On‐the‐Fly Power‐Aware Rendering

Abstract

Power saving is a prevailing concern in desktop computers and, especially, in battery‐powered devices such as mobile phones. This is generating a growing demand for power‐aware graphics applications that can extend battery life, while preserving good quality. In this paper, we address this issue by presenting a real‐time power‐efficient rendering framework, able to dynamically select the rendering configuration with the best quality within a given power budget. Different from the current state of the art, our method does not require precomputation of the whole camera‐view space, nor Pareto curves to explore the vast power‐error space; as such, it can also handle dynamic scenes. Our algorithm is based on two key components: our novel power prediction model, and our runtime quality error estimation mechanism. These components allow us to search for the optimal rendering configuration at runtime, being transparent to the user. We demonstrate the performance of our framework on two different platforms: a desktop computer, and a mobile device. In both cases, we produce results close to the maximum quality, while achieving significant power savings.

Color-Perception-Guided Display Power Reduction for Virtual Reality

“…As such, energy-aware methods have been developed to minimize power while maintaining rendering quality. Most prior work in the graphics literature, however, focuses on reducing the rendering power [Debattista et al 2018;Wang et al 2016;Zhang et al 2018Zhang et al , 2021b.…”

A Practically Applicable Performance Prediction Model Based on Capabilities of Texture Mapping Units for Mobile GPUs

“…The first method involves the use of primitive information on a 3D graphics pipeline [5], the collection of batch, vertex, and fragment data by each rendering path [6], and the analysis of power consumption by 3D graphics components in each pipeline stage [7], [8]. The second method entails the use of information on vertex-processing and pixelprocessing loads [9], frequency scaling that accounts for user and application conditions [10], and a dynamic power predication scheme that accords with CPU and GPU usage [11].…”