Modern real-time embedded systems increasingly integrate software with varying criticality levels, which increases the interest in mixed criticality scheduling (MCS). MCS provides runtime adaptation mechanisms when low criticality tasks exceed their allocated execution budgets in order to guarantee the timing constraints of high criticality tasks. Most of the current research on MCS adaptation mechanisms focuses on guaranteeing timing constraints by interrupting and discarding low criticality tasks when their budgets are exceeded. They consider only the temporal dimension, without taking into account the quality of the results obtained. Quality is defined as the accuracy level of the results computed by a task within a given execution time. In this article, we propose an approach to integrate quality in a new task model to establish a relationship between quality and scheduling design. We propose the q-AMC scheduling algorithm to validate our task model. This algorithm integrates quality degradation into the scheduling adaptation mechanism. Simulation-based experiments show that our approach increases the quality up to 44.6% compared to the original AMC approach.

In this article, we investigate the interest in applying a mixed-criticality approach to schedule convolutional neural network (CNN) applications on multicore architectures. We deal with software composed of real-time interactive applications and CNNs that have different criticality levels. A classical means to schedule software with various criticality levels is to apply partitioning methods to enforce spatial and temporal isolation, which may be inefficient if application execution times have a high level of variability. In that case, applying a mixed-criticality approach may improve resource usage. We conducted a measurement campaign to assess the variability of CNN execution time and investigate whether this kind of application could benefit from a mixed-criticality approach. The results show that the execution times of the chosen CNN application vary with an average execution time of 109 ms and a worst case of 252 ms. Furthermore, they indicate a potential save of computing resources up to 73 % when applying a mixed-criticality approach instead of partitioning methods.