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Instruction selection optimization for VLIW architecture based on classification node merging

Journal of Supercomputing 2025年第7期81卷 1-19页

作者： Liu, Fangjun Zhang, Huifu Hu, Yonghua Xie, Anxing Cao, Wei Mo, Shangfeng School of Computer Science and Engineering Hunan University of Science and Technology XiangTan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology XiangTan411201 China

Traditional instruction selection methods fail to fully exploit the very long instruction word (VLIW) architecture’s efficient scalar instructions. We propose an optimized instruction selection method based on classification node merging. This method abstracts scalar intermediate instructions into nodes and divides them into associated and similar ones according to operational semantics. Combined with the VLIW architecture’s instruction set features and guided by the instruction cost model, the nodes are merged and optimized to select efficient instructions. We design an instruction generation framework that swiftly converts the low level virtual machine intermediate representation into the instruction sequence of the target architecture. The experimental results show that, compared with the instruction sequence generated by M7002-cc, the instruction sequence generated with the solution proposed in this paper exhibits an average execution performance that is up to 54% higher on the FT-M7002 hardware platform. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Real time systems

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Hyper-IIoT: A Smart Contract-inspired Access Control Scheme for Resource-constrained Industrial Internet of Things

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable computing 2025年

作者： Li, Dun Li, Hongzhi Crespi, Noel Minerva, Roberto Li, Ming Liang, Wei Li, Kuan-Ching Tsinghua University Department of Industrial Engineering China Institut Polytechnique de Paris Samovar Telecom SudParis Palaiseau91120 France Chizhou University College of Big Data and Artificial Intelligence Chizhou247100 China The Hong Kong Polytechnic University Research Institute for Advanced Manufacturing Hung Hom Hong Kong Hunan University of Science and Technology School of Computer Science and Engineering Hunan University of Science and Technology Sanya Research Institute Hunan Key Laboratory for Service Computing and Novel Software Technology China Providence University Dept. of Computer Science and Information Engineering Taiwan

In recent years, the refinements in industrial processes and the increasing complexity of managing privacy-sensitive data in Industrial Internet of Things (IIoT) devices have highlighted the need for secure, robust, and adaptive data management solutions. In this work, we propose a smart contract-assisted access control scheme for IIoT, which employs the Attribute-Based Access Control (ABAC) model to set access permissions for different industrial components. Besides, we defined a storage model and data format for private data by designing and deploying smart contracts to manage system operations and access policies. In addition, the bloom filter component is deployed to optimize the efficiency of contract management and system performance. Experimental results show that in real-world simulations, Hyper-IIoT demonstrates excellent contract execution time control, stable system throughput, and rapid consensus processes that are capable of handling high throughput and efficient consensus in distributed systems, even in large-scale request scenarios. © 2016 IEEE.

关键词： Smart contract

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Joint Optimization of Dynamic service Selection and Request Routing in Cloud-Edge Collaborative Environments

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IEEE Internet of Things Journal 2025年

作者： Tang, Bing Xu, Wei Zhang, Li Cao, Buqing Yang, Qing Li, Kuanching Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Guangzhou Maritime University School of Computer Science Guangzhou510725 China Huaihua University School of Computer and Artificial Intelligence Huaihua418000 China Hunan Institute of Engineering School of Information Science and Engineering Xiangtan411104 China Hunan University of Technology School of Computer Zhuzhou412007 China Hunan University of Science and Technology Sanya Research Institute Sanya572024 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China

Optimizing multi-instance service composition and dynamic request routing has become a critical challenge in cloud-edge collaborative systems. Existing solutions struggle with effectively balancing performance, cost, and bandwidth constraints in dynamic and resource-constrained environments. In this work, we address these challenges by proposing the Removed Minimum Cost Flow (RMCF) algorithm, aiming to minimize average response time while considering constraints such as budget and bandwidth, making it well-suited for time-sensitive services in a cloud-edge collaborative service provision system. Simulations were conducted using real-world data from China Telecom’s base stations in Shanghai, and experiments in various scenarios were considered, including time-sensitive services and general application services, with key performance indicators such as time utility, cost-utility, request completion rate, and timeout rate. The experimental results demonstrate that RMCF achieves lower response times, superior performance, and improved cost-effectiveness compared to other baseline algorithms. © 2014 IEEE.

