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Energy efficiency of multimedia processing is a hot topic in modern, mobile computing where the lifetime of battery-powered devices is low. Authors often use power models as tools to evaluate the energy-efficiency of multimedia workloads and processing schemes. A challenge with these models is that they are built without sufficiently deep hardware knowledge and as a result they have the potential to mispredict substantially depending on hardware configuration. Typical rate-based power models can for example mispredict up to 70 % on the Tegra K1 SoC. Inspired by multimedia workloads, we introduce a modelling methodology which can be used to build a generic, high-precision power model for the Tegra K1's GPU and memory. By considering hardware utilisation, rail voltages, leakage currents and clocks, the model achieves an average accuracy above 99 % over all operating frequencies, and has been rigorously tested on several multimedia workloads. Our method exposes detailed insight into hardware and how it consumes energy. This knowledge is not only useful for researchers to understand how power models should be built, but also helps to understand what developers can do to minimise power usage. For example, experiments show that for a DCT benchmark, 3 % power can be saved by utilising non-coherent caches and smaller datatypes.

A high-precision, hybrid GPU, CPU and RAM power model for generic multimedia workloads