Monitoring the performance of multicore embedded systems without disrupting its timing requirements
Author:
Funder
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Publisher
Springer Science and Business Media LLC
Subject
Hardware and Architecture,Software
Link
https://link.springer.com/content/pdf/10.1007/s10617-023-09278-4.pdf
Reference24 articles.
1. Chen W, Izhbirdeev I, Hoornaert D, et al (2023) Low-overhead online assessment of timely progress as a system commodity. In: Papadopoulos AV (ed) 35th Euromicro conference on real-time systems (ECRTS 2023), Leibniz international proceedings in informatics (LIPIcs), vol 262. Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Dagstuhl, Germany, pp 13:1–13:26, https://doi.org/10.4230/LIPIcs.ECRTS.2023.13, https://drops.dagstuhl.de/opus/volltexte/2023/18042
2. Conradi Hoffmann JL, Frohlich AA (2021) Online machine learning for energy-aware multicore real-time embedded systems. IEEE Trans Comput. https://doi.org/10.1109/TC.2021.3056070
3. Das A, Al-Hashimi BM, Merrett GV (2016) Adaptive and hierarchical runtime manager for energy-aware thermal management of embedded systems. ACM Trans Embed Comput Syst. https://doi.org/10.1145/2834120
4. Donyanavard B, Mück T, Sarma S, et al (2016) Sparta: Runtime task allocation for energy efficient heterogeneous many-cores. In: Proceedings of the eleventh IEEE/ACM/IFIP international conference on hardware/software codesign and system synthesis. Association for computing machinery, New York, NY, USA, CODES ’16, https://doi.org/10.1145/2968456.2968459, https://doi.org/10.1145/2968456.2968459
5. Dutt N, Jantsch A, Sarma S (2016) Toward smart embedded systems: a self-aware system-on-chip (soc) perspective. ACM Trans Embed Comput Syst. https://doi.org/10.1145/2872936
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