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Traffic-oriented reconfigurable NoC with augmented inter-port buffer sharing

Frontiers of Information Technology & Electronic engineering 2024年第10期25卷 1322-1336页

作者： Chenglong SUN Yiming OUYANG Huaguo LIANG School of Computer and Information Engineering Fuyang Normal UniversityFuyang 236041China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230601China School of Electronic Science and Applied Physics Hefei University of TechnologyHefei 230601China

As the number of cores in a multicore system increases,the communication pressure on the interconnection network also *** network-on-chip(NoC)architecture is expected to take on the ever-expanding communication demands triggered by the ever-increasing number of *** communication behavior of the NoC architecture exhibits significant spatial–temporal variation,posing a considerable challenge for NoC *** this paper,we propose a traffic-oriented reconfigurable NoC with augmented inter-port buffer sharing to adapt to the varying traffic flows with a high ***,a modified input port is introduced to support buffer sharing between adjacent ***,the modified input port can be dynamically reconfigured to react to on-demand ***,it is ascertained that a centralized output-oriented buffer management works well with the reconfigurable input ***,this reconfiguration method can be implemented with a low overhead hardware design without imposing a great burden on the system *** experimental results show that compared to other proposals,the proposed NoC architecture can greatly reduce the packet latency and improve the saturation throughput,without incurring significant area and power overhead.

关键词： Network-on-chip Reconfigurable Traffic-oriented Buffer sharing

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Minimizing energy consumption in fixed node networks using a novel neutrosophic model: comparative analysis with standard and existing algorithms

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International Journal of Information Technology (Singapore) 2025年 1-15页

作者： Ghimire, Sangam Bhurtel, Nirjal Jha, Sudan Ahmad, Sultan Abdeljaber, Hikmat A. M. Nazeer, Jabeen Department of Computer Science and Engineering Kathmandu University 28 kilo Bagmati Dhulikhel 45210 Nepal Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-kharj 11942 Saudi Arabia School of Computer Science and Engineering Lovely Professional University Punjab Phagwara 144411 India Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Lebanon

The growing development and utilization of networked systems has led to more concern regarding the energy efficiency of these systems. In this paper, we present a novel approach to minimizing energy consumption in fixed node network for which we propose a new Neutrosophic model. This model leverages the unique strengths of Neutrosophic logic to enhance the efficiency by minimizing the energy consumption. The simulation of results is performed using Matplotlib (Python) with implementation parameters such as number of nodes (n) and additive white Gaussian noise (AWGN) (η). Additionally, we show a comparative analysis between the proposed model and other prevalent approaches used in minimizing energy consumption in networked systems. The comparative results shows improvement in energy efficiency, showcasing the potential of the proposed model as an alternative for optimizing energy consumption in networked systems. This work highlights the effectiveness of the proposed model and opens new avenues for future advancements in energy-efficient networked system using Neutrosophic approach. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Comparative analysis Energy-consumption optimization Networked systems Neutrosophic model Sensitivity analysis

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Chaotic Fuzzy Logic for Enhanced Educational Q&A System 6

Chaotic Fuzzy Logic for Enhanced Educational Q&A System

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6th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2024

作者： Chen, Haoyuan Shi, Nuobei Chen, Ling Lee, Raymond BNU-HKBU United International College Department of Computer Science Zhuhai China School of Applied Science and Civil Engineering Beijing Institute of Technology Zhuhai China

ISBN: (纸本)9798350374315

Online question-and-answer (Q&A) platforms are replete with extensive human resources support. This paper proposes a novel methodology of a customized Large Language Model (LLM) called Chaotic LLMs-based Educational Q&A System (CHAQS) to navigate the complexities associated with intelligent Q&A systems for educational sector. It uses an expansive dataset comprising over 383,000 educational data pairs, an intricated fine-tuning process encompassing p-tuning v2, Low Rank Adaptation (LRA), and strategies for parameter freezing at an open-source Large Language Model ChatGLM as baseline model. Also, Fuzzy Logic is implemented to regulate parameters, system's adaptability with Lee-Oscillator to refine model's response variability and precision. Experiment results showed that 5.12% improvement in Precision score, 11% increase in Recall metric, and 8% improvement in F1 score as compared to other models. © 2024 IEEE.

关键词： AI-based QA System Education Fuzzy Logic Large Language Model Lee-Oscillator

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Dynamic Priority Coflow Scheduling in Optical Circuit Switched Networks 24th

Dynamic Priority Coflow Scheduling in Optical Circuit Switch...

