Model Recovery at the Edge Under Resource Constraints for Physical AI

Model Recovery at the Edge Under Resource Constraints for Physical AI IOS Press 10/21/2025 10650903 10.3233/FAIA251275 Bin XuAyan BanerjeeSandeep KS Gupta 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.”

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