FPGA-Accelerated Model Recovery Framework Reduces Energy Consumption and Memory Footprint for Edge AI

Global: FPGA-Accelerated Model Recovery Framework Reduces Energy Consumption and Memory Footprint for Edge AI

Researchers have introduced a hardware‑efficient model recovery framework designed for resource‑constrained edge devices. The system, named MERINDA (Model Recovery in Reconfigurable Dynamic Architecture), was described in a preprint posted to arXiv in December 2025. Its primary goal is to enable autonomous systems to infer governing equations from sensor data in real time while meeting strict latency, compute, and power limits.

Background

Model recovery (MR) is a key technique for creating explainable and safe monitoring solutions in mission‑critical applications. Existing state‑of‑the‑art MR methods, such as EMILY and PINN+SR, rely on Neural ODE formulations that require iterative solvers. Those solvers are computationally intensive and difficult to accelerate on edge hardware, often leading to high energy use and large memory demands.

MERINDA Architecture

The MERINDA framework replaces the costly Neural ODE components with a hardware‑friendly pipeline. It combines a gated‑recurrent‑unit (GRU) based discretized dynamics model, dense inverse‑ODE layers, sparsity‑driven dropout, and lightweight ODE solvers. This composition allows the computation to be expressed as a series of streaming kernels that map naturally onto FPGA resources.

Hardware Implementation

By targeting reconfigurable logic, MERINDA structures its critical kernels for parallel execution, enabling full pipelining of data streams on the FPGA. The design leverages on‑chip memory and avoids off‑chip transfers, which contributes to the reported reductions in power consumption.

Performance Evaluation

Across four benchmark nonlinear dynamical systems, MERINDA achieved 114 times lower energy usage (434 J versus 49,375 J on a GPU), a 28 times smaller memory footprint (214 MB versus 6,118 MB), and a 1.68 times faster training time, while preserving the accuracy levels reported by leading MR approaches.

Implications for Edge AI

The results suggest that accurate, explainable model recovery can be deployed on devices with limited resources, expanding the feasibility of real‑time physical AI for autonomous vehicles, drones, and industrial monitoring systems. By reducing both energy and memory requirements, MERINDA may help extend battery life and lower hardware costs in such deployments.

Future Directions

The authors note that further optimization of the lightweight ODE solvers and exploration of additional benchmark domains could broaden the applicability of the framework. Integration with emerging low‑power sensors and validation on real‑world autonomous platforms are identified as next steps.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.