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13th IEEE Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2022

作者： Kirchhoff, Michael Debo, Saad Nau, Johannes Group for Computer Architecture and Embedded Systems Technische Universität Ilmenau Ilmenau Germany

ISBN: (数字)9781665463164

ISBN: (纸本)9781665463164

Accurate indoor position tracking and analysis of the movement dynamics of autonomous driving systems are important challenges when it comes to automatize industrial processing, supply chains or warehouses. In this paper, the authors present an indoor position tracking architecture with a novel sensor fusion approach for autonomous robots in three-dimensional space. For robots to be able to drive autonomous, they need information of their position, speed and orientation in 3D-space. With the presented architecture, position information is provided by the Indoor Positioning System (IPS) and orientation information as well as velocity are determined by the Inertial Navigation System (INS). The proposed tracking architecture combines those informations with a sensor fusion approach, thus enabling the autonomous driving system. © 2022 IEEE.

关键词： Real time systems

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Ambient intelligence for self-care and self-management

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Journal of Ambient Intelligence and Humanized Computing 2023年第1期14卷 1-3页

作者： Hervás, Ramón Baños, Oresti Villalonga, Claudia Department of Technologies and Information Systems Modelling Ambient Intelligence Research Group University of Castilla-La Mancha Paseo de La Universidad 4 Ciudad Real Spain Department of Computer Architecture and Computer Technology Research Center for Information and Communication Technologies University of Granada Calle Periodista Rafael Gomez Montero 2 Granada Spain

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ALF: A fitness-based artificial life form for evolving large-scale neural networks 21

ALF: A fitness-based artificial life form for evolving large...

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2021 Genetic and Evolutionary Computation Conference, GECCO 2021

作者： Krauss, Rune Merten, Marcel Bockholt, Mirco Drechsler, Rolf Group of Computer Architecture University of Bremen Cyber-Physical Systems Dfki GmbH Germany

ISBN: (纸本)9781450383516

Topology and Weight Evolving Artificial Neural Network (TWEANN) is a concept to find the topology and weights of Artificial Neural Networks (ANNs) using genetic algorithms. However, a well-known downside is that TWEANN algorithms often evolve inefficient large ANNs for large-scale problems and require long runtimes. To address this issue, we propose a new TWEANN algorithm called Artificial Life Form (ALF) with the following technical advancements: (1) speciation via structural and semantic similarity to form better candidate solutions, (2) dynamic adaptation of the observed candidate solutions for better convergence properties, and (3) integration of solution quality into genetic reproduction to increase the probability of optimization success. Experiments on large-scale problems confirm that these approaches allow effective solving of these problems and lead to efficient evolved ANNs. © 2021 Owner/Author.

关键词： Genetic algorithms

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Economy-based Greedy Bidding for Resources for CAE Workflows in Hybrid Cloud Infrastructure

Economy-based Greedy Bidding for Resources for CAE Workflows...

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IEEE International Conference on e-Science and Grid Computing

作者： Srishti Dasgupta Tähvend Uustalu Michael Gerndt Babak Gholami Chair of Computer Architecture and Parallel Systems Technische Universität München Garching (near Munich) Germany BMW Group Munich Germany

ISBN: (数字)9798350365610

ISBN: (纸本)9798350365627

The advent of generative design in the automotive sector, characterised by the automatic and iterative exploration of expansive solution spaces to discover optimal design configurations, has significantly increased the demand for computational resources to run intensive computer-aided engineering (CAE) simulations within constrained time frames. The inherent limitations of static high-performance computing (HPC) clusters have necessitated the adoption of cloud resources due to their flexible and elastic nature, thereby enhancing the capacity to accommodate the computational demands of these iterative workflows. These workflows, represented as Directed Acyclic Graphs (DAGs), involve the serial and parallel execution of tasks, which can dynamically share resources with other workflows during idle periods. In this paper, we propose an economy-based approach to exploit the gaps generated by these idle periods through a bidding system, thereby enabling more efficient resource utilisation and reducing the average wait time, makespan, cost and deadline miss by more than 40%, 6%, 13% and 45%respectively against certain infrastructures and baselines. Furthermore, we explore the potential for generating revenue by renting out idle resources in a hybrid cloud setup. This approach not only aims to optimise the use of computational resources but also seeks to provide cost-effective solutions to meet the escalating demands of generative design in the automotive sector.

