE.

关键词： Energy utilization

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A Joint distributed Computation Offloading and Resource Allocation Scheme for MEC networks 9

A Joint Distributed Computation Offloading and Resource Allo...

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9th international conference on Computer and Communications, ICCC 2023

作者： Sun, Enchang Li, Mengsi Dong, Xiaoxuan Zhang, Dongying Beijing University of Technology Faculty of Information Technology Beijing100124 China Beijing University of Technology Beijing Dublin International College Beijing100124 China Beijing University of Technology Information Technology Support Center Beijing100124 China

ISBN: (纸本)9798350317251

Multi-access edge computing (MEC) technology is a potential solution to the conflict between resource-constrained unmanned aerial vehicles (UAVs) and various emerging computationally intensive, delay-sensitive applications of UAVs. In this paper, we study the joint optimization problem of distributed computation offloading and resource allocation in MEC-supported UAV application scenarios, where the task of each UAV can be divided and offloaded to multiple servers deployed in ground wireless access points (WAPs) for parallel processing. To enhance the quality of user experience (QoE) while avoiding excessive optimization, we propose a new user satisfaction function to evaluate the task processing level of MEC network, which respectively scores the actual task processing delay and energy consumption based on different criteria determined by task requirements. We aim to trade off delay and energy consumption by jointly optimizing server selection, computing resource allocation, transmission power control, and task allocation to achieve maximum user satisfaction. The joint optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) problem. To reduce the complexity of the joint optimization problem, it is decomposed into three sub-problems based on the divide-and-conquer approach: server computing resource allocation, transmission power control, and task offloading decision. We propose a three-layer intelligent optimization (TIO) algorithm, which employs the Lagrange multiplier method, the AdaDelta gradient descent method, and our proposed multivariate decoupling-particle swarm optimization (MD-PSO) algorithm to solve the above three sub-problems alternately for gradually approaching the maximum user satisfaction. The numerical results show that our proposed algorithm is significantly superior to the benchmark algorithms in terms of solving speed, accuracy, and stability. © 2023 IEEE.

关键词： Computation offloading

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