Energy-efficient 3D point cloud neural network accelerators are desired for autonomous driving and AR/VR applications. This paper proposes TiPU, a spatial-locality-aware near-memory tile processing unit where the point clouds are partitioned into tiles to process spatial features locally. Intra-tile farthest point sampling and cross-tile neighbor search are employed to avoid unnecessary distance computing. To efficiently facilitate the tile operations, TiPU architecture consists of a tile-based unified distance computing unit, a near-CAM feature extractor, and a near-SRAM-computing MLP engine. The experimental results show that, compared to GPU implementation, TiPU achieves 15.7× processing speed and reduces 7308× energy consumption.