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.
翻译：高级驾驶协助系统(ADS)和自动驾驶系统(ADV)为汽车系统带来了前所未有的性能要求。基于图形处理股(GPU)的平台已经部署,以满足这些要求,它们是NVIDIA Jetson TX2及其高性能继承人NVIDIA AGX Xavier, 相关代表。然而,对于ADAS和AD工作量来说,高性能的GPU配置在多大程度上适合ADAS和AD工作量仍然是一个未决问题。本文件分析了这一问题,并通过模拟最近两个基于NVIDIA GPU的汽车平台,即TX2和AGXX Xavier,为这一问题提供了宝贵的见解。特别是,我们的工作对照相关基准评估了它们的微结构设计参数,确定了GPUP组在类似费用范围内提供更高性能,或降低硬件成本,同时保持原有性能水平。总体而言,我们的分析确定了优化汽车GPUP的机会,以进一步提高系统效率。
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