VAP: The Vulnerability-Adaptive Protection Paradigm Toward Reliable Autonomous Machines

TL;DR

This paper introduces VAP, a protection paradigm that adapts security measures based on the inherent robustness of different software layers in autonomous machines, improving reliability with lower costs.

Contribution

It reveals robustness variations across software layers and proposes a vulnerability-adaptive protection scheme to optimize reliability and efficiency.

Findings

VAP achieves high protection coverage with low overhead.

Robustness varies across software layers, with front-end being more resilient.

VAP reduces performance and energy costs compared to traditional methods.

Abstract

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.