GPU virtualization for automotive
The level of performance future ADAS platforms will require necessitates increasingly larger GPUs, which will inevitably increase manufacturing costs. To mitigate this, platform vendors will look to increase the value of the GPU by using it to perform multiple workloads in the car. This will only be possible if the GPU has rock-solid support for hardware accelerated virtualization. Virtualization lets the GPU run multiple operating contexts, such as an app/OS container, without any of those contexts being aware of each other or in any way affect each other.
This is important. Imagine a situation where a problem with the dashboard software was able to affect the correct operation of the driver assistance system. This could be potentially catastrophic and must be avoided at all costs. The ability to have protected, virtualized, execution contexts supported by the GPU will ensure that this situation does not arise.
Virtualization works at its best when there is hardware support for entirely separate managed address spaces for each context to use and for restarting or flushing a context that's misbehaving. This isolation is key to allowing cooperative use of the GPU, while keeping critical software, such as driver assistance systems, from being corrupted by any other process.