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检索条件"主题词=partitioning algorithms"

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Invited Paper--Circuit partitioning with Reinforcement Learning and Edge-Based Initialization

Invited Paper--Circuit Partitioning with Reinforcement Learn...

引用

International Workshop on Rapid System Prototyping (RSP)

作者： Ka Chuen Cheng Umair F. Siddiqi Gary Grewal Shawki M. Areibi School of Computer Science University of Guelph Guelph Canada Information Technology King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia School of Engineering University of Guelph Guelph Canada

ISBN: (数字)9798331519537

ISBN: (纸本)9798331519544

The Fiduccia-Mattheyses-Sanchis (FMS) algorithm is a widely used local search method for K-way circuit partitioning, but it’s prone to getting stuck in local minima. Traditionally, this has been addressed by running FMS multiple times with different random initial solutions, hoping for a better result. Building on our previous work with an RL-based local search method that helps FMS avoid these traps, this research explores a new approach: using constructive methods to generate superior initial solutions. We explored two such methods: NDE (node growing algorithm), a commonly used node-based method that maximizes node absorption, and NET (net growing algorithm), an edge-based approach that maximizes net absorption. By integrating NDE and NET with our RL-based local search, we’ve achieved significant improvements. Experiments on ISPD98/IBM benchmarks demonstrate that an edge-based approach provides higher-quality solutions for larger circuits and larger numbers of partitions. Combining these initial solutions with our RL-based approach further reduces the cutsize generate by the RL-based approach by up to 79.5%.

关键词： Frequency modulation Absorption Search methods Conferences Buildings Reinforcement learning Benchmark testing partitioning algorithms

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TBPart-b: An Effective Hypergraph partitioning Algorithm Considering Topological Order Balance for Processor-Based Hardware Emulation

TBPart-b: An Effective Hypergraph Partitioning Algorithm Con...

引用

International Conference on Solid-State and Integrated Circuit Technology

作者： Jing Tang Shunyang Bi Hailong You Xidian University China

ISBN: (数字)9798350361834

ISBN: (纸本)9798350361841

Ant selection for updating the pheromone is one most crucial operation in ant colony optimization (ACO). In this direction, this paper designs an adaptive ant selection strategy (AAS) to adaptively and dynamically select ants to update the pheromone for ACO. Therefore, a new ACO, called AAS-ACO is developed. Specifically, AAS-ACO first assigns a non-linear selection probability to each ant based on its path ranking. As a result, better ants preserve exponentially higher selection probabilities. Then, based on the selection probabilities, ants are adaptively selected for the pheromone update. By this means, on the one hand, the number of ants involved in the pheromone update is uncertain; on the other hand, relatively better ants instead of absolutely better ones are adaptively selected to update the pheromone, leading to the promotion of search diversity. Subsequently, a dynamic weighting strategy is designed to adjust the amount of the pheromone deposited by the best ant in the current iteration to enhance the search convergence. With the two schemes, AAS-ACO is expected to maintain a suitable compromise between search diversity and search convergence to seek the optimal solutions to TSP. Experiments on 10 classical TSP instances varying from 100 to 1000 cities have proven the significant superiority of AAS-ACO to 5 classic ACOs, especially on high-dimensional TSP problems.

关键词： Hands Measurement Integrated circuit technology Runtime Emulation Urban areas Very large scale integration Hardware partitioning algorithms Convergence

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Multi-objective Cuckoo Boundary Search based partitioning Flexible Interconnection Grid Planning Method

Multi-objective Cuckoo Boundary Search based Partitioning Fl...

引用

Energy, Power and Grid (ICEPG), International Conference on

作者： Xun Lu Xianfu Gong Peng Wang Guangdong Power Grid Co. Ltd. Power Guangdong China Guangdong Power Grid Co. Ltd. Power Power Grid Planning Research Center Guangdong China Civil Engineering and Mechanics Jiangsu University Jiangsu China

ISBN: (数字)9798350377798

ISBN: (纸本)9798350377804

The stochastic and volatile nature of large-scale new energy grid integration has posed a major challenge to the new power system. In this paper, we firstly introduce the reliability indicator system for planning the AC-DC interconnected main grid, which accounts for the spatio-temporal distribution, and then we comprehensively assess and optimize the reliability of AC/DC hybrid grid based on the spatio-temporal distribution characteristics. Secondly, an optimization problem is constructed to optimize different performance indicators in a compromise manner. Thirdly, a multi-objective cuckoo search optimization based partitioning flexible interconnection grid planning algorithm is proposed, which is able to quickly find the global optimal solution or near-optimal solution, adapting to the system demand changes and taking into account a variety of practical constraints. Finally, simulation results verify that the proposed algorithm improves the convergence performance by 37.15% and $\mathbf{5 9. 3 1 \%}$, and the adaptability by $\mathbf{7 1. 3 2 \%}$ and 78.24%, respectively, compared with the two baseline algorithms.

