提出MERINDA框架，让边缘设备高效运行物理AI模型恢复。
Model Recovery at the Edge under Resource Constraints for Physical AI
- 用可并行的神经结构替代迭代求解器，适配FPGA加速。
- 相比移动GPU，DRAM用量降11倍，运行速度提升2.2倍。
- 适合内存与能耗受限的实时关键系统部署。
模型恢复（MR）通过学习动态方程实现任务关键型自主系统（MCAS）的安全、可解释决策，但其在边缘设备上的部署受神经微分方程（NODEs）迭代特性限制，导致在FPGA上效率低下。内存和能耗是边缘设备实时运行时的主要瓶颈。本文提出MERINDA——一种新型FPGA加速的MR框架，以可并行的神经架构替代迭代求解器，等效于NODEs。实验表明，相较于移动GPU，MERINDA实现近11倍的DRAM使用降低和2.2倍的运行速度提升。在固定精度下，内存与能耗呈反比关系，凸显了MERINDA在资源受限、实时MCAS中的适用性。
原文摘要 · Abstract (English)
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 (NODEs), which are inefficient on FPGAs. Memory and energy consumption are the main concerns when applying MR on edge devices for real-time operation. We propose MERINDA, a novel FPGA-accelerated MR framework that replaces iterative solvers with a parallelizable neural architecture equivalent to NODEs. MERINDA achieves nearly 11x lower DRAM usage and 2.2x 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.
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