Assessing the use of NVIDIA Multi-Instance GPU in the Automotive Domain

The search for resource isolation and segregation is relentless to increase execution time determinism (ETD) as an essential feature for a successful certification process in embedded critical domains like automotive. In those domains, the advent of advanced software-controlled features requires unprecedented computing performance that calls for the use of acceleration hardware, with GPUs having a prominent position. The latest NVIDIA GPUs generations (Ampere, Hopper, Blackwell) feature MIG (Multi-Instance GPU), a mechanism that allows partitioning the GPU into fully isolated GPU instances, each with its own memory, cache, and computing resources. Despite the clear benefits of MIG on ETD, the latest NVIDIA automotive GPUs do not implement it. In this work, we first empirically analyze the benefits of MIG in a non-automotive GPU showing the main traits in its use to improve ETD. And second, we identify the potential reasons precluding the deployment of MIG for automotive GPUs: automotive market specific needs, and the difference between GPU and memory technologies used in high-performance GPUs, which implement MIG, and automotive GPUs that lack it.