Performance analysis and optimization of automotive GPUs
Document typeConference report
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
European Commission's projectSuPerCom - Sustainable Performance for High-Performance Embedded Computing Systems (EC-H2020-772773)
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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CitationMazzocchetti, F. [et al.]. Performance analysis and optimization of automotive GPUs. A: International Symposium on Computer Architecture and High Performance Computing. "SBAC-PAD 2019: 2019 31st International Symposium on Computer Architecture and High Performance Computing: proceedings: Campo Grande, MS, Brazil: October, 15th-18th, 2019". Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 96-103.
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