Open AccessEnergy estimation models for video decoders: reconfigurable video coding‐CAL case‐study
Author(s) -
Ren Rong,
Juarez Eduardo,
Sanz Cesar,
Raulet Mickael,
Pescador Fernando
Publication year - 2015
Publication title -
iet computers and digital techniques
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.219
H-Index - 46
eISSN - 1751-861X
pISSN - 1751-8601
DOI - 10.1049/iet-cdt.2014.0087
Subject(s) - computer science , coding (social sciences) , estimation , real time computing , computer architecture , embedded system , engineering , systems engineering , mathematics , statistics
In this study, a platform‐independent energy estimation methodology is proposed to estimate the energy consumption of reconfigurable video coding (RVC)‐CAL video codec specifications. This methodology is based on the performance monitoring counters (PMCs) of embedded platforms and demonstrates its portability, simplicity and accuracy for on‐line estimation. It has two off‐line procedure stages: the former, which automatically identifies the most appropriate PMCs with no specific detailed knowledge of the employed platform, and the latter, which trains the model using either a linear regression or a multivariable adaptive regression splines (MARS) method. Experimenting on an RVC‐CAL decoder, the proposed PMC‐driven model can achieve an average estimation error <10%. In addition, the maximal model computation overhead is 4.04%. The results show that the training video sequence has significant influence on the model accuracy. An experimental metric is introduced to achieve more stable accurate models based on a combination of training sequences. Furthermore, a comparison demonstrates better predictive ability of MARS techniques in scenarios with multi‐core platforms. Finally, the experimental results show a good potential of energy efficiency improvement when the estimation model is combined into the RVC framework. In two different scenarios, the battery lifetime is increased 5.16% and 20.9%, respectively.