Energy estimation models for video decoders: reconfigurable video coding-CAL case-study

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.