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.

翻译：尽管当前针对低功耗平台优化深度学习分类模型的策略假设模型在所有关注类别上进行了训练，本文提出采纳上下文感知（即仅关注当前上下文中可能出现的类别）能显著提升资源受限环境下的性能。我们提出了一种名为CACTUS的可扩展高效上下文感知分类新范式：其中微型分类器识别与当前上下文相关的小规模类别集合，并在上下文发生变更时快速切换至其他合适的微型分类器。CACTUS包含多项创新：优化上下文感知分类器的训练成本、实现分类器间的即时上下文感知切换，以及在资源约束条件下选择最优上下文感知分类器。实验表明，CACTUS在多个数据集和物联网平台上均能显著提升准确率、降低延迟并优化计算预算。