Projects of Power Optimization in Rendering

Members: Rui Wang, Bowen Yu, Hujun Bao

Real-time Rendering on A Power Budget

Rui Wang1, Bowen Yu1, Julio Marco2, Tianlei Hu1, Diego Gutierrez2,3, Hujun Bao1
1State Key Lab of CAD&CG, Zhejiang University     2Universidad de Zaragoza     3I3A Institute

ACM Transactions on Graphics (TOG), 35(4), 11 pages, ACM SIGGRAPH 2016

Abstract:

With recent advances on mobile computing, power consumption has become a significant limiting constraint for many graphics applications. As a result, rendering on a power budget arises as an emerging demand. In this paper, we present a real-time, poweroptimal rendering framework to address this problem, by finding the optimal rendering settings that minimize power consumption while maximizing visual quality. We first introduce a novel powererror, multi-objective cost space, and formally formulate power saving as an optimization problem. Then, we develop a two-step algorithm to efficiently explore the vast power-error space and leverage optimal Pareto frontiers at runtime. Finally, we show that our rendering framework can be generalized across different platforms, desktop PC or mobile device, by demonstrating its performance on our own OpenGL rendering framework, as well as the commercially available Unreal Engine.

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Paper
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On-the-Fly Power-Aware Rendering

Yunjin Zhang1*, Marta Ortin2*, Victor Arellano2, Rui Wang1, Diego Gutierrez2, Hujun Bao1
1State Key Lab of CAD&CG, Zhejiang University     2Universidad de Zaragoza, I3A
*Joint first authors

Computer Graphics Forum(CGF), 34(7), p155-166, Eurographics Symposium on Rendering(EGSR) 2018

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.

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Paper
Supplemental Document
Supplemental Video