A Case for Application-Aware Space Radiation Tolerance in Orbital Computing
<|reference_start|>A Case for Application-Aware Space Radiation Tolerance in Orbital Computing: We are witnessing a surge in the use of commercial off-the-shelf (COTS) hardware for cost-effective in-orbit computing, such as deep neural network (DNN) based on-satellite sensor data processing, Earth object detection, and task decision.However, once exposed to harsh space environments, COTS hardware is vulnerable to cosmic radiation and suffers from exhaustive single-event upsets (SEUs) and multi-unit upsets (MCUs), both threatening the functionality and correctness of in-orbit computing.Existing hardware and system software protections against radiation are expensive for resource-constrained COTS nanosatellites and overwhelming for upper-layer applications due to their requirement for heavy resource redundancy and frequent reboots. Instead, we make a case for cost-effective space radiation tolerance using application domain knowledge. Our solution for the on-satellite DNN tasks, \name, exploits the uneven SEU/MCU sensitivity across DNN layers and MCUs' spatial correlation for lightweight radiation-tolerant in-orbit AI computing. Our extensive experiments using Chaohu-1 SAR satellite payloads and a hardware-in-the-loop, real data-driven space radiation emulator validate that RedNet can suppress the influence of radiation errors to $\approx$ 0 and accelerate the on-satellite DNN inference speed by 8.4%-33.0% at negligible extra costs.<|reference_end|>
@article{wang2024a,
    title={A Case for Application-Aware Space Radiation Tolerance in Orbital
  Computing},
    author={Meiqi Wang and Han Qiu and Longnv Xu and Di Wang and Yuanjie Li and
  Tianwei Zhang and Jun Liu and Hewu Li},
    journal={arXiv preprint arXiv:2407.11853},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11853},
    primaryClass={cs.ET}
}