发表机构
Wroclaw University of Science and Technology（弗罗茨瓦夫科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究将树深度剪枝实例难度方法应用于TinyML系统，通过阈值控制调整分类准确率，以在有限分类质量变化下改变推理能耗，为相关领域提供概念验证。
AI 中文摘要
TinyML指在内存和计算资源有限的设备上部署机器学习。随着技术发展，AI系统规模和计算需求不断扩大，迫使研究人员需设计降低AI模型推理计算成本与能耗的技术，以实现环境可持续性，即便在小型设备上亦是如此。本研究初步探索将树深度剪枝实例难度方法应用于TinyML系统的新场景，结果显示阈值控制可改变能耗，且分类质量变化有限；该方法能调整分类准确率，进而影响推理的计算复杂度与能耗，本研究为处于进展中的工作，其初始结果为概念验证。
英文摘要
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.
