,
Vincenzo Stoico
,
Giacomo Valente
,
Marco Santic
,
Luigi Pomante
,
Daniele Frigioni
Creative Commons Attribution 4.0 International license
The rapidly increasing complexity of embedded systems and the critical impact of non-functional requirements demand the adoption of an appropriate system-level HW/SW co-design methodology. This methodology tries to satisfy all design requirements by simultaneously considering several alternative HW/SW implementations. In this context, early performance estimation approaches are crucial in reducing the design space, thereby minimizing design time and cost. To address the challenge of system-level performance estimation, this work presents and formalizes a novel approach based on a unifying HW/SW performance metric for early execution time estimation. The proposed approach estimates the execution time of a C function when executed by different HW/SW processor technologies. The approach is validated through an extensive experimental study, demonstrating its effectiveness and efficiency in terms of estimation error (i.e., lower than 10%) and estimation time (close to zero) when compared to existing methods in the literature.
@InProceedings{muttillo_et_al:OASIcs.PARMA-DITAM.2025.3,
author = {Muttillo, Vittoriano and Stoico, Vincenzo and Valente, Giacomo and Santic, Marco and Pomante, Luigi and Frigioni, Daniele},
title = {{System-Level Timing Performance Estimation Based on a Unifying HW/SW Performance Metric}},
booktitle = {16th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures and 14th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2025)},
pages = {3:1--3:14},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-363-8},
ISSN = {2190-6807},
year = {2025},
volume = {127},
editor = {Cattaneo, Daniele and Fazio, Maria and Kosmidis, Leonidas and Morabito, Gabriele},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.PARMA-DITAM.2025.3},
URN = {urn:nbn:de:0030-drops-229071},
doi = {10.4230/OASIcs.PARMA-DITAM.2025.3},
annote = {Keywords: embedded systems, hw/sw co-design, performance estimation, lasso, machine learning}
}
archived version