TY - GEN
T1 - A Sparsity-Aware Autonomous Path Planning Accelerator with Algorithm-Architecture Co-Design
AU - Zhang, Yanjun
AU - Niu, Xiaoyu
AU - Zhang, Yifan
AU - Tian, Hongzheng
AU - Yu, Bo
AU - Liu, Shaoshan
AU - Huang, Sitao
N1 - Publisher Copyright:
© 2024 Copyright is held by the owner/author(s).
PY - 2025/4/9
Y1 - 2025/4/9
N2 - 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.
AB - 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.
KW - FPGA
KW - Path planning
KW - autonomous driving
KW - quadratic programming
UR - http://www.scopus.com/inward/record.url?scp=105003621458&partnerID=8YFLogxK
U2 - 10.1145/3676536.3676700
DO - 10.1145/3676536.3676700
M3 - Conference contribution
AN - SCOPUS:105003621458
T3 - IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
BT - Proceedings of the 43rd IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 43rd International Conference on Computer-Aided Design, ICCAD 2024
Y2 - 27 October 2024 through 31 October 2024
ER -