A Sparsity-Aware Autonomous Path Planning Accelerator with HW/SW Co-Design and Multi-Level Dataflow Optimization

TL;DR

This paper introduces a sparsity-aware FPGA acceleration framework for autonomous path planning, utilizing HW/SW co-design and multi-level dataflow optimization to significantly improve speed and energy efficiency.

Contribution

It presents a novel FPGA-based acceleration framework with customized sparse matrix operations and multi-level dataflow optimization for efficient autonomous path planning.

Findings

Achieves 1.48x faster performance than the best FPGA design

Provides 2.89x speedup over an Intel i7 CPU

Improves throughput by 2.05x over existing FPGA solutions

Abstract

Path planning is critical for autonomous driving, generating smooth, collision-free, feasible paths based on perception and localization inputs. However, its computationally intensive nature poses significant challenges for resource-constrained autonomous driving hardware. This paper presents an end-to-end FPGA-based acceleration framework targeting the quadratic programming (QP), core of optimization-based path planning. We employ a hardware-friendly alternating direction method of multipliers (ADMM) for QP solving and a parallelizable preconditioned conjugate gradient (PCG) method for linear systems. By analyzing sparse matrix patterns, we propose customized storage schemes and efficient sparse matrix multiplication units, significantly reducing resource usage and accelerating matrix operations. Our multi-level dataflow optimization strategy incorporates intra-operator parallelization and pipelining, inter-operator fine-grained pipelining, and CPU-FPGA system-level task mapping. Implemented on the AMD ZCU102 platform, our framework achieves state-of-the-art latency and energy efficiency, including 1.48x faster performance than the best FPGA-based design, 2.89x over an Intel i7-11800H CPU, 5.62x over an ARM Cortex-A57 embedded CPU, and 1.56x over a state-of-the-art GPU solution, along with a 2.05x throughput improvement over existing FPGA-based designs.