Secure and Timely GPU Execution in Cyber-physical Systems

Abstract

Graphics Processing Units (GPU) are increasingly deployed on Cyber-physical Systems (CPSs), frequently used to perform real-time safety-critical functions, such as object detection on autonomous vehicles. As a result, availability is important for GPU tasks in CPS platforms. However, existing Trusted Execution Environments (TEE) solutions with availability guarantees focus only on CPU computing. To bridge this gap, we propose AvaGPU, a TEE that guarantees real-time availability for CPU tasks involving GPU execution under compromised OS. There are three technical challenges. First, to prevent malicious resource contention due to separate scheduling of CPU and GPU tasks, we proposed a CPU-GPU co-scheduling framework that couples the priority of CPU and GPU tasks. Second, we propose software-based secure preemption on GPU tasks to bound the degree of priority inversion on GPU. Third, we propose a new split design of GPU driver with minimized Trusted Computing Base (TCB) to achieve secure and efficient GPU management for CPS. We implement a prototype of AvaGPU on the Jetson AGX Orin platform. The system is evaluated on benchmark, synthetic tasks, and real-world applications with 15.87% runtime overhead on average.