Re-thinking CNN frameworks for time-sensitive autonomous-driving applications: Addressing an industrial challenge

Proceedings of the IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS

Volumn 2019-April, Issue , 2019, Pages 305-317

  Yang, Ming ^a   Wang, Shige ^b   Bakita, Joshua ^a   Vu, Thanh ^a   Smith, F Donelson ^a   Anderson, James H ^a   Frahm, Jan Michael ^a  

^a University of North Carolina   (United States)

^b GENERAL MOTORS CORPORATION   (United States)

Author keywords

Autonomous Driving; GPU; Neural Networks; Real time Embedded Systems

Indexed keywords

AUTONOMOUS VEHICLES; CAMERAS; COMPUTER HARDWARE DESCRIPTION LANGUAGES; EMBEDDED SYSTEMS; GRAPHICS PROCESSING UNIT; NEURAL NETWORKS; RHENIUM COMPOUNDS;

AUTONOMOUS DRIVING; CONVOLUTION NEURAL NETWORK; INDUSTRIAL CASE STUDY; INDUSTRIAL CHALLENGES; MULTIPLE VIDEO STREAMS; REAL-TIME EMBEDDED SYSTEMS; SURROUNDING ENVIRONMENT; VISION-BASED PERCEPTION;

REAL TIME SYSTEMS;

EID: 85067828856     PISSN: 15453421     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/RTAS.2019.00033     Document Type: Conference Paper

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