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Aging-Aware Energy-Efficient Task Deployment of Heterogeneous Multicore Systems

IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2024年第5期43卷 1580-1593页

作者： Chen, Yu-Guang Wang, Chieh-Shih Lin, Ing-Chao Chen, Zheng-Wei Schlichtmann, Ulf National Central University Department of Electrical Engineering Taoyuan320 Taiwan National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan Technical University of Munich Chair of Electronic Design Automation Munich80333 Germany

Heterogeneous multicore systems, which consist of high-performance and power-efficient cores, are emerging to satisfy the various demands on performance and power consumption. On the other hand, as CMOS technology continues to shrink in size, the aging effect, which can cause performance degradation or timing failures, has become a non-negligible threat to lifetime reliability. To overcome the challenges under the aging effect, various approaches have been proposed in previous studies. Most previous studies, however, did not consider the different characteristics of big and little cores. In addition, most of them do not consider critical tasks with the strict timing requirements present in real-time applications, resulting in early system failure. Therefore, considering different characteristics of cores and the presence of critical tasks, we propose an aging-aware task deployment framework for real-time systems. In this framework, for high-performance big cores, we propose a novel asymmetric aging-aware strategy. This strategy finds an energy-efficient task-to-core assignment to reserve some healthy cores at the early system life stage. The reserved cores are kept idle with the lowest voltage and can execute critical tasks at the late system life stage, extending the system lifetime. Meanwhile, the nonreserved cores use lower voltages to execute tasks, reducing the aging effect. For energy-efficient little cores, we adopt the symmetric aging-aware strategy to balance out the aging effect of each little core. With a balanced aging effect, the utilization of little cores is improved. In addition, we propose voltage/frequency boosting and task migration techniques to increase the number of cores that can meet the task timing constraints. Compared to the state of the art, the proposed framework can achieve 1.10× lifetime improvement and 5% energy reduction. © 1982-2012 IEEE.

关键词： computer architecture

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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A Software Defect Prediction Method Using a Multivariate Heterogeneous Hybrid Deep Learning Algorithm

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computers, Materials & Continua 2025年第2期82卷 3251-3279页

作者： Qi Fei Haojun Hu Guisheng Yin Zhian Sun College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Information Technology Research Department Jiangsu Automation Research InstituteLianyungang222062China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.

关键词： Software defect prediction multiple heterogeneous data graph convolutional network models based on adjacency and spatial topologies CNN-BiLSTM TabNet

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Towards migration from IEC 61131-3 to IEC 61499 in process industry: Redesign of visualisation 32

Towards migration from IEC 61131-3 to IEC 61499 in process i...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Kothalawala, Hiruni Jhunjhunwala, Pranay Vyatkin, Valeriy Aalto University Department of Electrical Engineering and Automation Espoo Finland Lulea Tekniska Universitet Department of Computer Science Computer and Space Engineering Sweden

ISBN: (纸本)9798350399714

This work focuses on reengineering an HMI implemented in a third-party legacy tool to an IEC 61499 implementation. We propose a method to re-engineer the view for a process system and gather relevant information for the migration of the controller to IEC 6499. The proposed methodology has been tested and demonstrated using a lab-scale 'Water process system' that is currently controlled using an IEC 61131-3 compliant PLC with a view and HMI developed on a legacy tool. It is intended to migrate to the IEC 61499-compliant EcoStruxure automation Expert (EAE) tool. © 2023 IEEE.

关键词： Process control

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Multi-dimensional networks as a tool to model, analyze, and interpret multi-subject brain connectivity in hyperscanning settings

Multi-dimensional networks as a tool to model, analyze, and ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Puxeddu, Maria Grazia Rinaldini, Greta Astolfi, Laura University of Rome La Sapienza Department of Computer Control and Management Engineering Rome Italy

ISBN: (纸本)9798350386226

Social interactions are woven in the fabric of our lives, from birth to adulthood. Studies show that when social connections are lacking, there is a high probability of developing poor mental and physical health. Conversely, some mental disorders result in a compromised ability to form these interactions, with autism being a prime example. For these reasons, understanding the neural mechanisms underlying social interactions is crucial. Hyperscanning experiments, where brain activity is simultaneously recorded from multiple subjects during social tasks, have emerged as a powerful tool for tackling this issue, and the study of brain-to-brain connectivity has provided significant insights into social neuroscience. However, no agreed-upon methods exist for analyzing and interpreting such data, and most of the analyses still rely on techniques designed for single-subject experiments. Here, we propose a framework to model brain-to-brain connectivity through multi-dimensional networks. Under this conceptualization, both intra- and inter-brain connectivity are viewed as components of a higher-order network that can be studied by extending graph theory indices to a multidimensional case. In this article, we also discuss how these indices can be interpreted in the context of multi-subject networks. Future investigations will apply this framework to hyperscanning datasets to provide new insights into the neural circuits associated with social interactions. © 2024 IEEE.

关键词： Diseases

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Semantic Explanations of Classifiers through the Ontology-Based Data Management Paradigm (Extended Abstract) 37

Semantic Explanations of Classifiers through the Ontology-Ba...

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37th International Workshop on Description Logics, DL 2024

作者： Papi, Laura Cima, Gianluca Console, Marco Lenzerini, Maurizio Department of Computer Control and Management Engineering 25 Via Ariosto Rome00185 Italy

One of the main challenges in modern AI systems is to explain the decisions of complex machine learning models, and recent years have seen a burgeoning of novel approaches. These approaches often rely on some structural components of the models under consideration, e.g., the set of features used for the classification task. As a result, explanations provided by these approaches are expressed in terms of the sub-symbolic information and, therefore, they are hard to interpret for users. In this paper, we argue that, in order to foster interpretability, these explanations should be expressed in terms of the knowledge that the users posses on the underlying application domain rather than on the sub-symbolic components of the model. To this end, our first contribution is the illustration of a novel formal framework for explaining the decisions of machine learning classifiers grounded on the Ontology-Based Data Management paradigm. Within this framework, explanations are defined by logical formulae using the symbols that an ontology defines and, as such, they posses a well-defined semantics. As a second contribution, we provide an algorithm that computes the best explanations that can be expressed in the class of conjunctive queries. © 2024 Copyright for this paper by its authors.

