Computer Science / Machine Learning

Enabling Physical AI at the Edge: Hardware-Accelerated Recovery of System Dynamics

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📝 Original Info

  • Title: Enabling Physical AI at the Edge: Hardware-Accelerated Recovery of System Dynamics
  • ArXiv ID: 2512.23767
  • Date: 2025-12-29
  • Authors: Bin Xu, Ayan Banerjee, Sandeep Gupta

📝 Abstract

Physical AI at the edge-enabling autonomous systems to understand and predict real-world dynamics in realtime-demands efficient hardware acceleration. Model recovery (MR), which extracts governing equations from sensor data, is critical for safe and explainable monitoring in mission-critical autonomous systems (MCAS) operating under severe time, compute, and power constraints. While Field Programmable Gate Arrays (FPGAs) offer promising reconfigurable hardware for edge deployment, state-of-the-art (SOTA) MR methods like EMILY and PINN+SR rely on Neural ODEs requiring iterative solvers that resist hardware acceleration. This paper presents MERINDA (Model Recovery in Dynamic Architecture), an FPGA-accelerated framework specifically designed to enable physical AI at the edge. MERINDA replaces computationally expensive Neural Ordinary Differential Equation (ODE) components with a hardware-friendly architecture combining: (a) Gated Recurrent Unit (GRU) layers for discretized dynamics, (b) dense inverse ODE layers, (c) sparsity-driven dropout, and (d) lightweight ODE solvers-with critical components fully parallelized on FPGA. Evaluated on four benchmark nonlinear dynamical systems, MERINDA achieves transformative improvements over Graphic...

📄 Full Content

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