论文标题
火焰：一种用于异质移动处理器的自适应自动标记系统
FLAME: A Self-Adaptive Auto-labeling System for Heterogeneous Mobile Processors
论文作者
论文摘要
如何在移动设备上准确有效地标记数据对于在移动设备上培训机器学习模型的成功至关重要。移动设备上的自动标记数据具有挑战性，因为数据通常是逐渐生成的，并且有可能具有未知标签。此外，移动设备上丰富的硬件异质性在有效执行自动标记工作负载方面构成了挑战。在本文中，我们介绍了Flame，这是一种自动标记系统，可以标记具有未知标签的非平稳数据。火焰包括一个运行时系统，该系统有效地安排和执行异质移动处理器上的自动标记工作负载。用智能手机上的八个数据集评估火焰，我们证明火焰可以具有高标签精度和高性能的自动标记。
How to accurately and efficiently label data on a mobile device is critical for the success of training machine learning models on mobile devices. Auto-labeling data on mobile devices is challenging, because data is usually incrementally generated and there is possibility of having unknown labels. Furthermore, the rich hardware heterogeneity on mobile devices creates challenges on efficiently executing auto-labeling workloads. In this paper, we introduce Flame, an auto-labeling system that can label non-stationary data with unknown labels. Flame includes a runtime system that efficiently schedules and executes auto-labeling workloads on heterogeneous mobile processors. Evaluating Flame with eight datasets on a smartphone, we demonstrate that Flame enables auto-labeling with high labeling accuracy and high performance.
