New paper accepted at the ACM/IEEE Design Automation Conference (DAC) 2024

A paper titled ``VARADE: a Variational-based AutoRegressive model for Anomaly Detection on the Edge’’, co-authored with Alessio Mascolini, Francesco Ponzio, Sebastiano Gaiardelli, Enrico Macii, Sara Vinco, Santa Di Cataldo, and Franco Fummi has been accepted at the ACM/IEEE Design Automation Conference (DAC) 2024, to be held at Moscone Center West in San Francisco, CA from the 23th to the 27th of June 2024.

Abstract: Detecting complex anomalies on massive amounts of data is a crucial task in Industry 4.0, best addressed by deep learning. However, available solutions are computationally demanding, requiring cloud architectures prone to latency and bandwidth issues. This work presents VARADE, a novel solution implementing a light autoregressive framework based on variational inference, which is best suited for real-time execution on the edge. The proposed approach was validated on a robotic arm, part of a pilot production line, and compared with several state-of-the-art algorithms, obtaining the best trade-off between anomaly detection accuracy, power consumption and inference frequency on two different edge platforms.