Presentation
Swift-Healer: Firmware-Reconfigurable Self-Healing for Remote Glitch-Injection on Autonomous Driving Systems
DescriptionAutonomous Navigation Systems (ANS) incorporate many safety-critical functions, such as collision avoidance. Recent studies have shown how remote clock/voltage glitch injections pose an imminent threat to mission-sensitive modules in the autonomous navigation domain: timing/power perturbations in the perception stages can cascade into severe accuracy loss and latency drift for downstream tasks. In this paper, we present Swift-Healer, a firmware-reconfigurable self-healing architecture that unifies prediction-detection modules and an automated healing unit to mitigate remote clock/voltage glitches, while satisfying the application latency constraints. Our solution leverages a chiplet-based architecture that offers isolation from compromised hardware modules, while enabling self-healing in the firmware management layer. Our proposed design incorporates an autonomous monitor that is a combination of a glitch predictor and a reactive detector. We implement our design on a Zynq–7000 with a hardware accelerator, where Swift-Healer predicts glitches within autonomous driving kernels up to two real–time loop iterations earlier ≈ 0.06𝑚𝑠, thereby giving abundant time for self-healing (typically closer to 0.005 ms); if a prediction is below the confidence threshold, the reactive detector flags the fault, and deploys the healing module rapidly. The system restores perception to its regular 0.03 ms latency, holds steady-state power at 1.95 W, and exhibits transient peaks up to ∼2.44 W during the self-healing process.
Event Type
Work in Progress
TimeMonday, July 276:32pm - 6:33pm PDT
LocationExhibit Hall