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24th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2023

作者： Ren, Hongkun Shen, Hong Wang, Xin School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Faculty of Applied Sciences Macao Polytechnic University China

ISBN: (纸本)9789819982103

OCS (Optical Circuit Switch) is increasingly popular for accelerating data transmission of coflows due to its higher bandwidth and lower power consumption compared with EPS (Electronic Packet Switch), where a coflow is a collection of related parallel flows between two computation stages in data-intensive applications. However, the extra port constraints and reconfiguration delay of OCS obstruct the efficiency of OCS operations. This paper studies the problem of coflow scheduling in the OCS of datacenter networks to minimize the total Coflow Completion Time (CCT). We propose a Dynamic Priority Coflow Scheduling Algorithm that schedules coflows preemptively by considering coflow transmission time and OCS reconfiguration delay jointly to dynamically update each coflow's priority, which can significantly reduce the waiting time of small coflows and reduce head-of-line blocking. Extensive simulations based on Facebook data traces show that our approach outperforms the state-of-the-art scheme OMCO [19] significantly, and transmits multiple coflows 1.30× faster than OMCO. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Scheduling algorithms

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Deep Reinforcement Learning Based Multi-WiFi Offloading of UAV Traffic 24th

Deep Reinforcement Learning Based Multi-WiFi Offloading of U...

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24th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2023

作者： Liu, Zhiyong Shen, Hong School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Faculty of Applied Sciences Macao Polytechnic University China

ISBN: (纸本)9789819982103

As the growing network deployment of Unmanned Aerial Vicheles (UAVs), traffic offloading has been widely used to mitigate UAV's problem of limited bandwidth in communications due to limited battery capacity. Existing work on traffic offloading has focused on reducing the average delay of the system without considering the fairness issue, and assumed that data transmission follows line-of-sight propagation which contradicts with the realistic situations in both urban and suburban areas. Achieving both fairness and system efficiency with non-line-of-sight user-UAV communication requires to solve a complex non-convex optimization problem. This paper proposes an effective algorithm (NAPPO) for joint UAV navigation and user traffic allocation by applying deep reinforcement learning (DRL). Our NAPPO applies DRL to collect user information (position, data rate and traffic demand) and dynamically adjusts the UAV position and traffic allocation ratio to minimize the maximum delay and hence improve the fairness (i.e., variation in delay between users). We show that our proposed approach of minimizing maximum delay is more effective than minimizing average delay for achieving fairness while preserving the total delay at a reasonable level. The results of the simulation experiments show that NAPPO achieves an impressive performance on the maximum delay, i.e., 49. 82% better than the heuristic algorithm and only 0.1536s worse than the optimal solution. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Light field depth estimation:A comprehensive survey from principles to future

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High-Confidence Computing 2024年第1期4卷 92-107页

作者： Tun Wang Hao Sheng Rongshan Chen Da Yang Zhenglong Cui Sizhe Wang Ruixuan Cong Mingyuan Zhao State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Zhongfa Aviation Institute Beihang UniversityHangzhou 311115China Faculty of Applied Sciences Macao Polytechnic UniversityMacao 999078China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Light Field(LF)depth estimation is an important research direction in the area of computer vision and computational photography,which aims to infer the depth information of different objects in threedimensional scenes by capturing LF *** this new era of significance,this article introduces a survey of the key concepts,methods,novel applications,and future trends in this *** summarize the LF depth estimation methods,which are usually based on the interaction of radiance from rays in all directions of the LF data,such as epipolar-plane,multi-view geometry,focal stack,and deep *** analyze the many challenges facing each of these approaches,including complex algorithms,large amounts of computation,and speed *** addition,this survey summarizes most of the currently available methods,conducts some comparative experiments,discusses the results,and investigates the novel directions in LF depth estimation.

关键词： Light field Depth estimation Deep learning Sub-aperture image Epipolar-plane image

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Minimize Resource Cost for Containerized Microservices Under SLO via ML-Enhanced Layered Queueing Network Optimization 14

Minimize Resource Cost for Containerized Microservices Under...

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Luan, Shao Shen, Hong School of Computer Science and Engineering Sun Yat-sen University China School of Engineering and Technology Central Queensland University Australia Macao Polytechnic University Faculty of Applied Sciences China

ISBN: (纸本)9798350344837

The microservice based architecture is widely used in large software systems. Despite its profound advantages, introduction of microservices brings in many challenges, especially in terms of autoscaling. Layered Queueing Network (LQN) as an effective mathematical formation for representing distributed systems can be used for modeling microservices and predicting microservices' performance. Traditional analysis methods solving LQN requires product-form solution and FCFS service discipline. Complex software design and unstable container environment is likely to prevent microservices from meeting these assumptions. To address this problem, we propose a novel autoscaling method using machine learning enhanced LQN optimization to minimize the total resource cost while satisfying the the requirement of Service Level Objectives (SLO). We adopt Deep Neural Networks (DNNs) to fit the submodels from LQN decomposition and propose a DNN based Method of Layers (MOL) to obtain the average response time metric. It avoids the drawbacks of the existing analytical methods. Applying particle swarm optimization to the DNN enhanced LQN, we present an effective method to search for the optimal resource allocation to containerized microservices that achieves our goal.. Experimental results show that our autoscaling method can reduce total CPU costs by 30% to 50% against baselines. © 2024 IEEE.

