Predictable vision for autonomous systems

In this perspective cum case-study paper, we argue the need for designing timing-predictable vision processing algorithms for autonomous systems. Many core functions in systems like autonomous vehicles involve computer vision within a control loop. Designing such closed-loop controllers and guaranteeing their performance requires the vision processing to be predictable. But this is challenging given the multitude of choices when implementing vision processing algorithms, and the heterogeneity of the architectures (involving GPUs and FPGAs) on which such algorithms are implemented. Towards this, we report a tracing and measurement infrastructure we have been building and illustrate its potential utility using a case study.