关键词： Simulation Search problems Planning partitioning algorithms Power system reliability Reliability Optimization Convergence

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3D-HEVC Fast partitioning Algorithm Based on MD-CNN

3D-HEVC Fast Partitioning Algorithm Based on MD-CNN

引用

Artificial Intelligence & Green Energy (ICAIGE), IEEE International Conference on

作者： Nacir Omran Imen Werda Amna Maraoui Rostom Kachouri Hamdi Belgacem LEμE University of Monastir Monastir Tunisia LETI University of Sfax Sfax Tunisia LEAT University of Nice Nice France LIGM Gustave Eiffel University ESIEE Paris Paris France

ISBN: (数字)9798350389838

ISBN: (纸本)9798350389845

The introduction of the 3D-HEVC encoding standard has revolutionized the field of 3D and multi-view video by effectively synthesizing 3D video with adequate depth effects using depth map sequences. However, achieving this involves computationally intensive operations, such as the quad-tree partitioning of Intra Coding Units (CUs). Researchers used multiple approaches including heuristic, machine learning and deep learning to address this complexity. We opted to train a Multi-Deep Convolutional Neural Network (MD-CNN) model specifically for depth maps and integrated it into the 3D-HEVC encoder. This modified encoder uses the independent views as a reference to encode the dependent depth map views. This strategy reduces the complexity of the 3D-HEVC video encoder by 70.12% while slightly reducing video compression efficiency by 0.02 % and a small Peak signal-to-noise ratio (PSNR) penalty of -0.80 dB.

关键词： Three-dimensional displays PSNR Neural networks Rate-distortion Video compression Encoding Complexity theory Topology partitioning algorithms Standards

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Semi-Supervised Negative Selection Algorithm Based on Feature Space Adaptive partitioning and Label Propagation for Fault Detection

Semi-Supervised Negative Selection Algorithm Based on Featur...

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Information Science, Computer Technology and Transportation (ISCTT), International Conference on

作者： Ziqi Liu Shouke Gu Hongchao Wang Ming Kang China Tobacco Sichuan Industrial Co. LTD Chengdu China The Fifth Electronic Research Institute of MIIT Guangzhou China School of Cyber Science and Engineering Sichuan University Chengdu China

ISBN: (数字)9798350388435

ISBN: (纸本)9798350388442

Fault detection plays an important role in ensuring the reliability and safety of industrial systems. However, traditional fault detection methods often require a large amount of labeled data for the learning process, which can be hard to satisfy. In the paper, we propose a semi-supervised negative selection algorithm (SS-NSA) that leverages feature space adaptive partitioning and label propagation tolerating with a limited number of labeled samples. Firstly, the label propagation algorithm (LPA) expands the limited number of labeled samples, enabling the SS-NSA to learn from labeled and unlabeled data. Then, the feature space is divided into cells of different sizes according to the data density. The space partition is utilized to reduce the complexity of NSA's detector training process, and the detectors are trained only in these cells, which contain both labeled self/non-self (normal/abnormal) samples. Extensive experiments are carried out on benchmark fault detection datasets and real-world industrial datasets, demonstrating the effectiveness and superiority of SS-NSA, compared with traditional NSAs and other fault detection algorithms.

关键词： Training Fault detection Heuristic algorithms Transportation Detectors Benchmark testing Feature extraction Robustness partitioning algorithms Safety

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Bridge-Rhombus Transformation to Solve partitioning Problem in Telecommunications Network

Bridge-Rhombus Transformation to Solve Partitioning Problem ...

引用

International Technical Conference o Circuits/Systems, Computers and Communications (ITC-CSCC)

作者： Masahiro Hayashi Department of Electrical Electronic and Communication Engineering Faculty of Science and Engineering Tokyo Japan

ISBN: (数字)9798350379051

ISBN: (纸本)9798350379068

We propose a new reduction, defined as the operation of deleting a link which is shared in two deltas, where delta is complete graph having three nodes. We call this reduction a bridge-rhombus transformation. This transformation enables us to simplify a graph without affecting the properties of whether or not it can be partitioned into two subgraphs with each subgraph being a connected graph and that both nodes of every pre-specified node pair have these two subgraphs. This type of partitioning is known to be useful for making highly reliable telecommunications networks. However, the previous solution to this problem showed an exponential increase in computation time as the size of the graph increases. Here, the bridge-rhombus transformation can be incorporated into the previous algorithm to give a faster solution.

关键词： Computers Computer network reliability Telecommunications partitioning algorithms Telecommunication network reliability

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HP-ICM: Curiosity-Driven Offloading Based on Hypergraph partitioning

HP-ICM: Curiosity-Driven Offloading Based on Hypergraph Part...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Chengwei Wang Kang Huang Chenxuan Hou Chao Qiu Xiaofei Wang College of Intelligence and Computing Tianjin University Tianjin China

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Computing power network (CPN) is a distributed network system designed to connect and integrate computing resources globally, enabling efficient sharing and utilization of computing power. In dependent task offloading, the dependency relationship between tasks is generally used to determine the ex-ecution order. However, the assignment phase often overlooks the relevance and sharing between tasks, leading to a waste of system resources in CPNs. In the learning process, agents frequently encounter the issue of sparse rewards, which results in slow learning and makes it challenging to develop effective strategies. To address the aforementioned issues, we design a dependent task offloading method based on hypergraph partitioning and an intrinsic curiosity module, i.e., HP-ICM, which offloads tasks with similar resource requirements or dependencies into the same partition and utilizes the ICM to enhance the speed and quality of learning. Simulation results show that HP-ICM can reduce latency by 22.8% and energy consumption by 25.7% compared to the PPO baseline during task offloading,

关键词： Training Energy consumption Simulation Smart systems Ad hoc networks partitioning algorithms Network systems

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Data partitioning and Storage Strategies for Artificial Intelligence and Machine Learning Applications: A Review of Techniques

Data Partitioning and Storage Strategies for Artificial Inte...

