An effective and high-speed 3D point cloud-based neural network processing unit (PNNPU) is proposed using the block-based point processing. It has three key features: 1) page-based block memory management unit (PMMU) with linked list-based page table (LLPT) for on-chip memory footprint reduction, 2) hierarchical block-wise farthest point sampling (HFPS), and block skipping ball query (BSBQ) for fast and efficient point processing, 3) skipping-based max-pooling prediction (SMPP) for throughput enhancement. The PNNPU is fabricated in 65nm CMOS process and evaluated on the 3D object detection (3D OD) application. As a result, it shows 84.8 fps at 266.8mW power consumption and achieving 6.6-11.9 TOPS/W energy efficiency.

  - Page-based point block memory management unit (PMMU)

  - Hierarchical block-wise farthest point sampling (HFPS)

  - Block skipping ball-query (BSBQ)

  - Skipping-based max-pooling prediction (SMPP) 

  - S.VLSI 2021

  - HOTCHIPS 2021