The Vulnerability-Adaptive Protection Paradigm

We present a comprehensive review of the design landscape for resilient autonomous machines. We show that existing techniques are of a "one-size-fits-all" nature, where the same protection scheme is applied to the entire software stack, leading to either high overhead or low protection strength. We provide a thorough characterization of the inherent resilience of different tasks in widely used, open source software stacks for autonomous vehicles (AutoWare) and drones (MAVBench). We show that different tasks vary significantly in their resilience under hardware faults. In particular, front-end machine vision tasks that operate on massive visual data are much more resilient to faults than back-end tasks, such as planning and control, which operate on smaller data but are more sensitive to faults. We propose VAP for resilient autonomous machines. In VAP, we spend less protection efforts on front-end machine-vision tasks and more budget on back-end planning and control tasks. Experimentally, we show that the VAP mechanism provides high protection coverage while maintaining low protection overhead on both autonomous vehicle and drone systems.