Reproduce “FPPS: An FPGA-Based Point Cloud Processing System” on OpenResearch
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About this paper
Xiaofeng Zhou, Linfeng Du, Hanwei Fan, Wei Zhang
An FPGA-based system, FPPS, accelerates the Iterative Closest Point (ICP) algorithm for point cloud processing, demonstrating an average 15.95x speedup and 8.58x higher power efficiency over CPU baselines on the KITTI dataset, with accuracy maintained.
Key insights
- FPGAs offer a critical balance of performance, energy efficiency, and reconfigurability, making them well-suited for the dynamic algorithmic demands of autonomous driving applications.
- High-throughput, deterministic low-latency for ICP can be achieved by implementing exact, parallel nearest-neighbor search using a systolic array architecture on FPGAs, outperforming k-d tree-based hardware approaches.
- Effective hardware-software co-design, combined with familiar API wrappers, is crucial for bridging the gap between specialized hardware acceleration and practical developer adoption in complex systems.
Summary by alphaXiv.
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