关键词： Edge computing

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A Flexible Multi-Mechanism Market Model for Air Conditioning Users in Electricity Market: Design and Application

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IEEE Access 2025年

作者： Xiao, Chupeng Tang, Zhuofan Wang, Suoyue Wang, Kui Xiang, Rui Li, Xiaojing State Grid Electric Power Research Institute Wuhan Efficiency Evaluation Company Limited Hubei Wuhan China State Grid Electric Power Research Institute Jiangsu Nanjing China State Grid Shanghai Electric Power Company Shanghai China Shanghai Key Laboratory of Smart Grid Demand Response Shanghai China State Grid Shandong Marketing Service Center Shandong Jinan China PKU-Changsha Institute for Computing and Digital Economy Hunan Changsha China

With the continuous growth of electricity demand, optimizing load management strategies has become a crucial approach for alleviating the pressure on power supply. As a significant component of power consumption, air conditioning loads offer substantial curtailment potential. However, the current design of electricity market mechanisms for air conditioning users remains inadequate, making it difficult to fully mobilize user participation and utilize the curtailment potential of air conditioning loads. This paper considers market operation objectives across four dimensions: demand fulfillment, operational costs, user participation, and user comfort. A flexible multi-mechanism market model for air conditioning users is proposed, enabling market operators to design adaptable mechanisms that meet various operational needs. Additionally, a set of market mechanism evaluation metrics is developed. Through case studies, the application effects of various market mechanisms under different load reduction demands are compared. The results demonstrate that the proposed model provides a new approach to designing electricity market mechanisms for air conditioning users, effectively enhancing system responsiveness, flexibility, and economic efficiency of grid operations. © 2013 IEEE.

关键词： Demand response

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Short-term speed prediction of urban roads based on multi-source feature fusion 23

Short-term speed prediction of urban roads based on multi-so...

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23rd IEEE International Conference on High Performance computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Liu, Silin Liao, Zhuhua Liu, Yizhi Yi, Aiping School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory of Service Computing and New Software Service Technology Xiangtan China

ISBN: (纸本)9781665494571

Short-term traffic forecasting is an important means to prevent urban congestion. In this paper, the urban traffic operation time series data sets are used for modeling, combining with various characteristic variables of weather conditions, air quality index (AQI) and travel time index (TTI). A bidirectional long and short-term memory network framework (MF-BiLSTM) based on multi-source feature fusion was established to predict the average road speed. The comparison experiment selects three benchmark models: ARIMA, LSTM and CNN, and a bidirectional long short-term memory network (BiLSTM) model using only road speed time series data sets, and compares the prediction accuracy of these models under three prediction durations. The results show that MF-BiLSTM has the best prediction performance in terms of prediction error measurement, the prediction accuracy is 2.93%, 1.95%, 2.87%, 0.89% higher than that of ARIMA, LSTM, CNN, and BiLSTM, respectively. In addition, comparing the prediction accuracy of each model under different weather conditions and air pollution levels, MF-BiLSTM has the smallest deviation. © 2021 IEEE.

关键词： Correlation Roads Atmospheric modeling Time series analysis Weather forecasting Predictive models Air pollution

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SyNet:Medical image anomaly detection with noise synthesis network 10

SyNet:Medical image anomaly detection with noise synthesis n...

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10th IEEE Conference on Big Data Security on Cloud, BigDataSecurity 2024

作者： Liu, Jingnian Liang, Wei Chen, Yuxiang Jin, Kai Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Xiangtang China

ISBN: (纸本)9798350389524

Medical image anomaly detection refers to machine learning techniques to analyze and identify lesions and abnormalities in them. However, in medical images, anomaly samples are usually sparse, which can lead to supervised learning not being able to obtain sufficient training. While existing unsupervised learning is mainly based on reconstruction and generation as well as feature embedding, these methods do not take into account the problem of data type bias during domain migration and often face the problem of loose judgment boundaries. To this end, this paper proposes a novel unsupervised learning method, SyNet, based on noisy anomaly synthesis. First, the problem at domain migration is well solved by aggregating the range of features within the feature map, fusing the multiscale features at the intermediate level, and adding feature adapters. Then, anomaly samples are synthesized by adding noise in the feature space, which is more efficient and stable than generating samples in the image space. Finally, normal and abnormal data are distinguished by training the discriminator. In this paper, experiments are conducted on ISIC and brain MRI datasets, and SyNet achieves nearly 20% improvement in AUC, ACC and other metrics compared with the current mainstream methods, and also has good inference efficiency. © 2024 IEEE.

