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arXiv CS.AI·Jun 15·5 min read
FPGA-Based Neural Network Accelerators for Space Applications: A Survey
TL;DR
Space missions require advanced computing systems to handle complex tasks, but traditional hardware may not meet these demands. This survey explores the use of field-programmable gate arrays (FPGAs) as neural network (NN) accelerators for space applications.
✦ Why It Matters
Engineers can leverage FPGA-based NN accelerators to enhance the performance of onboard computing systems in space applications.
Key Takeaways
- Explore FPGA technology for space mission applications.
- Investigate radiation tolerance in FPGA designs.
- Focus on energy efficiency in NN implementations.
- Identify gaps in current research for future studies.
Full Summary
As space missions grow more complex, the need for high-performance onboard computing systems becomes critical. Field-programmable gate arrays (FPGAs) are emerging as a flexible and cost-effective solution, particularly for implementing neural networks (NNs) that can perform tasks like autonomous operations and data analysis.
This survey reviews existing literature on FPGA-based NN accelerators, analyzing their effectiveness in space applications. It identifies key trends, such as the increasing integration of NNs in spacecraft systems, and highlights gaps in current research, particularly in radiation tolerance and energy efficiency.
The authors propose future research directions to address these gaps, emphasizing the need for more robust and adaptable FPGA designs. The findings suggest that leveraging FPGAs for NN acceleration can significantly improve the computational capabilities of onboard systems, making them more suitable for demanding space missions.