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    This study introduces a real-time power-budget rendering system for graphics applications. It optimizes rendering settings to maximize visual quality within a power budget, extending battery life on mobile devices.

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    Area of Science:

    • Computer Graphics
    • Mobile Computing
    • Energy Efficiency

    Background:

    • Embedded GPUs on mobile devices necessitate power-efficient rendering for graphics applications.
    • Reducing power consumption is crucial for extending battery life in mobile devices.

    Purpose of the Study:

    • To present a novel real-time power-budget rendering system.
    • To maximize visual quality for each frame under a given power budget.

    Main Methods:

    • Utilizes two independent neural networks trained on synthesized datasets.
    • Predicts power consumption and image quality for various workloads.
    • Avoids time-consuming precomputation, runtime refitting, and error computation.

    Main Results:

    • Evaluated on desktop PCs and smartphones.
    • Demonstrates less overhead and better flexibility compared to prior methods.
    • Achieves optimal rendering settings for power-constrained graphics.

    Conclusions:

    • The proposed system offers an efficient solution for power-aware rendering.
    • Integrable into existing rendering engines with minimal cost.
    • Contributes to enhanced performance and battery life in graphics applications.