1,721,008 research outputs found
An OpenMP Parallel Genetic Algorithm for Design Space Exploration of Heterogeneous Multi-processor Embedded Systems
No full textCriticality-aware Design Space Exploration for Mixed-Criticality Embedded Systems
No full textThis work focuses on Design Space Exploration for embedded systems based on heterogeneous parallel architectures and subjected to mixed-criticality constraints. In particular, it presents a criticality-aware evolutionary approach integrated into a reference Electronic System Level HW/SW Co-Design flo
Design Space Exploration for Mixed-Criticality Embedded Systems Considering Hypervisor-Based SW Partitions
No full textThis work faces the role of Design Space Exploration for embedded systems based on heterogeneous parallel architectures and subject to mixed-criticality system requirements, while considering the exploitation of hypervisor-based SW partitions to better manage isolation. In particular, it presents an evolutionary partitioning and mapping approach integrated into a reference Electronic System Level HW/SW Co-Design framework to propose and early validate design solutions by means of HW/SW Co-Simulations
Leveraging synthetic trace generation of modeling operations for intelligent modeling assistants using large language models
No full textContext: Due to the proliferation of generative AI models in different software engineering tasks, the research community has started to exploit those models, spanning from requirement specification to code development. Model-Driven Engineering (MDE) is a paradigm that leverages software models as primary artifacts to automate tasks. In this respect, modelers have started to investigate the interplay between traditional MDE practices and Large Language Models (LLMs) to push automation. Although powerful, LLMs exhibit limitations that undermine the quality of generated modeling artifacts, e.g., hallucination or incorrect formatting. Recording modeling operations relies on human-based activities to train modeling assistants, helping modelers in their daily tasks. Nevertheless, those techniques require a huge amount of training data that cannot be available due to several factors, e.g., security or privacy issues. Objective: In this paper, we propose an extension of a conceptual MDE framework, called MASTER-LLM, that combines different MDE tools and paradigms to support industrial and academic practitioners. Method: MASTER-LLM comprises a modeling environment that acts as the active context in which a dedicated component records modeling operations. Then, model completion is enabled by the modeling assistant trained on past operations. Different LLMs are used to generate a new dataset of modeling events to speed up recording and data collection. Results: To evaluate the feasibility of MASTER-LLM in practice, we experiment with two modeling environments, i.e., CAEX and HEPSYCODE, employed in industrial use cases within European projects. We investigate how the examined LLMs can generate realistic modeling operations in different domains. Conclusion: We show that synthetic traces can be effectively used when the application domain is less complex, while complex scenarios require human-based operations or a mixed approach according to data availability. However, generative AI models must be assessed using proper methodologies to avoid security issues in industrial domains
A proposal to expand the community of users able to process historical rainfall data by means of the today available open source libraries
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An early-stage statement-level metric for energy characterization of embedded processors
Full text linkSystemC-based electronic system-level design space exploration environment for dedicated heterogeneous multi-processor systems
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A proposal to expand the community of users able to process historical rainfall data by means of the today available open source libraries
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