Selective abstraction and stochastic methods for scalable power modelling of heterogeneous systems
Ashur Rafiev, Fei Xia, Alexei Iliasov, Rem Gensh, Ali Aalsaud, Alexander B. Romanovsky, Alex Yakovlev · 2016
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, this paper studies 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.