A Near Sensor Edge Computing System for Point Cloud Semantic Segmentation

TL;DR

This paper introduces a lightweight point cloud semantic segmentation network and a near sensor computing system using FPGA-based DPU to improve autonomous vehicle processing efficiency and reduce latency.

Contribution

It presents a novel efficient segmentation network based on range view and a near sensor FPGA system that offloads computation from the vehicle ECU.

Findings

Achieved 10 fps on Xilinx DPU

Network has 42.5 GOP/W efficiency

Reduces ECU computational load

Abstract

Point cloud semantic segmentation has attracted attentions due to its robustness to light condition. This makes it an ideal semantic solution for autonomous driving. However, considering the large computation burden and bandwidth demanding of neural networks, putting all the computing into vehicle Electronic Control Unit (ECU) is not efficient or practical. In this paper, we proposed a light weighted point cloud semantic segmentation network based on range view. Due to its simple pre-processing and standard convolution, it is efficient when running on deep learning accelerator like DPU. Furthermore, a near sensor computing system is built for autonomous vehicles. In this system, a FPGA-based deep learning accelerator core (DPU) is placed next to the LiDAR sensor, to perform point cloud pre-processing and segmentation neural network. By leaving only the post-processing step to ECU, this solution heavily alleviate the computation burden of ECU and consequently shortens the decision making and vehicles reaction latency. Our semantic segmentation network achieved 10 frame per second (fps) on Xilinx DPU with computation efficiency 42.5 GOP/W.