A bird’s-eye-view (BEV) semantic segmentation accelerator (BEVSA) is proposed for real-time 3D space perception in multi-camera system (MCS). The view transformation from multi-camera-view to BEV obstructs the real-time operation on edge devices through 68.3 ms of time consumption during BEV pooling, which requires sorting and irregular memory access over wide searching space. Moreover, the 69.1% high average input activation sparsity of the segmentation process for the transformed BEV features results in excessive meaningless computations. For the real-time implementation of BEV semantic segmentation on edge platforms, this paper proposes two key features: 1) Block-decomposed hierarchical BEV pooling cluster that partitions the searching space in an MCS-suitable way and supports parallel pooling, achieving $43.2 \times$ speedup for BEV pooling over the edge computing platform; 2) Coarse-to-fine-grained zero skipping convolution cluster, which conducts coarse-grained zero skipping for all-zero channels and tile-wise fine-grained zero skipping, improving the convolution throughput by $1.61 \times$. Implemented with 28 nm technology, and evaluated on two representative MCS datasets, BEVSA finally achieves 23.1 frames-per-second of real-time BEV segmentation throughput with $167.4 \times$ higher energy-per-frame over the edge computing platform.

This paper introduces a fast and accurate point segmentation system for real-time (< 50 ms) 3D-LiDAR semantic segmentation. The real-time application of 3D point-cloud neural networks (PNNs) for semantic segmentation of LiDAR-measured data faces considerable challenges such as memory overhead and processing delay in its implementation on GPUs due to the significant computational requirements. These challenges arise from the large volume of points and their complex spatial relationships, leading to intense computational and memory usage. To facilitate real-time implementation with high accuracy, a selective point segmentation (SPS) system is proposed with 3 key features: 1) Adaptive ground estimation for the surroundings, excluding the ground from PNN inference, thereby reducing latency by 46.0%, and addressing accuracy reductions due to false positives by implementing 2-step bin skipping; 2) Coarse-grained entropy-and-density-based region skipping (RSK) excludes large areas from ground estimation; and 3) Fine-grained bin skipping (BSK) with z-distribution skips non-ground bins within these areas. Together, the system achieves a processing time of 42.24 ms and a 3D semantic segmentation accuracy of 90.69% at the semantic KITTI dataset.