gGuard: Enabling Leakage-Resilient Memory Isolation in GPU-accelerated Autonomous Embedded Systems

Abstract

Graphics processing units (GPUs) are being widely used as co-processors for performance acceleration in many autonomous embedded systems such as robotics and autonomous vehicles. However, current GPU hardware and systems software, including GPU device drivers, compilers, and operating systems, do not implement proper memory protection mechanisms due to performance and proprietary reasons, causing severe vulnerabilities such as information leakage. In this paper, we present gGuard, a leakage-resilient GPU memory management system with strong isolation. Based on the intrinsic characteristics of information leakage vulnerabilities on GPUs, gGuard develops a set of efficient and accurate data shredding techniques implemented at the compiler, library, and operating system levels, with the core idea of exploring the data access patterns and dependencies for efficient application-aware data shredding. Our implementation and evaluation show that gGuard can provide effective mitigation on GPU data leakage issues through efficient GPU data shredding while introducing less than 6% overhead in all tested scenarios.