Palleon: A Runtime System for Efficient Video Processing toward Dynamic Class Skew

Authors: 

Boyuan Feng, Yuke Wang, Gushu Li, Yuan Xie, and Yufei Ding, University of California, Santa Barbara

Abstract: 

On par with the human classification accuracy, convolutional neural networks (CNNs) have fueled the deployment of many video processing systems on cloud-backed mobile platforms (e.g., cell phones and robotics). Nevertheless, these video processing systems often face a tension between intensive energy consumption from CNNs and limited resources on mobile platforms. To address this tension, we propose to accelerate video processing with a widely-available, but not yet well-explored runtime input-level information, namely class skew. Through such runtime-profiled information, it strives to automatically optimize CNNs toward the time-varying video stream. Specifically, we build Palleon, a runtime system that dynamically adapts and selects a CNN model with the least energy consumption based on the automatically detected class skews, while still achieving the desired accuracy. Extensive evaluations on state-of-the-art CNNs and real-world videos demonstrate that Palleon enables efficient video processing with up to 6.7x energy saving and 7.9x latency reduction.

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BibTeX
@inproceedings {273761,
author = {Boyuan Feng and Yuke Wang and Gushu Li and Yuan Xie and Yufei Ding},
title = {Palleon: A Runtime System for Efficient Video Processing toward Dynamic Class Skew},
booktitle = {2021 {USENIX} Annual Technical Conference ({USENIX} {ATC} 21)},
year = {2021},
isbn = {978-1-939133-23-6},
pages = {427--441},
url = {https://www.usenix.org/conference/atc21/presentation/feng-boyuan},
publisher = {{USENIX} Association},
month = jul,
}

Presentation Video