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International Conference on Computer and Applications (ICCA)

作者： Kingsley C. Nwosu Ibrahim Kamara Musbah Abdulgader Yen-Hung Hu Department of Computer Science Norfolk State University Norfolk VA USA

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

This paper presents a review of data partitioning and storage strategies critical for optimizing the performance and scalability of Artificial Intelligence (AI) and Machine Learning (ML) applications. As the volume and complexity of data continue to grow, effective data management becomes increasingly essential for ensuring the efficiency and accuracy of AI/ML models. The review categorizes and evaluates various data partitioning techniques, including hash-based, range-based and graph-based algorithms, highlighting their strengths, weaknesses, and suitability for different types of datasets and applications. It also explores the data processing challenges with AI/ML applications, data access patterns for AI/ML algorithms; and implementation techniques and evaluation metrics. By identifying key factors that influence the choice of partitioning and storage strategies, this review provides valuable insights for researchers and practitioners seeking to enhance the performance of AI/ML systems. The review also serves as a foundation for future research and development, guiding the design of more efficient and scalable data management solutions in artificial intelligence.

关键词： Technological innovation Machine learning algorithms Reviews Scalability Distributed databases Machine learning Computer architecture Big Data Data processing partitioning algorithms

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A New Swap-Based Algorithm for Solving Multidimensional Multi-Way Number partitioning Problem

A New Swap-Based Algorithm for Solving Multidimensional Mult...

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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Bozhan Zhang University of Electronic Science and Technology of China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

The Multidimensional Multi-Way Number partitioning Problem (MDMWNPP) is a variant problem of the Number partitioning Problem (NPP). A special case of the Number partitioning Problem (NPP) known as the Two-Way Number partitioning Problem (TWNPP) is one of the original 21 problems that Richard Karp proved to be NP-complete. The existing algorithms usually solve the Mixed-Integer Linear Programming (MILP) model of the MDMWNPP by solver or consider the problem from the perspective of metaheuristics. This paper proposes a swap-based heuristic algorithm to solve the MDMWNPP from another point of view. At the same time, the paper provides theoretical supports for the swap process and applies the theory to the new algorithm. The new algorithm has achieved the best results on average when compared with the previous algorithms, and it can also provide solutions that previous algorithms were unable to obtain within a limited time. In terms of experimental performance, there is an improvement of approximately 30%-40% on the previous benchmark sets, while the time efficiency has been improved by one or two orders of magnitude.

关键词： Heuristic algorithms Metaheuristics Information processing Media Benchmark testing Approximation algorithms Distance measurement partitioning algorithms Mixed integer linear programming

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A Dynamic Sharding Scheme For Blockchain Based On Graph partitioning

A Dynamic Sharding Scheme For Blockchain Based On Graph Part...

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IEEE International Conference on Blockchain (Blockchain)

作者： Huan Li Dongbin Wang Hui Zhi Yuchen Wang Tao Yang Jiang Song School of Cyberspace Engineering Beijing University of Post and Telecommunications Beijing China National Engineering Laboratory for Mobile Network Beijing China TravelSky Technology Limited Beijing China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9798350351590

ISBN: (纸本)9798350351606

Blockchain is a decentralized distributed ledger that is stored in a block chain, cannot be tampered with, is safe and trustworthy with the help of cryptography and consensus mechanism. However, the current performance and scalability of blockchain hinders its practical application in wider areas, so blockchain sharding technology was proposed. Sharding technology divides the blockchain into multiple shards. Each shard is responsible for processing a part of transactions and status. Multiple shards verify and process transactions in parallel, which improves the performance of the blockchain. Although sharding technology has potential in theory, in practical applications, it still faces a series of problems, such as shard load imbalance and a high proportion of cross-shard transactions. In order to solve the problems of unbalanced shard load and high proportion of cross-shard transactions in the blockchain sharding system, the DLDG (Dynamic Linear Deterministic Greedy) dynamic account division algorithm is proposed. DLDG constructs the shard load and cross-shard transaction ratio. The shard transaction rate balancing model periodically divides the account transaction network graph through historical transaction data, thereby achieving shard load balancing and reducing the cross-shard transaction rate. Experimental results show that this scheme alleviates performance problems caused by load imbalance and high cross-shard transaction rates.

关键词： Sharding Heuristic algorithms Scalability Smart contracts Load management Blockchains partitioning algorithms

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