Deploying Machine Learning Anomaly Detection Models to Flight Ready AI Boards

James Murphy, Maria Buckley, Leonie Buckley, Adam Taylor, Jake O'brien, Brian Mac Namee; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 6828-6836

This study explores the development and implementation of machine learning (ML) models on space-qualified AI boards aiming to identify the most effective solution for implementing anomaly detection systems on space missions. We investigate various ML anomaly detection techniques including Autoencoders Long Short-Term Memory (LSTM) cells Isolation Forests and Transformers. These models were trained on a univariate dataset derived from real space missions and deployed on hardware engineered for space environments. Our analysis extends to a diverse array of hardware platforms to comprehensively assess performance. Specifically we explore space flight ready boards (Ubotica CogniSAT-XE1 and XE2 which incorporate Intel's Myriad 2 and X chips respectively); commercial non-space flight ready edge-AI boards (NVIDIA's Jetson Nano and Google Coral); and Field Programmable Gate Array (FPGA) implementations (from Microchip AMD and NanoXplore) to provide a thorough comparison of available platforms for onboard anomaly detection. This paper therefore provides a detailed examination of both the optimal ML models and hardware platforms for deploying univariate anomaly detection systems in space flight contexts and draws conclusions about which ones are most suitable.