关键词： Directed acyclic graph Costs High performance computing Computational modeling Hybrid power systems Iterative methods Time factors

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Elastic Workflows in Hybrid Cloud for CAE Simulations

Elastic Workflows in Hybrid Cloud for CAE Simulations

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IEEE International Conference on Cloud Engineering (IC2E)

作者： Srishti Dasgupta Michael Gerndt Babak Gholami Chair of Computer Architecture and Parallel Systems Technische Universität München Garching (near Munich) Germany BMW Group Munich Germany

ISBN: (数字)9798331528690

ISBN: (纸本)9798331528706

Companies traditionally dependent on on-premise HPC clusters for simulations are increasingly migrating workloads to the cloud. Cloud computing offers greater flexibility in selecting processors, memory, network bandwidth, along with enhanced resource availability and scalability. Automotive companies rely on computationally intensive numerical simulation tools for CAE (computer-Aided Engineering), particularly with the growing demand for generative design, which utilizes algorithms to automatically explore a large solution space. This work addresses the gap between the growing runtime demands of these simulations and the limitations of static HPC infrastructure by representing iterative workflows as Directed Acyclic Graphs (DAGs) and optimizing their scheduling. We propose a unified hybrid infrastructure that leverages the elasticity of cloud resources along with existing HPC clusters to maximize computational efficiency, ensure timely completion of simulations, and optimize resource utilization and costs.

关键词： Cloud computing Runtime Program processors Processor scheduling Computational modeling Scalability Companies Numerical simulation Numerical models Resource management

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VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency

arXiv

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arXiv 2025年

作者： Zhao, Zhuorui Qiu, Ruidi Lin, Ing-Chao Zhang, Grace Li Li, Bing Schlichtmann, Ulf Munich Germany Computer Architecture and IC Design National Cheng Kung University Taiwan Hardware for Artificial Intelligence Group Technical University of Darmstadt Germany Research Group of Digital Integrated Systems University of Siegen Germany

Large Language Models (LLMs) have demonstrated promising capabilities in generating Verilog code from module specifications. To improve the quality of such generated Verilog codes, previous methods require either time-consuming manual inspection or generation of multiple Verilog codes, from which the one with the highest quality is selected with manually designed testbenches. To enhance the generation efficiency while maintaining the quality of the generated codes, we propose VRank, an automatic framework that generates Verilog codes with LLMs. In our framework, multiple code candidates are generated with LLMs by leveraging their probabilistic nature. Afterwards, we group Verilog code candidates into clusters based on identical outputs when tested against the same testbench, which is also generated by LLMs. Clusters are ranked based on the consistency they show on testbench. To determine the best candidate, Chain-of-Thought is further applied to select the best candidate from the top-ranked clusters. By systematically analyzing diverse outputs of generated codes, VRank reduces errors and enhances the overall quality of the generated Verilog code. Experimental results on the VerilogEval-Human benchmark demonstrate a significant 10.5% average increase in functional correctness (pass@1) across multiple LLMs, demonstrating VRank’s effectiveness in improving the accuracy of automated hardware description language generation for complex design tasks. © 2025, CC BY-NC-SA.

关键词： computer hardware description languages

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Efficient Evolution of Variable Ordering for Binary Decision Diagram Optimization

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IEEE Transactions on Evolutionary Computation 2025年

作者： Krauss, Rune Drechsler, Rolf German Research Center for Artificial Intelligence Cyber-Physical Systems Department Bremen28359 Germany University of Bremen Group of Computer Architecture Institute of Computer Science Bremen28359 Germany

The hardware complexity related to the number of transistors in electronic devices used by today's society has grown considerably in the last decades because of technological progress. In order to guarantee the correct behavior of such devices and meet time-to-market constraints, there is a need to continuously develop more efficient data structures and algorithms in formal verification. The performance of verification algorithms depends in particular on the compactness of data structures. A reduced ordered binary decision diagram (BDD) is basically a suitable data structure to verify digital circuits, as it represents Boolean functions canonically respecting a variable ordering and allows direct manipulation of them. However, one of the main challenges with BDDs is to find a variable ordering so that the BDD size of a function is compact and does not become impractical due to a dramatically increasing number of BDD nodes. To address the aforementioned issue, this article presents a novel evolution strategy having an efficient evaluation of variable ordering in a divide-and-conquer manner for BDD optimization. Experiments on benchmarks of multilevel circuits show that using this strategy results in considerably smaller BDDs being found significantly faster compared to state-of-the-art optimization techniques. © 1997-2012 IEEE.

