An Energy-Efficient 3D Semantic Segmentation Processor With Offset-Wise Weight Quantization

Voxel-based point cloud neural networks have gained significant attention for 3D semantic segmentation due to their effectiveness in processing point clouds. However, the high computational overhead of processing large-scale point clouds and the inherent irregularity of these point clouds hinder the fast and energy-efficient acceleration of segmentation. This brief presents a 3D semantic segmentation accelerator with an offset-wise weight quantization technique that drastically reduces computational complexity. The proposed design introduces a unified voxel search unit that efficiently processes various types of operations needed to capture spatial relationships among irregularly stored voxels. In addition, a dual-mode computing engine combined with a novel workload allocation technique enables highly parallel processing and maximizes processing core utilization. Fabricated in a 28-nm CMOS technology, the proposed processor achieves 10.24 TOPS/W energy efficiency, outperforming prior art.