APGPM: Automated PMC-Based Power Modeling Methodology for Modern Mobile GPUs

The rise of machine learning workload on smart-phones has propelled GPUs into one of the most power hungry components of modern smartphones. Optimizing the power consumption of mobile GPUs in turn requires accurate estimation of their power draw during app execution. We observe that the prior-art, utilization-frequency based GPU models cannot capture the diverse micro-architectural usage of modern mobile GPUs, and study whether performance monitoring counter (PMC)-based models recently proposed for desktop/server GPUs can be applied to accurately model mobile GPU power. Our study shows that the PMCs that come with dominating mobile GPUs used in modern smartphones are sufficient to model mobile GPU power, but exhibit multicollinearity if used altogether. We present APGPM, the first mobile GPU power modeling methodology that automatically selects an optimal set of PMCs that maximizes the GPU power model accuracy. Evaluation on the two representative mobile GPUs shows that APGPM-generated GPU power models reduce the MAPE modeling error of prior-art by 11.3% to 15.4% while using only 4.66% to 20.41 % of the total number of available PMCs.