A Hybrid GPU + FPGA System Design for Autonomous Driving Cars

Cong Hao, Junli Gu, Deming Chen, Atif Sarwari, Zhijie Jin, Husam Abu-Haimed, Daryl Sew, Yuhong Li, Xinheng Liu, Bryan Wu, Dongdong Fu

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Autonomous driving cars need highly complex hardware and software systems, which require high performance computing platforms in order to enable a real time AI-based perception and decision making pipeline. The industry has been exploring various in-vehicle accelerators such as GPUs, ASICs and FPGAs. Yet the autonomous driving platform design is far from mature when taking into account of system reliability, redundancy and higher level of autonomy. In this work, we propose a hybrid computing system design, which integrates a GPU as the primary computing system and a FPGA as a secondary system. This hybrid system architecture has multiple advantages: 1) The FPGA can be constantly running as a complementary system with very short latency, helping to detect main system failure and anomalous behavior, contributing to system functionality verification and reliability. 2) If the primary system fails (mostly from sensor or interconnection error), the FPGA will quickly detect the failure and run a safe-mode task with a subset of sensors. 3) The FPGA can be used as an independent computing system to run extra algorithm components to improve the overall system autonomy. For example, FPGA can handle driver monitoring tasks while GPU focuses on driving functions. Together they can boost the driving function from L2 (constantly requires driver's attention) to L3 (allows driver to mind off for 10 seconds). This paper defines how such a system works, discusses various use cases and potential design challenges, and shares some initial results and insights about how to make such a system deliver the maximum value for autonomous driving.

Original languageEnglish (US)
Title of host publication2019 IEEE International Workshop on Signal Processing Systems, SiPS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728119274
StatePublished - Oct 2019
Event33rd IEEE International Workshop on Signal Processing Systems, SiPS 2019 - Nanjing, China
Duration: Oct 20 2019Oct 23 2019

Publication series

NameIEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation
ISSN (Print)1520-6130


Conference33rd IEEE International Workshop on Signal Processing Systems, SiPS 2019


  • AI
  • Autonomous Driving
  • FPGA
  • GPU
  • Hybrid System
  • Reliability

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing
  • Applied Mathematics
  • Hardware and Architecture

Cite this