关键词： Adversarial machine learning

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An Optimal Weighted Ensemble of 3D CNNs for Early Diagnosis of Alzheimer’s Disease

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SN computer Science 2024年第2期5卷 252页

作者： Dharwada, Sriram Tembhurne, Jitendra Diwan, Tausif Department of Computer Science and Automation Indian Institute of Science Bangalore India Department of Computer Science and Engineering Indian Institute of Information Technology Maharashtra Nagpur 441108 India

Alzheimer disease (AD) is a chronic neurological disorder in which the loss of brain cells causes dementia. Early and accurate diagnosis of AD will lead to better treatment of the disease before irreversible brain damage has been occurred. This paper proposes the classification of Alzheimer's disease using 3D structural Magnetic Resonance Imaging (sMRI) images through 3D convolutional neural networks (CNNs). Most existing methods utilizing 3D subject-level CNNs for Alzheimer's disease classification design a single model which relies on a very large training dataset for improved generalization. Herein, we address this issue through 3D transfer learning which makes use of knowledge gained from a pre-trained task. We train 3D versions of five classical 2D image classification architectures—ResNet, ResNeXt, SE-ResNet, SE-ResNeXt, and SE-Net—by initializing each model with pre-trained weights from their 2D counterparts, and combine their predictions through a weighted average method. The weights assigned to each model of the ensemble are optimized to achieve a performance better than any single 3D CNN model. With a relatively smaller training dataset, the proposed model obtains 97.27%, 82.33%, 90.41%, 84.22%, 84.26%, and 77.1% accuracies for the Alzheimer’s disease (AD) versus cognitively normal (CN), early mild cognitive impairment (EMCI) versus CN, late mild cognitive impairment (LMCI) versus CN, EMCI versus AD, LMCI versus AD, and EMCI versus LMCI classification tasks, outperforming current state-of-the-art methods, and indicating the effectiveness of our proposed model. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Alzheimer’s disease Convolutional neural networks Ensemble learning Transfer learning

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Real-time and Energy-aware Scheduling for Edge-to-Cloud Continuum based on Reinforcement Learning 28

Real-time and Energy-aware Scheduling for Edge-to-Cloud Cont...

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28th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2024

作者： Panceri, Andrea Mattia, Gabriele Proietti Beraldi, Roberto Sapienza University of Rome Control and Management Engineering "A. Ruberti" Department of Computer Italy

ISBN: (纸本)9798331527211

By spreading out computing workloads over multiple levels, the Edge-to-Cloud continuum paradigm improves the performance of applications that are sensitive to latency. However, real-time scheduling is difficult on the Edge computing layer since it consists of a variety of nodes with varying uptime. In this paper, we address this issue by proposing an online and adaptive scheduling algorithm based on a continuous learning Reinforcement Learning. Our algorithm determines each work request individually, optimizing scheduling policies to meet real-time application requirements while taking environmental energy and battery limits into account. We validate the efficacy of our approach in dynamically assigning tasks, particularly in scenarios where Edge nodes exhibit variable speeds and unpredictable failures, while efficiently managing energy resources and battery constraints through extensive simulations and comparisons with static scheduling strategies. © 2024 IEEE.

关键词： Scheduling algorithms

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Informativeness of Query Answers for Knowledge Bases (Extended Abstract) 37

Informativeness of Query Answers for Knowledge Bases (Extend...

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37th International Workshop on Description Logics, DL 2024

作者： Andolfi, Luca Cima, Gianluca Console, Marco Lenzerini, Maurizio Department of Computer Control and Management Engineering 25 Via Ariosto Rome00185 Italy

This extended abstract summarises our recent work on the informativeness of query answers over Description Logics Knowledge Bases (KBs). We introduce a framework to characterise the information that query answers for KBs can represent and under its lens we study the informative power of current query answering definitions across the literature. Moreover, we also present novel notions of certain and possible answers, showing that they are able to represent a meaningful form of information. Lastly, we study the data complexity properties for relevant problems associated to the answers we introduced. © 2024 Copyright for this paper by its authors.

关键词： Structured Query Language

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Towards Interoperability of Edge Datacentre in the Energy Community with IEC 61850 Modelling 49

Towards Interoperability of Edge Datacentre in the Energy Co...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Yang, Chen-Wei Galkin, Nikolai Vyatkin, Valeriy Luleå Tekniska Universitet Department of Computer Science Computer and Space Engineering Sweden Aalto University Department of Electrical Engineering and Automation Espoo Finland

ISBN: (纸本)9798350331820

Green modular datacentres are a new class of datacentres which can reduce the carbon footprints of the datacentre industry which accounts for close to 1% of total energy use worldwide. Green modular datacentres can operate as a prosumer and contribute energy to the energy grid through participation in the energy market as a part of the energy community. This paper investigates the interoperability of the datacentre in the energy community by proposing an IEC 61850 Logical Device model for a green modular datacentre. The applicability of the datacentre model is demonstrated through the re-implementation of the Energy Management System of an edge datacentre installation with the proposed IEC 61850 Logical Device model and its logical implementation in IEC 61499. © 2023 IEEE.

关键词： Interoperability

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