通过调整阈值，在不显著影响准确率的前提下，灵活控制微型设备的能耗。
On the Instance Hardness as a Decision Criterion in TinyML Systems
- 利用树深度剪枝与实例难度结合的方法，动态调节推理复杂度。
- 实验显示改变阈值可显著降低能耗，准确率波动小于5%。
- 适合关注边缘设备能效优化的研究者和嵌入式AI开发者。
TinyML致力于在内存和算力受限的设备上部署机器学习模型。随着人工智能系统规模与计算需求持续增长，研究者需设计节能方法以提升环境可持续性，降低小设备上的推理成本。本文初步探索将树深度剪枝结合实例难度判定的方法应用于TinyML系统。结果表明，通过调节阈值可在保持分类质量基本不变（准确率变化小于5%）的前提下，显著改变能量消耗。该方法使分类准确率、计算复杂度与能耗之间实现可调平衡，为资源受限场景下的智能推理提供新思路。目前工作尚处初期，结果仅为概念验证。
原文摘要 · Abstract (English)
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 threshold control can change energy consumption with limited classification quality changes. This method allows us to adjust classification accuracy, thereby influencing computational complexity and energy consumption for inference. We present a work in progress with initial results as a proof of concept.
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