Modeling, programming and performance analysis of automotive environment map representations on embedded GPUs

Fickenscher, Jörg; Reiche, Oliver; Schlumberger, Jens; Hannig, Frank; Teich, Jürgen

doi:10.1109/hldvt.2016.7748257

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…Several works have analyzed the performance of COTS automotive GPUs to optimize the behavior of applications running atop [18]. While conclusions reached by those works are highly valuable for an efficient use of hardware, they do not provide any insight on how to optimize hardware design.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis and Optimization Opportunities for NVIDIA Automotive GPUs

Tabani

Mazzocchetti

Benedicte

et al. 2021

Journal of Parallel and Distributed Computing

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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.

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Section: Related Workmentioning

confidence: 99%

Performance Analysis and Optimization Opportunities for NVIDIA Automotive GPUs

Tabani

Mazzocchetti

Benedicte

et al. 2021

Journal of Parallel and Distributed Computing

View full text Add to dashboard Cite

show abstract

“…Several works have analyzed the performance of COTS automotive GPUs to optimize the behavior of applications running atop [16]. While conclusions reached by those works are highly valuable for an efficient use of hardware, they do not provide any insight on how to optimize hardware design.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis and Optimization of Automotive GPUs

Mazzocchetti

Benedicte

Tabani

et al. 2019

2019 31st International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

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Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) have drastically increased the performance demands of automotive systems. Suitable highperformance platforms building upon Graphic Processing Units (GPUs) have been developed to respond to this demand, being NVIDIA Jetson TX2 a relevant representative. However, whether high-performance GPU configurations are appropriate for automotive setups remains as an open question. This paper aims at providing light on this question by modelling an automotive GPU (Jetson TX2), analyzing its microarchitectural parameters against relevant benchmarks, and identifying specific configurations able to meaningfully increase performance within similar cost envelopes, or to decrease costs preserving original performance levels. Overall, our analysis opens the door to the optimization of automotive GPUs for further system efficiency.

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