  Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) bring
unprecedented performance requirements for automotive systems. Graphic
Processing Unit (GPU) based platforms have been deployed with the aim of
meeting these requirements, being NVIDIA Jetson TX2 and its high-performance
successor, NVIDIA AGX Xavier, relevant representatives. However, to what extent
high-performance GPU configurations are appropriate for ADAS and AD workloads
remains as an open question.
  This paper analyzes this concern and provides valuable insights on this
question by modeling two recent automotive NVIDIA GPU-based platforms, namely
TX2 and AGX Xavier. In particular, our work assesses their microarchitectural
parameters against relevant benchmarks, identifying GPU setups delivering
increased performance within a similar cost envelope, or decreasing hardware
costs while preserving original performance levels. Overall, our analysis
identifies opportunities for the optimization of automotive GPUs to further
increase system efficiency.
