Research Paper#Edge AI, FPGA, Model Recovery, Autonomous Systems🔬 ResearchAnalyzed: Jan 3, 2026 16:11
FPGA-Accelerated Model Recovery for Edge AI
Published:Dec 29, 2025 04:51
•1 min read
•ArXiv
Analysis
This paper addresses the challenge of enabling physical AI on resource-constrained edge devices. It introduces MERINDA, an FPGA-accelerated framework for Model Recovery (MR), a crucial component for autonomous systems. The key contribution is a hardware-friendly formulation that replaces computationally expensive Neural ODEs with a design optimized for streaming parallelism on FPGAs. This approach leads to significant improvements in energy efficiency, memory footprint, and training speed compared to GPU implementations, while maintaining accuracy. This is significant because it makes real-time monitoring of autonomous systems more practical on edge devices.
Key Takeaways
- •MERINDA is an FPGA-accelerated framework for Model Recovery (MR).
- •It replaces computationally expensive Neural ODEs with a hardware-friendly formulation.
- •MERINDA achieves significant improvements in energy efficiency, memory footprint, and training speed compared to GPU implementations.
- •The framework is designed for real-time monitoring of autonomous systems on edge devices.
Reference
“MERINDA delivers substantial gains over GPU implementations: 114x lower energy, 28x smaller memory footprint, and 1.68x faster training, while matching state-of-the-art model-recovery accuracy.”