Performance Analysis and Optimization Opportunities for NVIDIA Automotive GPUs
| Autor: | Francisco J. Cazorla, Jaume Abella, Hamid Tabani, Fabio Mazzocchetti, Pedro Benedicte |
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| Přispěvatelé: | Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Barcelona Supercomputing Center |
| Rok vydání: | 2021 |
| Předmět: |
Optimization
FOS: Computer and information sciences Driver assistance systems Computer Networks and Communications Design space exploration Computer science Vehicles autònoms Automotive industry Autonomous vehicles Advanced driver assistance systems 02 engineering and technology Theoretical Computer Science CUDA Artificial Intelligence Hardware Architecture (cs.AR) 0202 electrical engineering electronic engineering information engineering Computer Science - Hardware Architecture Informàtica::Arquitectura de computadors [Àrees temàtiques de la UPC] business.industry Performance analysis 020206 networking & telecommunications Benchmarking Unitats de processament gràfic Computer architecture Hardware and Architecture Automotive GPUs 020201 artificial intelligence & image processing business Graphics processing units Software |
| Zdroj: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) Journal of Parallel and Distributed Computing |
| DOI: | 10.48550/arxiv.2104.07735 |
| Popis: | 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. This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under grant TIN2015-65316-P, the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 772773) and the HiPEAC Network of Excellence. |
| Databáze: | OpenAIRE |
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