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Model Recovery (MR) enables safe, explainable decision-making in mission-critical autonomous systems (MCAS) by learning governing dynamical equations, but its deployment on edge devices is hindered by the iterative nature of neural ordinary differential equations (NODE), which are inefficient on FPGAs. Memory and energy consumption are the main concern of applying MR on edge devices for real-time running MR. We propose MERINDA, a novel FPGA-accelerated MR framework that replaces iterative solvers with a parallelizable neural architecture equivalent to NODEs. MERINDA achieves nearly 11× lower DRAM usage and 2.2× faster runtime compared to mobile GPUs. Experiments reveal an inverse relationship between memory and energy at fixed accuracy, highlighting MERINDA’s suitability for resource-constrained, real-time MCAS. “The implementation and datasets are publicly available at github.com/ImpactLabASU/ECAI2025.” 

The paper presents AIIM, an Artificial Intelligence (AI) enabled personalIzation Management software for human-in-the-loop, human-in-the-plant Learning enabled systems (LES). AIIM can be integrated with LES software to aid a human user to achieve safe and effective operation under dynamically changing contexts. AIIM consists of: A) an AI technique to derive model coefficient of a physics guided surrogate model from operational data shared following privacy norms, and b) continuous model conformance to identify key changes in LES operational behavior that may jeopardize safety. We demonstrate two capabilities of AIIM, personalization and unknown error detection, through case studies that span a significant breadth of dynamic context change scenarios including: a) involuntary change in user context such as medication induced glucose metabolism change in automated insulin delivery (AID), b) actuation failure such as cartridge blockage in AID, c) latent sensor error in aviation, and d) unknown coding error in autonomous car software patches. We compare AIIM personalization with human-in-the-loop and self-adaptive model-predictive control design on real-life and simulation settings, to show safe and improved diabetes management.