Performance Analysis and Optimization Opportunities for NVIDIA Automotive GPUs

TL;DR

This paper evaluates NVIDIA automotive GPUs TX2 and AGX Xavier, analyzing their microarchitectural performance and identifying optimization opportunities to enhance efficiency for ADAS and autonomous driving workloads.

Contribution

It provides a detailed microarchitectural analysis of NVIDIA automotive GPUs and suggests optimization strategies to improve performance and cost-efficiency.

Findings

Certain GPU configurations offer higher performance at similar costs

Some setups can reduce hardware costs without sacrificing performance

Opportunities for microarchitectural optimizations are identified

Abstract

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.