ACM Computing Surveys· 2026Q1· Review
FPGA-Based Neural Network Accelerators for Space Missions: A Survey
- 0citations
- Q1SCImago
- 2026year
Short summary
This survey reviews FPGA-based neural network accelerators designed for space missions, highlighting their integration into next-generation spacecraft computing systems.
AI-generated from the title and abstract; the full text is not read.
Key points
- Space missions require increasingly powerful onboard computing.
- FPGAs offer reconfigurable and cost-effective solutions for space computing.
- Neural networks are vital for critical spacecraft functions.
- The survey reviews and classifies current FPGA-based NN accelerators for space applications.
- Key challenges and future research directions for integrating NN acceleration in spacecraft are identified.
AI-generated from the title and abstract; the full text is not read.
Abstract
Space missions are growing ever more ambitious, placing greater demands on onboard computing. Field-programmable gate arrays (FPGAs) have garnered interest due to their reconfigurability and cost-effectiveness. At the same time, neural network (NN)-based methods are proving invaluable for critical spacecraft tasks such as autonomous operations, remote sensing, selective downlink, and data compression. This survey reviews and classifies the state of the art in FPGA-based NN accelerators for space missions. We examine current trends, highlight key challenges, and suggest directions for future research. This work highlights key aspects for integrating high-performance NN acceleration into next-generation spacecraft computing systems.
The authors' abstract, as published at the source. ACM Computing Surveys, 2026 · DOI ↗
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Field: Hardware and Architecture
Hardware and ArchitectureComputer Science