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Deep learning–based classifiers are widely used for perception in autonomous Cyber-Physical Systems (CPS’s). However, such classifiers rarely offer guarantees of perfect accuracy while being optimized for efficiency. To support safety-critical perception, ensembles of multiple different classifiers working in concert are typically used. Since CPS’s interact with the physical world continuously, it is not unreasonable to expect dependencies among successive inputs in a stream of sensor data. Prior work introduced a classification technique that leverages these inter-input dependencies to reduce the average time to successful classification using classifier ensembles. In this paper, we propose generalizations to this classification technique, both in the improved generation of classifier cascades and the modeling of temporal dependencies. We demonstrate, through theoretical analysis and numerical evaluation, that our approach achieves further reductions in average classification latency compared to the prior methods. 

Tolerating hardware faults in modern architectures is becoming a prominent problem due to the miniaturization of the hardware components, their increasing complexity, and the necessity to reduce costs. Software-Implemented Hardware Fault Tolerance approaches have been developed to improve system dependability regarding hardware faults without resorting to custom hardware solutions. However, these come at the expense of making the satisfaction of the timing constraints of the applications/activities harder from a scheduling standpoint. This article surveys the current state-of-the-art of fault tolerance approaches when used in the context of real-time systems, identifying the main challenges and the cross-links between these two topics. We propose a joint scheduling-failure analysis model that highlights the formal interactions among software fault tolerance mechanisms and timing properties. This model allows us to present and discuss many open research questions with the final aim to spur future research activities.