Voxel-CIM: An Efficient Compute-in-Memory Accelerator for Voxel-based Point Cloud Neural Networks

The 3D point cloud perception has emerged as a fundamental role for a wide range of applications. In particular, with the rapid development of neural networks, the voxel-based networks attract great attention due to their excellent performance. Various accelerator designs have been proposed to improve the hardware performance of voxel-based networks, especially to speed up the map search process. However, several challenges still exist including: (1) massive off-chip data access volume caused by map search operations, notably for high resolution and dense distribution cases, (2) frequent data movement for data-intensive convolution operations, (3) imbalanced workload caused by irregular sparsity of point data.