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AI Research
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
- Investigate FPGA technology for space mission applications.
- Utilize neural networks for autonomous spacecraft operations.
- Focus on radiation tolerance in hardware design.
- Explore gaps in current research for future innovations.
Full Summary
As space missions grow more complex, the need for advanced onboard computing systems has intensified. Field-programmable gate arrays (FPGAs) are gaining traction due to their adaptability, cost-effectiveness, and potential to withstand radiation, making them suitable for space environments.
Neural networks (NNs) are increasingly utilized for tasks like autonomous operations and data analysis in these missions. This survey compiles and analyzes current research on FPGA-based NN accelerators, highlighting their capabilities and the gaps in existing studies.
By examining trends and proposing future research avenues, the authors aim to guide researchers in developing more efficient onboard computing solutions for space applications. The findings underscore the significant potential of these accelerators to improve mission outcomes.