  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.
