10.17634/123238-3
A Rafiev
A
Rafiev
F Xia
F
Xia
R Gensh
R
Gensh
A Aalsaud
A
Aalsaud
A Romanovsky
A
Romanovsky
A Yakovlev
A
Yakovlev
Models and simulation data supporting the method of Selective Abstraction
Newcastle University
2017
Selective abstraction
Error contamination
Model complexity
Stochastic Activity Networks
SANs
Order Graphs
Mobius tool
Simulation
Odroid XU3
big.LITTLE
2017-01-01 00:00:00
Dataset
https://data.ncl.ac.uk/articles/dataset/Models_and_simulation_data_supporting_the_method_of_Selective_Abstraction/10280870
With the increase of system complexity in both platforms and applications, power modelling of heterogeneous systems is facing grand challenges from the model scalability issue. To address these challenges, our work uses two systematic methods: selective abstraction and stochastic techniques. The concept of selective abstraction via black-boxing is realised using hierarchical modelling and cross-layer cuts, respecting the concepts of boxability and error contamination. The stochastic aspect is formally underpinned by Stochastic Activity Networks (SANs). The proposed method is validated with experimental results from Odroid XU3 heterogeneous 8-core platform and is demonstrated to maintain high accuracy while improving scalability.