Resiliency of automotive object detection networks on GPU architectures

Safety is the most important aspect of an autonomous driving platform. Deep neural networks (DNNs) play an increasingly critical role in localization, perception, and control in these systems. The object detection and classification inference are of particular importance to construct a precise picture of a vehicle's surrounding objects. Graphics Processing Units (GPU) are well-suited to accelerate such DNN-based inference applications since they leverage data and thread-level parallelism in GPU architectures. Understanding the vulnerability of such DNNs to random hardware faults (including transient and permanent faults) in GPU-based systems is essential to meet the safety requirements of auto safety standards such as the ISO 26262, as well as to influence the design of hardware and software-based safety features in current and future generations of GPU architectures and GPU-based automotive platforms. In this paper, we assess the vulnerability of object detection and classification DNNs to permanent and transient faults using fault injection experiments and accelerated neutron beam testing respectively. We also evaluate the effectiveness of chip-level safety mechanisms in GPU architectures, such as ECC and parity, in detecting these random hardware faults. Our studies demonstrate that such object detection networks tend to be vulnerable to random hardware faults, which cause incorrect or mispredicted object detection outcomes. The neutron beam experiments show that existing chip-level protections successfully mitigate all silent data corruption events caused by transient faults. For permanent faults, while ECC and parity are effective in some cases, our results suggest the need for exploring other complementary detection methods, such as periodic online and offline diagnostic testing.