Energy Consumption Estimation in Embedded Systems

2008 ◽  
Vol 57 (4) ◽  
pp. 797-804 ◽  
Author(s):  
V. Konstantakos ◽  
A. Chatzigeorgiou ◽  
S. Nikolaidis ◽  
T. Laopoulos
Keyword(s):  
Energy Consumption ◽  
Embedded Systems

Impact of JVM superoperators on energy consumption in resource-constrained embedded systems

2008 ◽  
Vol 43 (7) ◽  
pp. 23-30 ◽  
Author(s):  
Carmen Badea ◽  
Alexandru Nicolau ◽  
Alexander V. Veidenbaum
Keyword(s):  
Energy Consumption ◽  
Embedded Systems ◽  
Resource Constrained

scholarly journals A Survey of Energy Consumption Measurement in Embedded Systems

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Chen Guo ◽  
Song Ci ◽  
Yanglin Zhou ◽  
Yang Yang
Keyword(s):  
Energy Consumption ◽  
Embedded Systems

scholarly journals Hybrid PSO-SA Type Algorithms for Multimodal Function Optimization and Reducing Energy Consumption in Embedded Systems

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Lhassane Idoumghar ◽  
Mahmoud Melkemi ◽  
René Schott ◽  
Maha Idrissi Aouad
Keyword(s):  
Energy Consumption ◽  
Embedded Systems ◽  
Evolutionary Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Time Saving ◽  
Local Optima ◽  
Hybrid Evolutionary Algorithm ◽  
Multimodal Function Optimization ◽  
Multimodal Function

The paper presents a novel hybrid evolutionary algorithm that combines Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms. When a local optimal solution is reached with PSO, all particles gather around it, and escaping from this local optima becomes difficult. To avoid premature convergence of PSO, we present a new hybrid evolutionary algorithm, called HPSO-SA, based on the idea that PSO ensures fast convergence, while SA brings the search out of local optima because of its strong local-search ability. The proposed HPSO-SA algorithm is validated on ten standard benchmark multimodal functions for which we obtained significant improvements. The results are compared with these obtained by existing hybrid PSO-SA algorithms. In this paper, we provide also two versions of HPSO-SA (sequential and distributed) for minimizing the energy consumption in embedded systems memories. The two versions, of HPSO-SA, reduce the energy consumption in memories from 76% up to 98% as compared to Tabu Search (TS). Moreover, the distributed version of HPSO-SA provides execution time saving of about 73% up to 84% on a cluster of 4 PCs.

Modelling Energy Consumption in Embedded Systems with VDM-RT

2014 ◽  
pp. 130-135
Author(s):  
José Antonio Esparza Isasa ◽  
Peter Würtz Vinther Jørgensen ◽  
Claus Ballegård Nielsen ◽  
Stefan Hallerstede
Keyword(s):  
Energy Consumption ◽  
Embedded Systems
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