Presentation
High-throughput Point-Cloud Accelerator with Sparsity-aware Hierarchical Neighbor Voxel Search and Skipping
DescriptionPoint cloud-based 3D sparse convolution networks are widely employed to process voxel features efficiently. However, the irregularity of voxel sparsity poses significant challenges, leading to increased hardware complexity and inefficiencies. We propose an algorithm-hardware co-design for sparse 3D convolution. At the algorithm level, an on-the-fly threshold-based voxel skipping is adopted, enhancing efficiency. At the hardware level, a hierarchical 3-stage Voxel Search and Skipping is developed to systematically narrow down the non-zero search space, enhancing both performance and hardware utilization. We implemented the proposed accelerator in a 65 nm process to demonstrate a 77.7% reduction in delay compared to a baseline design without the proposed sparsity adaptations. The proposed system also achieved the 1.34× and 2.22× higher energy efficiency and throughput as compared to the state-of-arts.
Event Type
Research Manuscript
TimeWednesday, June 254:30pm - 4:45pm PDT
Location3000, Level 3