Work-In-Progress: Could Tensorflow Applications Benefit from a Mixed-Criticality 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.