论文标题
测量驱动的边缘辅助对象识别系统的分析
Measurement-driven Analysis of an Edge-Assisted Object Recognition System
论文作者
论文摘要
我们开发了一个边缘辅助对象识别系统，目的是研究端到端延迟和对象识别精度之间的系统级权衡。我们专注于开发优化系统传输延迟的技术，并演示图像编码速率和神经网络大小对这两个性能指标的影响。我们通过测量实时对象识别应用程序的性能来探索这些指标之间的最佳权衡。我们的测量结果揭示了迄今未知的参数效应和急剧的权衡，因此为优化这项关键服务铺平了道路。最后，我们使用基于测量的模型来提出两个优化问题，并在帕累托分析之后，我们发现对系统操作的仔细调整在实时条件下，与标准传输方法相比，对实时条件的性能至少提高了33％。
We develop an edge-assisted object recognition system with the aim of studying the system-level trade-offs between end-to-end latency and object recognition accuracy. We focus on developing techniques that optimize the transmission delay of the system and demonstrate the effect of image encoding rate and neural network size on these two performance metrics. We explore optimal trade-offs between these metrics by measuring the performance of our real time object recognition application. Our measurements reveal hitherto unknown parameter effects and sharp trade-offs, hence paving the road for optimizing this key service. Finally, we formulate two optimization problems using our measurement-based models and following a Pareto analysis we find that careful tuning of the system operation yields at least 33% better performance for real time conditions, over the standard transmission method.
