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On the Instance Hardness as a Decision Criterion in TinyML Systems

arXiv · AI, language, vision and robotics · article · Aug 30, 2026 · UTC

TinyML includes the implementation of machine learning on devices with limited memory and computing resources. With the development of technology, AI systems continue to scale in terms of size and computational requirements. This forces researchers to adapt methods to be environmentally sustainable by designing techniques for reducing computational costs and energy consumption in inferring AI models, even in small devices. In this work, we present preliminary findings on a novel application of the tree depth prune instance hardness method to the TinyML system. The results indicate that thresho

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