关键词： Anomaly detection

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ScIAMC:Single-Cell Imputation via adaptive matrix completion 10

ScIAMC:Single-Cell Imputation via adaptive matrix completion

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10th IEEE International Conference on Cyber Security and Cloud computing and 9th IEEE International Conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Zhang, Shuai Chen, Xiang Peng, Li Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan Hunan411201 China Hunan Key Laboratory for Service computing and Novel Software Technology China

ISBN: (纸本)9798350312461

Single-cell sequencing techniques are often impacted by technical noise, leading to the generation of very sparse expression matrices. This technical noise is referred to as dropouts and poses as a major challenge for downstream analysis. In this study, we introduce scIAMC (single-cell imputation via adaptive parameter matrix completion), which is based on matrix completion theory to recover missing values in expression matrices. To expedite the algorithm's running time and avoid any parameter tuning on data, we formulated an optimization problem. Our approach led to an enhanced cell population identification and minimal errors, while also restoring biological landscapes that were damaged by these dropouts. © 2023 IEEE.

关键词： Cell proliferation

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ESR: Optimizing Gene Feature Selection for scRNA-seq Data 10

ESR: Optimizing Gene Feature Selection for scRNA-seq Data

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10th IEEE International Conference on Cyber Security and Cloud computing and 9th IEEE International Conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Huang, Tao Chen, Xiang Peng, Li Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan Hunan411201 China Hunan Key Laboratory for Service computing and Novel Software Technology China

ISBN: (纸本)9798350312461

The rapid development of single-cell RNA sequencing (scRNA-seq) technology has enabled researchers to explore gene expression differences at the level of individual cells, revealing more refined cell types and states. However, due to the low expression and high noise of scRNA-seq data, feature selection has become particularly important in the analysis of single-cell data. Here, we introduce the Entropy Stepwise Regression (ESR) method for feature selection. This method utilizes the correlation between genes and the entropy values of each feature to filter out genes that are conducive to downstream analysis. In mouse kidney samples, we compared the performance of three methods in terms of Adjusted Rand Index and achieved good results. This indicates that the method can improve the accuracy of downstream analysis. © 2023 IEEE.

关键词： Entropy

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Towards Planning Package Delivery Paths by Exploiting the Crowd of Private Cars 24

Towards Planning Package Delivery Paths by Exploiting the Cr...

引用

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： Nie, Hejing Wen, Yiping Li, Tiancai Ouyang, Sipeng Tian, Muyang School of Computer Science and Engineering Hunan University of Science and Technology China Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology China

ISBN: (纸本)9798350319934

Crowdsourced delivery can provide cost-effective deliveries and it is becoming increasingly important in practice. This paper considers a package delivery problem which exploits relays of private cars to transport packages via their commuter routes. Specifically, we propose a method of planning package delivery paths based on the commuter routes of private cars. Using the commuter routes and time constraints as the reference, it can find optimal delivery paths and assign drivers of private cars to transport packages. The simulation experiment results show the validity and effectiveness of such method. © 2022 IEEE.

关键词： Cost effectiveness

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A Short Review of Truck and Drone Collaborative Delivery Problem 10

A Short Review of Truck and Drone Collaborative Delivery Pro...

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10th IEEE International Conference on Cyber Security and Cloud computing and 9th IEEE International Conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Zhu, Yena Liu, Min Jin, Dongyao Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Hunan Key Laboratory for Service computing and Novel Software Technology Xiangtan China

ISBN: (纸本)9798350312461

With the emergence of efficient logistics models and the development of drone technology, the last-mile delivery problem has become a focus of attention for scholars in various fields. The advantages of drones, such as low cost, energy saving, and independence from road conditions, have made them increasingly popular in the delivery industry. However, drones have shortcomings such as short endurance, small payload capacity, and susceptibility to interference. Therefore, the cooperative delivery model of vehicles and drones has important theoretical research significance and practical application value. Existing literature reviews focus on static truck-drone delivery problems (TDDPs) and methods such as exact algorithms, heuristics, and metaheuristics. However, real-life truck-drone delivery problems are easily affected by dynamic environmental changes. Accordingly, this article introduces some new dynamic variants of TDDPs proposed in recent years, which consider variable drone flight endurance, urban crowded road conditions, stochastic customer demands, and the influence of weather changes. In recent years, some new methods for solving TDDPs have been introduced, such as the increasingly popular machine learning and reinforcement learning methods. Finally, the possible future research directions and development trends of TDDPs are presented. © 2023 IEEE.

关键词： Reinforcement learning

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