Model Recovery at the Edge under Resource Constraints for Physical AI

• URL: http://arxiv.org/abs/2512.02283v1
• Date: Mon, 01 Dec 2025 23:54:23 GMT
• Title: Model Recovery at the Edge under Resource Constraints for Physical AI
• Authors: Bin Xu, Ayan Banerjee, Sandeep K. S. Gupta,
• Abstract summary: We propose a novel FPGA-accelerated Model Recovery framework that replaces iterative solvers with a parallelizable neural architecture equivalent to NODEs.<n> MERINDA achieves nearly 11x lower DRAM usage and 2.2x faster runtime compared to mobile GPUs.
• Score: 4.415937510184061
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• PhyG-MoE: A Physics-Guided Mixture-of-Experts Framework for Energy-Efficient GNSS Interference Recognition [49.955269674859004]
  This paper introduces PhyG-MoE (Physics-Guided Mixture-of-Experts), a framework designed to align model capacity with signal complexity.<n>Unlike static architectures, the proposed system employs a spectrum-based gating mechanism that routes signals based on their spectral feature entanglement.<n>A high-capacity TransNeXt expert is activated on-demand to disentangle complex features in saturated scenarios, while lightweight experts handle fundamental signals to minimize latency.
  arXiv  Detail & Related papers  (2026-01-19T07:57:52Z)
• RMAAT: Astrocyte-Inspired Memory Compression and Replay for Efficient Long-Context Transformers [11.099872871193028]
  This work explores computational principles derived from astrocytes-glial cells critical for biological memory and synaptic modulation.<n>We introduce the Recurrent Memory Augmented Transformer (RMAAT), an architecture integrating astrocyte functionalities.
  arXiv  Detail & Related papers  (2026-01-01T18:34:06Z)
• Enabling Physical AI at the Edge: Hardware-Accelerated Recovery of System Dynamics [4.058950730052848]
  textbfMERINDA (Model Recovery in Reconfigurable Dynamic Architecture) is an FPGA-accelerated MR framework designed to make physical AI practical on resource-constrained devices.<n>We show that MERINDA can bring accurate, explainable MR to the edge for real-time monitoring of autonomous systems.
  arXiv  Detail & Related papers  (2025-12-29T04:51:51Z)
• Hardware Software Optimizations for Fast Model Recovery on Reconfigurable Architectures [4.058950730052848]
  We present MERINDA, an FPGA-accelerated MR framework that restructures computation as a streaming dataflow pipeline.<n>On representative MR workloads, MERINDA delivers up to 6.3x fewer cycles than an FPGA-based LTC baseline.
  arXiv  Detail & Related papers  (2025-12-05T19:38:34Z)
• Merging Memory and Space: A State Space Neural Operator [8.378604588491394]
  State Space Neural Operator (SS-NO) is a compact architecture for learning solution operators of time-dependent partial differential equations.<n>We show that SS-NO achieves state-of-the-art performance across diverse PDE benchmarks.
  arXiv  Detail & Related papers  (2025-07-31T11:09:15Z)
• The Larger the Merrier? Efficient Large AI Model Inference in Wireless Edge Networks [56.37880529653111]
  The demand for large computation model (LAIM) services is driving a paradigm shift from traditional cloud-based inference to edge-based inference for low-latency, privacy-preserving applications.<n>In this paper, we investigate the LAIM-inference scheme, where a pre-trained LAIM is pruned and partitioned into on-device and on-server sub-models for deployment.
  arXiv  Detail & Related papers  (2025-05-14T08:18:55Z)
• RadioDiff-$k^2$: Helmholtz Equation Informed Generative Diffusion Model for Multi-Path Aware Radio Map Construction [76.24833675757033]
  We propose a physics-informed generative learning approach, named RadioDiff-$k2$, for accurate and efficient multipath-aware radio map (RM) construction.<n>We show that the proposed RadioDiff-$k2$ framework achieves state-of-the-art (SOTA) performance in both image-level RM construction and localization tasks.
  arXiv  Detail & Related papers  (2025-04-22T06:28:13Z)
• ReCoM: Realistic Co-Speech Motion Generation with Recurrent Embedded Transformer [58.49950218437718]
  We present ReCoM, an efficient framework for generating high-fidelity and generalizable human body motions synchronized with speech.<n>The core innovation lies in the Recurrent Embedded Transformer (RET), which integrates Dynamic Embedding Regularization (DER) into a Vision Transformer (ViT) core architecture.<n>To enhance model robustness, we incorporate the proposed DER strategy, which equips the model with dual capabilities of noise resistance and cross-domain generalization.
  arXiv  Detail & Related papers  (2025-03-27T16:39:40Z)
• A Remedy to Compute-in-Memory with Dynamic Random Access Memory: 1FeFET-1C Technology for Neuro-Symbolic AI [14.486320458474536]
  Neuro-symbolic artificial intelligence (AI) excels at learning from noisy and generalized patterns, conducting logical inferences, and providing interpretable reasoning. Current hardware struggles to accommodate applications requiring dynamic resource allocation between 'neuro' and'symbolic' components. We propose a ferroelectric charge-domain compute-in-memory (CiM) array as the foundational processing element for neuro-symbolic AI.
  arXiv  Detail & Related papers  (2024-10-20T05:52:03Z)
• Frequency-Assisted Mamba for Remote Sensing Image Super-Resolution [49.902047563260496]
  We develop the first attempt to integrate the Vision State Space Model (Mamba) for remote sensing image (RSI) super-resolution. To achieve better SR reconstruction, building upon Mamba, we devise a Frequency-assisted Mamba framework, dubbed FMSR. Our FMSR features a multi-level fusion architecture equipped with the Frequency Selection Module (FSM), Vision State Space Module (VSSM), and Hybrid Gate Module (HGM)
  arXiv  Detail & Related papers  (2024-05-08T11:09:24Z)
• Resistive Memory-based Neural Differential Equation Solver for Score-based Diffusion Model [55.116403765330084]
  Current AIGC methods, such as score-based diffusion, are still deficient in terms of rapidity and efficiency. We propose a time-continuous and analog in-memory neural differential equation solver for score-based diffusion. We experimentally validate our solution with 180 nm resistive memory in-memory computing macros.
  arXiv  Detail & Related papers  (2024-04-08T16:34:35Z)
• Full-Stack Optimization for CAM-Only DNN Inference [2.0837295518447934]
  This paper explores the combination of algorithmic optimizations for ternary weight neural networks and associative processors. We propose a novel compilation flow to optimize convolutions on APs by reducing their arithmetic intensity. Our solution improves the energy efficiency of ResNet-18 inference on ImageNet by 7.5x compared to crossbar in-memory accelerators.
  arXiv  Detail & Related papers  (2024-01-23T10:27:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.