I am glad to announce the description of the WATERS Industrial Challenge 2019 that is proposed by Bosch in cooperation with University of Modena.
Please note that the Amalthea performance file that is necessary to respond to the challenge will be delivered in 2-3 weeks.
Automotive E/E architectures are currently undergoing a radical shift in the way they are designed, implemented and deployed. Especially, the computational power and communication bandwidth required for new functionalities, such as automated driving or connected vehicle
functions (e.g. path planning, object recognition, predictive cruise control), exceed the capabilities of current compute nodes (mainly micro-controller SoCs) and is leading to a reorganization of automotive systems following the paradigm of so-called centralized E/E architectures that are based on a new class of computing nodes featuring more powerful micro-processors and accelerators such as GPUs.
One consequence of these centralized E/E architectures is that heterogeneous applications will be co-existing on the same HW platform, heterogeneous not only in their model of computation (ranging from classical periodic control over event-based planning to stream-based perception applications) but also in their criticality, in terms of real-time and safety requirements. Ultimately, the burden of integration is shifted from the network to the ECU level and in this regard typically from the vehicle manufacturer to the supplier of the control unit.
In order to cope with design and integration challenge, expressive performance models capturing the heterogeneity of the hardware-software system are needed. The WATERS industrial challenge addresses one example in this context, where the processing power offered by GPUs and their capability to execute parallel workloads is exploited to execute and accelerate applications related to advanced driver assistance systems.
Thereby, we start with relatively simple models that are from our point-of-view, on the one hand, sufficient to derive sensible performance predictions for the presented use-case, and that are, on the other hand, not too far away from established models used in the real-time community, or in other words, there is a chance for existing models to be extended for addressing this challenge.
This document contains the following information:
- A primer on NVIDIA TX2 platform with focus on CPU–GPU interactions, scheduling, memory model, and offloading mechanisms as basis for performance modeling in Amalthea
- A brief description of the challenge questions
- A description of the considered application (a concrete Amalthea performance model will be provide to a later point in time)
- A description of utilized Amalthea performance modeling approaches suitable to description the software and hardware parts including their interaction for the given challenge.
answer your questions in a timely fashion.