BEVSA: A Real-Time Bird's-Eye-View Semantic Segmentation Accelerator for Multi-Camera System

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.