Memory-aware optimization of FPGA-based space systems

Designing FPGA-based space systems that meet mission goals of high performance, low power, and high dependability is challenging. Our previous work established a framework to help designers of FPGA-based space systems to consider a wide range of designs, evaluate the power and dependability of those designs, and narrow the large design search space down to a significantly reduced Pareto-optimal set. To further improve and extend our framework’s ability to evaluate and optimize increasingly complex aerospace systems, this paper details our framework’s memory extension, which enables memory-aware analysis by refinements to our framework’s original analysis.

The memory-aware analysis more accurately predicts a system’s power and dependability by modeling three memory resources: internal-memory capacity, internal-memory bandwidth, and external-memory bandwidth. We demonstrate the importance of our framework’s memory extension by investigating a case study based on an enhanced version of a hyperspectral-imaging┬ásatellite mission. After analyzing 22 unique Virtex FPGA devices and optimizing each for power and then dependability, the framework selects four Pareto-optimal designs, ranging from very-low power to high dependability. Results of the framework’s memory extension show that memory resources may limit the performance of an FPGA-based space-system design and contribute significantly towards power and dependability results.