关键词： Containers

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Secrecy Rate Threshold VS. An Optimum Power Splitting Factor Using Beamforming Technique in PHY Layer Security for MU-MIMO Systems 3

Secrecy Rate Threshold VS. An Optimum Power Splitting Factor...

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3rd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Elhoula, Abeer A S Abousetta, Marai M. Ghmati, Tarek Saleh M. Libyan Academy School of Applied Science and Engineering Department of Electrical and Computer Engineering Libya Elemrgib University Al-Graboulli Faculty of Engineering Department of Electrical and Computer Engineering Libya

ISBN: (纸本)9798350319897

This paper describes a new approach to the problem of interception of wireless communication channels between the legitimate users. Physical PHY Layer Security (PLS) is new topic enhancing the secrecy performance of a Multi-User Multiple-Input-Multiple-Output (MU-MIMO) system for wireless communication from a single base-station to many users. Beamforming techniques such as "Pre-Maximum Ratio Combining (P-MRC) and Pre-Equal Gain Combining (P-EGC)"with assumption of perfect Channel State Information CSI is considered in order to achieve the perfect secure transmission between legitimate users. Along with beamforming, Artificial Noise (AN) is a technology that is being studied. The plotted achievable worst-case of secrecy rate down to the best secrecy rate value (threshold of secrecy rate) versus the power allocation fractions (The power splitting factor). The achieved threshold of secrecy rates for the implemented MU-MIMO were used by Matched Filter (MF) beamforming schemes (P-MRC) and (P-EGC) and suggested that the (P-MRC) is considered as an optimal beamforming technique in order to achieve a best secrecy rate or best threshold of secrecy rate for a nearest Information User (IU) to the Eavesdropper Eve. © 2023 IEEE.

关键词： Beamforming

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Event-based White Blood Cell Classification from Nailfold Capillaries

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IEIE Transactions on Smart Processing and Computing 2022年第4期11卷 270-275页

作者： Hariyani, Yuli Sun Park, Cheolsoo Department of Computer Engineering Kwangwoon University Seoul Korea Republic of School of Applied Science Telkom University Bandung Indonesia

Detecting absorption gaps in a nailfold capillary can be used to quantify an estimation of white blood cells (WBCs). Previously, the absorption gaps in a nailfold capillary were usually measured using a standard camera on a fingernail. However, difficulties arise due to low visibility of the gap, the small size of the capillaries, the high speed of WBC movement, and the lack of contrast between the capillary and its environment/background. To address these issues, an event-based WBC image is utilized as input data to detect WBC existence in the nailfold. Specifically, we utilize a dynamic vision sensor (DVS) camera, which can detect a change in luminance on a pixel basis and can produce a stream of asynchronous event output at a microsecond temporal resolution. With the event-based WBC dataset, we conduct a classification task using three different machine learning algorithms: k-nearest neighbors, the decision tree, and random forest. The best result is from random forest with 75.51% accuracy. Based on our evaluation, event-based WBC classification is a promising new approach to detecting WBC presence in nailfold capillaries. © 2022 Institute of Electronics and Information Engineers. All rights reserved.

关键词： Classification (of information)

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LRP-based network pruning and policy distillation of robust and non-robust DRL agents for embedded systems

LRP-based network pruning and policy distillation of robust ...

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作者： Luan, Siyu Gu, Zonghua Xu, Rui Zhao, Qingling Chen, Gang Department of Applied Physics and Electronics Umeå University Umeå Sweden School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

Reinforcement learning (RL) is an effective approach to developing control policies by maximizing the agent's reward. Deep reinforcement learning uses deep neural networks (DNNs) for function approximation in RL, and has achieved tremendous success in recent years. Large DNNs often incur significant memory size and computational overheads, which may impede their deployment into resource-constrained embedded systems. For deployment of a trained RL agent on embedded systems, it is necessary to compress the policy network of the RL agent to improve its memory and computation efficiency. In this article, we perform model compression of the policy network of an RL agent by leveraging the relevance scores computed by layer-wise relevance propagation (LRP), a technique for Explainable AI (XAI), to rank and prune the convolutional filters in the policy network, combined with fine-tuning with policy distillation. Performance evaluation based on several Atari games indicates that our proposed approach is effective in reducing model size and inference time of RL agents. We also consider robust RL agents trained with RADIAL-RL versus standard RL agents, and show that a robust RL agent can achieve better performance (higher average reward) after pruning than a standard RL agent for different attack strengths and pruning rates. © 2022 The Authors. Concurrency and Computation: Practice and Experience published by John Wiley & Sons Ltd.

关键词： Reinforcement learning

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