Performance modeling of heterogeneous HW platforms

The push towards automated and connected driving functionalities mandates the use of heterogeneous hardware platforms in order to provide the required computational resources. For these platforms, established methods for performance modeling in industry are no longer effective or adequate. In this paper, we explore the detailed problem of mapping a prototypical autonomous driving application on a Nvidia Tegra X2 platform while considering different constraints of the application, including end-to-end latencies of event chains spanning CPU and GPU boundaries. With the given use-case and platform, we propose modeling concepts in Amalthea, capturing the architectural aspects of heterogeneous platforms and also the execution structure of the application. These models can be fed into appropriate tools to predict performance properties. We proposed the above problem in the Workshop on Analysis Tools and Methodologies for Embedded and Real-time Systems (WATERS) Industrial Challenge 2019 and in response, academicians came up with different solutions. In this paper, we evaluate these different solutions and summarize all approaches. The lesson learned from this challenge is then used to improve on the simplifying assumptions we made in our original formulation and discuss future modeling extensions.