Anomaly Detection on the Edge: Comparison of Reconstruction and Classification Based Approaches
Łukasz Grzymkowski, Tymoteusz Cejrowski, Tomasz P. Stefański · 2025
In this work we discuss and evaluate different approaches to solving anomaly detection task when the target platform is a tiny microcontroller. We investigate modeling techniques and propose a comprehensive set of measurements to analyze performance, compute and memory requirements, and power efficiency. We run experiments to collect these measurements on platforms used in TinyML systems including Cortex-M7, Cortex-M55 and Ethos-U55 running TensorFlow Lite for Microcontrollers. The measurements are collected for an autoencoder in reconstruction-based anomaly detection and a MobileNetV2-like model trained for classification. We show which approach is more suitable depending on the system requirements and constraints. This work underscores the need for a holistic approach in selecting modeling and deployment strategies, providing empirical evidence to guide the development of efficient on-device anomaly detection systems.