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TinyOOD: Effective out-of-Distribution Detection for TinyML by Yongchang Li; Juncheng Jia; Yan Zuo; Weipeng Zhu is a scholarly article available to read on EtoBox.

What is TinyOOD: Effective out-of-Distribution Detection for TinyML about?

Tiny machine learning (TinyML) has emerged recently for resource constrained Internet of Things (IoT) devices. However, the deployed TinyML model cannot handle outof-distribution (OOD) inputs appropriately. While many high-accuracy OOD detection methods have emerged, they often ignore the limitations of the deployment environment. In this paper, we propose a novel effective out-of-distribution detection method for TinyML (TinyOOD), which exploits cascading early exit and channel-attention-based neural mean discrepancy (CA-NMD) for dynamic and efficient OOD detection on microcontroller units (MCUs). To demonstrate its effectiveness, we extensively evaluate TinyOOD using four public datasets, one as the in-distribution (ID) dataset and the others as the OOD datasets. Experiments demonstrate that TinyOOD significantly reduces the computations by up to 38.23% in inference while maintaining the performance of OOD detection.

Author

Yongchang Li; Juncheng Jia; Yan Zuo; Weipeng Zhu

Publisher

IEEE

Published

2023

Language

EN