Abstract
Automotive systems are integrating artificial intelligence and complex software stacks aiming to interpret the real world, make decisions, and perform actions without human input. The occurrence of soft errors in such systems can lead to wrong decisions, which might ultimately incur in life losses. This brief focuses on the soft error susceptibility assessment of a real automotive application running on top of unmodified Linux kernels, and considering two commercially available processors, and three cross-compilers. Results collected from more than 29 thousand simulation hours show that the occurrence of faults in critical functions may cause 2.16× more failures on the system.
Highlights
Market predictions estimate that autonomous vehicles will result in up to US$ 1.3 trillion savings for the US economy, mainly, due to the reduction in accidents, fuel utilisation, traffic and parking congestion [1]
Different from other works, this paper contributes with an extensive soft error assessment that employs a technique that isolates the critical functions of a real automotive application while considering different inputs, kernels, compilers and processor architectures
The adopted convolutional neural network (CNN) is relatively simple, i.e., 17-layer, when compared to the 32-layer used in the present work. While this approach evaluates the Deep Neural Network (DNN) application behaviour under the presence of faults, our work considers a real software stack with multiple variants
Summary
Market predictions estimate that autonomous vehicles will result in up to US$ 1.3 trillion savings for the US economy, mainly, due to the reduction in accidents, fuel utilisation, traffic and parking congestion [1]. Due to the complexity of such stacks (e.g., kernels, drivers and heavy applications), analysing their soft error reliability may take several months if conducted at low level simulations (e.g., gate-level) For this reason, this work utilises an open-source fault injection framework [3] to perform an in-depth and extensive soft error susceptibility evaluation of a realistic automotive software stack. Different from other works, this paper contributes with an extensive soft error assessment that employs a technique that isolates the critical functions of a real automotive application while considering different inputs (e.g., images), kernels, compilers and processor architectures This is the first work that uses such an approach and number of variants, enabling to identify the soft error occurrence and the specific software characteristics that contribute more directly to their appearance
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