关键词： Evolutionary algorithms

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Compliance Management of IaC-Based Cloud Deployments During Runtime 23

Compliance Management of IaC-Based Cloud Deployments During ...

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Proceedings of the IEEE/ACM 16th International Conference on Utility and Cloud Computing

作者： Ghareeb Falazi Lukas Harzenetter Kálmán Képes Frank Leymann Uwe Breitenbücher Evangelos Ntentos Uwe Zdun Martin Becker Elena Heldwein Institute of Architecture of Application Systems University of Stuttgart Stuttgart Germany Reutlingen University Reutlingen Germany Research Group Software Architecture Faculty of Computer Science University of Vienna Vienna Austria IBM Deutschland Böblingen Germany

ISBN: (纸本)9798400702341

Modern cloud applications increasingly depend on Infrastructure-as-Code (IaC) practices for infrastructure automation to help manage the complexity of deploying large-scale architectures. Additionally, the deployment of cloud applications is commonly subject to compliance rules. Moreover, designing compliant IaC-based cloud deployments is not enough since runtime changes to the infrastructure or the configuration of individual components may introduce compliance violations. Often, the process of checking and fixing such violations is done manually, which is time-consuming and error-prone. Therefore, this work aims to define and implement a method for runtime IaC compliance management that reduces the complexity, effort, and uncertainty of checking and enforcing compliance rules against IaC-based cloud deployments at runtime. To this end, we follow the design-science research methodology to design and implement (i) the Runtime IaC Compliance Management (RICMa) method and (ii) the IaC Compliance Management Framework (IaCMF) that supports the execution of the RICMa method. We prototypically implement IaCMF and evaluate it using a qualitative interview study with industry experts.

关键词： infrastructure-as-code IaC IT compliance cloud compliance compliance management

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Data-Driven Model for Photovoltaic Generation: Comparison with Physical Models Using a Microgrid in Puerto Rico

Data-Driven Model for Photovoltaic Generation: Comparison wi...

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IEEE General Meeting Power& Energy Society

作者： Marcos R. Pesante Colón Alberto I. Cruz Salamán Dylan Cruz Figueroa Aditya Sundararajan Maximiliano Ferrari Electrical and Computer Engineering Department University of Puerto Rico Mayagüez USA Grid Systems Architecture Group Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Photovoltaic (PV) generation is a critical component of microgrids, but its accurate modeling is challenging due to the complex and dynamic interactions between solar irradiance, temperature, and PV system installation. This paper develops a multilayer perceptron (MLP) model that inputs solar irradiance and temperature to estimate the PV generation, and it compares the proposed data-driven model’s performance to two well-known physical models: the single-diode model and the inverter model. The results demonstrate that all the models can reach high levels of accuracy. However, the MLP model outperforms the physical models on average by 4.5 to 6.6 percent in R squared scores and 220 to 290 Watts in RMSE scores, and it does not require physical system parameters. Moreover, the data-driven model can overcome the limitations of the lack of real-time PV generation data.

关键词： Temperature measurement Solar irradiance Photovoltaic systems Adaptation models Accuracy Voltage measurement Predictive models Multilayer perceptrons Data models Inverters

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Detecting and Resolving Coupling-Related Infrastructure as Code Based architecture Smells in Microservice Deployments

Detecting and Resolving Coupling-Related Infrastructure as C...

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IEEE International Conference on Cloud Computing, CLOUD

作者： Evangelos Ntentos Uwe Zdun Ghareeb Falazi Uwe Breitenbücher Frank Leymann Software Architecture Group University of Vienna Vienna Austria University of Vienna Doctoral School Computer Science Vienna Austria Institute of Architecture of Application Systems University of Stuttgart Stuttgart Germany

The Infrastructure as Code (IaC) concept enables IT infrastructure to be managed as software: resources can be managed, monitored, and provisioned automatically instead of manually by developers or operations teams. Many industries have already embraced this concept widely. However, research on IaC-based deployments, particularly research focusing on loose coupling, often does not offer methods for evaluating architectural conformance, spotting architecture smells, and support for correcting the found smells. Our work strives to provide an automatic method for continuously developing microservice-based systems and the associated infrastructure. We aim to offer an automated architectural refactoring method that checks if IaCbased deployments adhere to patterns and best practices and do not contain potential architectural smells. We provide architects with viable options for enhancing architectural conformance during microservice development. In short, by continuously detecting architectural smells and suggesting possible fixes, we aim to support architecture evolution within the framework of continuous delivery practices. We evaluate our approach using three case studies and variants based on open-source microservice architectures.

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