While existing strategies for optimizing deep learning-based classification models on low-power platforms assume the models are trained on all classes of interest, this paper posits that adopting context-awareness i.e. focusing solely on the likely classes in the current context, can substantially enhance performance in resource-constrained environments. We propose a new paradigm, CACTUS, for scalable and efficient context-aware classification where a micro-classifier recognizes a small set of classes relevant to the current context and, when context change happens, rapidly switches to another suitable micro-classifier. CACTUS has several innovations including optimizing the training cost of context-aware classifiers, enabling on-the-fly context-aware switching between classifiers, and selecting the best context-aware classifiers given limited resources. We show that CACTUS achieves significant benefits in accuracy, latency, and compute budget across a range of datasets and IoT platforms.

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