mlsanityData to 14 Oct 2026

FPGA-Based Neural Network Accelerators for Space Applications: A Survey

arXiv 2504.16173 ↗First posted 22 Apr 2025cs.AR cs.AIcited by 2

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Abstract (arXiv)

Space missions are becoming increasingly ambitious, necessitating high-performance onboard spacecraft computing systems. In response, field-programmable gate arrays (FPGAs) have garnered significant interest due to their flexibility, cost-effectiveness, and radiation tolerance potential. Concurrently, neural networks (NNs) are being recognized for their capability to execute space mission tasks such as autonomous operations, sensor data analysis, and data compression. This survey serves as a valuable resource for researchers aiming to implement FPGA-based NN accelerators in space applications. By analyzing existing literature, identifying trends and gaps, and proposing future research directions, this work highlights the potential of these accelerators to enhance onboard computing systems.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 45 of its 216 references that are in the map. First posted on arXiv 22 Apr 2025.

Builds on

45 of 216 references.

References · 45

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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Fluxamba: Topology-Aware Anisotropic State Space Models for Geological Lineament Segmentation in Multi-Source Remote Sensingcited by 0
  2. 2026Evaluating Four FPGA-accelerated Space Use Cases based on Neural Network Algorithms for On-board Inferencecited by 0