A 28-nm Energy-Efficient Sparse Neural Network Processor for Point Cloud Applica

Abstract

Voxel-based point cloud networks composed of multiple kinds of sparse convolutions (SCONVs) play an essential role in emerging applications such as autonomous driving and visual navigation. Many researchers have proposed sparse processors for image applications. However, they cannot properly deal with three problems in the point cloud, including low efficiency of random memory access, non-parallel neighbor search and area overhead of supporting hybrid operators, and unbalanced workload among mul