A Sparsity-Aware Autonomous Path Planning Accelerator with Algorithm-Architecture Co-Design
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 p...
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| Vydané v: | Digest of technical papers - IEEE/ACM International Conference on Computer-Aided Design s. 1 - 9 |
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27.10.2024
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| ISSN: | 1558-2434 |
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| 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 \times speedup compared with the state-of-the-art QP solver on Intel i7-11800H CPU, 3.90 \times speedup over an ARM Cortex-A57 embedded CPU, and 12.3 \times speedup over an NVIDIA RTX 3090 GPU. |
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| AbstractList | 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 \times speedup compared with the state-of-the-art QP solver on Intel i7-11800H CPU, 3.90 \times speedup over an ARM Cortex-A57 embedded CPU, and 12.3 \times speedup over an NVIDIA RTX 3090 GPU. |
| Author | Zhang, Yanjun Tian, Hongzheng Zhang, Yifan Niu, Xiaoyu Liu, Shaoshan Yu, Bo Huang, Sitao |
| Author_xml | – sequence: 1 givenname: Yanjun surname: Zhang fullname: Zhang, Yanjun organization: Beijing Institute of Technology – sequence: 2 givenname: Xiaoyu surname: Niu fullname: Niu, Xiaoyu organization: Beijing Institute of Technology – sequence: 3 givenname: Yifan surname: Zhang fullname: Zhang, Yifan organization: University of California,Irvine – sequence: 4 givenname: Hongzheng surname: Tian fullname: Tian, Hongzheng organization: University of California,Irvine – sequence: 5 givenname: Bo surname: Yu fullname: Yu, Bo organization: Shenzhen Institute of Artificial Intelligence and Robotics for Society – sequence: 6 givenname: Shaoshan surname: Liu fullname: Liu, Shaoshan organization: Shenzhen Institute of Artificial Intelligence and Robotics for Society – sequence: 7 givenname: Sitao surname: Huang fullname: Huang, Sitao organization: University of California,Irvine |
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| Snippet | Path planning is a critical task in autonomous driving systems that is most susceptible to real-time constraints but often demands computationally intensive... |
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| SubjectTerms | autonomous driving Autonomous vehicles FPGA Graphics processing units Linear systems Memory management Path planning Phonocardiography Pipeline processing Quadratic programming Real-time systems Throughput |
| Title | A Sparsity-Aware Autonomous Path Planning Accelerator with Algorithm-Architecture Co-Design |
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