Voxel-PIM: An Efficient Process-in-Memory Based ASIC Accelerator for Voxel-Based Point Cloud Neural Networks

The Voxel-based point cloud neural networks (PNNs) have demonstrated exceptional accuracy across various 3D point cloud tasks. However, the practical deployment of Voxel-based PNNs on resource-constrained edge devices faces significant challenges, primarily stemming from two memory-access intensive operations: the map search operation and the sparse 3D convolution (Spconv3D) operation. Specifically, three key challenges remain to be addressed, including: (1) the extensive sequential coordinate comparisons and irregular off-chip memory access patterns generated by map search operation; (2) the considerable data movement requirements associated with data-intensive convolution operation; (3) significant workload imbalance resulting from point cloud sparsity and irregularity.In this paper, we propose Voxel-PIM, an efficient SRAM-based accelerator for Voxel-based PNNs that leverages in-memory search and compute techniques. The Voxel-PIM architecture primarily contains two Processing-in-Memory (PIM) cores: a Content-Addressable Memory (CAM)-based Search Core and a Compute-in-Memory (CIM)-based Computing Core. For the CAM core, we propose the KD-Tree-based Partitioning and Searching (KDPS) strategy, which activates only a fraction of the array and reduces search energy consumption during search operation. For the CIM array, we introduce a novel submatrices mapping method to flexibly support both Spconv3D and Conv2D operations. Furthermore, we propose a Weight Workload Balanced (W2B) method to mitigate computational imbalance. The hardware performance of Voxel-PIM is estimated for a 22nm technology. The simulation results demonstrate that Voxel-PIM achieves averagely 1.53~7.26× higher energy efficiency, 1.03~2.98× speed up in detection task, and 1.4~11.4× speed up in segmentation task compared to state-of-the-art (SOTA) point cloud accelerators and general-purpose processors.