VAP: The Vulnerability-Adaptive Protection Paradigm Toward Reliable Autonomous Machines
<|reference_start|>VAP: The Vulnerability-Adaptive Protection Paradigm Toward Reliable Autonomous Machines: The next ubiquitous computing platform, following personal computers and smartphones, is poised to be inherently autonomous, encompassing technologies like drones, robots, and self-driving cars. Ensuring reliability for these autonomous machines is critical. However, current resiliency solutions make fundamental trade-offs between reliability and cost, resulting in significant overhead in performance, energy consumption, and chip area. This is due to the "one-size-fits-all" approach commonly used, where the same protection scheme is applied throughout the entire software computing stack.   This paper presents the key insight that to achieve high protection coverage with minimal cost, we must leverage the inherent variations in robustness across different layers of the autonomous machine software stack. Specifically, we demonstrate that various nodes in this complex stack exhibit different levels of robustness against hardware faults. Our findings reveal that the front-end of an autonomous machine's software stack tends to be more robust, whereas the back-end is generally more vulnerable. Building on these inherent robustness differences, we propose a Vulnerability-Adaptive Protection (VAP) design paradigm. In this paradigm, the allocation of protection resources - whether spatially (e.g., through modular redundancy) or temporally (e.g., via re-execution) - is made inversely proportional to the inherent robustness of tasks or algorithms within the autonomous machine system. Experimental results show that VAP provides high protection coverage while maintaining low overhead in both autonomous vehicle and drone systems.<|reference_end|>
@article{wan2024vap:,
    title={VAP: The Vulnerability-Adaptive Protection Paradigm Toward Reliable
  Autonomous Machines},
    author={Zishen Wan, Yiming Gan, Bo Yu, Shaoshan Liu, Arijit Raychowdhury,
  Yuhao Zhu},
    journal={arXiv preprint arXiv:2409.19892},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19892},
    primaryClass={cs.RO}
}