  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.
