Online Machine Learning for Energy-Aware Multicore Real-Time Embedded Systems

In this article, we present an Online Learning Artificial Neural Network (ANN) model that is able to predict the performance of tasks in lower frequency levels and safely optimize real-time embedded systems' power saving operations. The proposed ANN model is supported by feature selection, whic...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE transactions on computers Ročník 71; číslo 2; s. 493 - 505
Hlavní autori:	Hoffmann, Jose Luis Conradi, Frohlich, Antonio Augusto
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.02.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Artificial neural networks Bandwidth Computer architecture Embedded systems Energy management energy-aware systems Learning theory Machine learning Monitoring multi-core/single-chip multiprocessors Multicore processing Optimization Predictive models Real time Real-time and embedded systems Real-time systems Runtime scheduling and task partitioning Task analysis
ISSN:	0018-9340, 1557-9956
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!