Performance analysis and optimization of automotive GPUs

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.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.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. Pedro Benedicte and Jaume Abella have been partially supported by the MINECO under FPU15/01394 grant and Ramon y Cajal postdoctoral fellowship number RYC-2013-14717 respectively and Leonidas Kosmidis under Juan de la Cierva-Formacin postdoctoral fellowship (FJCI-2017-34095).Peer ReviewedPostprint (author's final draft

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