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.

在资源受限环境中，采用上下文感知的策略，即专注于当前上下文中可能的类别，可以显著提高基于深度学习的分类模型在低功耗平台上的性能。本文提出了一种名为 CACTUS 的新范式，用于可扩展和高效的上下文感知分类，通过微分类器识别与当前上下文相关的一小组类别，在上下文变化时迅速切换到另一个合适的微分类器，实现了上下文感知分类器的训练成本优化、在运行时动态切换分类器，并在资源有限的情况下选择最佳的上下文感知分类器。我们展示了 CACTUS 在一系列数据集和物联网平台上在准确性、延迟和计算资源方面带来的显著优势。