论文标题
边缘云系统有效的功能压缩
Efficient Feature Compression for Edge-Cloud Systems
论文作者
论文摘要
在边缘云系统中优化计算是一个重要但具有挑战性的问题。在本文中，我们考虑了比特率，分类准确性和编码Edge-Cloud图像分类系统中的复杂性之间的三路权衡。我们的方法包括一种新的培训策略和有效的编码器体系结构，以提高利率准确性的性能。根据边缘设备上的不同计算资源，我们的设计也很容易缩放，迈出了一步，以实现速率准确的复杂性（RAC）权衡。在各种设置下，我们的功能编码系统在RAC性能方面始终优于以前的方法。
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.
