A Sparsity-Aware Autonomous Path Planning Accelerator with Algorithm-Architecture Co-Design

Yanjun Zhang, Xiaoyu Niu, Yifan Zhang, Hongzheng Tian, Bo Yu, Shaoshan Liu, Sitao Huang*

*Corresponding author for this work

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

Abstract

Path planning is a critical task in autonomous driving systems that is most susceptible to real-time constraints but often demands computationally intensive mathematical solvers, two contradictory goals. This conflict makes the computing of path planning a paramount challenge. At the heart of most path planners is the quadratic programming (QP) solver, which places excessive demands on the CPU in real-world autonomous driving applications. In this paper, we present an FPGA-based acceleration framework for path planning problems. Our approach leverages an operator splitting solver for quadratic programs (OSQP) and employs the preconditioned conjugate gradient (PCG) method for solving linear systems, which are customized to be more hardware-friendly than prior works. Specific memory management and parallel processing were tailored to the matrix pattern, and the incorporation of pipelining was executed to enhance throughput and execution speed. Our FPGA-based implementation achieves state-of-the-art performance against existing works, including an average 1.98× speedup compared with the state-of-the-art QP solver on Intel i7-11800H CPU, 3.90× speedup over an ARM Cortex-A57 embedded CPU, and 12.3× speedup over an NVIDIA RTX 3090 GPU.

Original languageEnglish
Title of host publicationProceedings of the 43rd IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798400710773
DOIs
Publication statusPublished - 9 Apr 2025
Event43rd International Conference on Computer-Aided Design, ICCAD 2024 - New York, United States
Duration: 27 Oct 202431 Oct 2024

Publication series

NameIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
ISSN (Print)1092-3152

Conference

Conference43rd International Conference on Computer-Aided Design, ICCAD 2024
Country/TerritoryUnited States
CityNew York
Period27/10/2431/10/24

Keywords

  • FPGA
  • Path planning
  • autonomous driving
  • quadratic programming

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