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arXiv · 2211.09897

Efficient Feature Compression for Edge-Cloud Systems

Zhihao Duan·Fengqing Zhu
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Abstract

Optimizing computation in an edge-cloud system is an important yet challenging problem. In this paper, we consider a three-way trade-off between bit rate, classification accuracy, and encoding complexity in an edge-cloud image classification system. Our method includes a new training strategy and an efficient encoder architecture to improve the rate-accuracy performance. Our design can also be easily scaled according to different computation resources on the edge device, taking a step towards achieving a rate-accuracy-complexity (RAC) trade-off. Under various settings, our feature coding system consistently outperforms previous methods in terms of the RAC performance.

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Zhihao Duan, Fengqing Zhu. 2022-11-17. Efficient Feature Compression for Edge-Cloud Systems. https://arxiv.org/abs/2211.09897

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