论文标题
FPGA上的PointNet用于实时LIDAR点云处理
PointNet on FPGA for Real-Time LiDAR Point Cloud Processing
论文作者
论文摘要
激光雷达传感器已被广泛用于许多自动驾驶方式，例如感知，映射和定位。本文提出了一个基于FPGA的深度学习平台，用于针对自动驾驶汽车的实时点云处理。 Velodyne激光雷达传感器的软件驱动程序已修改并移至片上处理器系统中，而可编程逻辑则设计为定制的硬件加速器。作为用于点云处理的最先进的深度学习算法，PointNet已成功地在拟议的FPGA平台上实现。 PointNet的FPGA实现位于Xilinx Zynx Zynq Ultrascale+ MPSOC ZCU104开发委员会中，分别实现了182.1 GOPS和280.0 GOPS的计算性能，分别用于分类和细分。所提出的设计可以支持每个框架最多4096点的输入。分类的处理时间为19.8 ms，细分时间为34.6毫秒，满足大多数现有LIDAR传感器的实时需求。
LiDAR sensors have been widely used in many autonomous vehicle modalities, such as perception, mapping, and localization. This paper presents an FPGA-based deep learning platform for real-time point cloud processing targeted on autonomous vehicles. The software driver for the Velodyne LiDAR sensor is modified and moved into the on-chip processor system, while the programmable logic is designed as a customized hardware accelerator. As the state-of-art deep learning algorithm for point cloud processing, PointNet is successfully implemented on the proposed FPGA platform. Targeted on a Xilinx Zynq UltraScale+ MPSoC ZCU104 development board, the FPGA implementations of PointNet achieve the computing performance of 182.1 GOPS and 280.0 GOPS for classification and segmentation respectively. The proposed design can support an input up to 4096 points per frame. The processing time is 19.8 ms for classification and 34.6 ms for segmentation, which meets the real-time requirement for most of the existing LiDAR sensors.
