Speaker
Description
This paper presents a Satellite-as-a-Service (SaaS) architecture designed to enable flexible and efficient deployment of Machine Learning (ML) workloads on heterogeneous edge hardware platforms in space. Leveraging container-based virtualization (Docker) and an orchestration framework (Kubernetes), our approach abstracts hardware complexity and supports a variety of accelerators — FPGAs, TPUs, VPUs and NPUs — within a unified development and deployment environment. We integrate DevOps design principles delivering a reconfigurable stack that supports rapid ML model updates and deployment on target hardware. By treating satellites as extensible service platforms, we demonstrate how containerization and hardware abstraction streamline the onboarding of advanced ML algorithms, ranging from convolutional neural networks for image processing to neuromorphic paradigms for ultra-low-power inference. We detail how standardized APIs and modular workflows promote interoperability across multiple satellite systems and heterogeneous hardware accelerators.
