Improving the performance of ADAS application in heterogeneous context: A case of lane detection

Xiebing Wang, Mingyue Cui, Kai Huang, Alois Knoll, Long Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

This paper investigates the optimization of OpenCL-based ADAS applications in heterogeneous context. In particular, we take the widely-used lane detection algorithm (LDA) as a case study. The application is profiled to identify the performance bottlenecks and then three optimization strategies are adopted. On the kernel side, the parallel granularity is regulated via compute unit replication and loop unrolling. On the host side, the kernel API function calls are scheduled in an interleaved manner to overlap the accelerator execution time. Moreover, the computation workload of the algorithm is tuned by dynamically adjusting the processed image ROI size. Experimental results reveal that the optimized implementation can achieve an average 2.27x speedup when compared with the naive parallel application.

Original languageEnglish
Title of host publication2017 IEEE 20th International Conference on Intelligent Transportation Systems, ITSC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538615256
DOIs
StatePublished - 2 Jul 2017
Event20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017 - Yokohama, Kanagawa, Japan
Duration: 16 Oct 201719 Oct 2017

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
Volume2018-March

Conference

Conference20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017
Country/TerritoryJapan
CityYokohama, Kanagawa
Period16/10/1719/10/17

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