System-level PMC-driven energy estimation models in RVC-CAL video codec specifications

In this paper, a platform-independent energy estimation methodology is proposed to estimate the energy consumption of 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 requirement on any specific detailed knowledge of the employed platform and, the latter, which trains the model using either a linear regression or a MARS method. Experimenting on an RVC-CAL decoder, the proposed PMC-driven model can achieve a maximum estimation error smaller than 10%. Furthermore, 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, the comparison between linear and MARS methods demonstrates the better predictive ability of piecewise modeling techniques in different scenarios. It is worth noting the attractiveness of this asset to analyze the energy consumption of RVC-CAL codec specifications. As a consequence, this methodology is suggested to be combined into the RVC framework to help the designer to have an overview of the energy consumption and energy-aware decoder reconfiguration.