The graph convolutional network (GCN) based 3D point cloud semantic segmentation (PCSS) processor for mobile devices is proposed. GCN based 3D PCSS requires a lot of computation, making it unsuitable for real-time operation in mobile devices. For real-time 3D PCSS on mobile devices, this paper proposes two key features: 1) a sparse grouping based dilated graph convolution (SG-DGC) which reduces 71.7% of the overall computation of GCN by simply dividing input point cloud into multiple sparse point cloud. 2) group-level pipelining which improves low pipeline utilization due to the computation imbalance of GCN. Finally, the proposed GCN processor is simulated in 65 nm CMOS technology and occupies 4.0 mm 2 . The proposed processor consumes 176mW and shows 54.7 frames-per-second (fps) for the 3D point cloud semantic segmentation of indoor scene with 4k points.