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How to accurately and efficiently label data on a mobile device is criticalfor the success of training machine learning models on mobile devices.Auto-labeling data on mobile devices is challenging, because data is usuallyincrementally generated and there is possibility of having unknown labels.Furthermore, the rich hardware heterogeneity on mobile devices createschallenges on efficiently executing auto-labeling workloads. In this paper, weintroduce Flame, an auto-labeling system that can label non-stationary datawith unknown labels. Flame includes a runtime system that efficiently schedulesand executes auto-labeling workloads on heterogeneous mobile processors.Evaluating Flame with eight datasets on a smartphone, we demonstrate that Flameenables auto-labeling with high labeling accuracy and high performance.

FLAME: A Self-Adaptive Auto-labeling System for Heterogeneous Mobile Processors