부산대학교 도서관

Voxel-based neural networks are promising algorithms to process 3D point cloud data. However, the voxel-based neural networks run inefficiently on GPUs for the unique calculation modes of 3D sparse convolution. In addition, the large irregular activation sparsity of 3D voxel-based neural networks can be utilized more to improve the performance of the hardware accelerator. This brief presents a high-performance 3D point cloud accelerator, namely HISPOC, which can effectively leverage irregular activation sparsity. Specifically, we propose a 3D bitmap-based search engine that significantly reduces the complexity of voxel searching. In addition, we propose a match-based input feature manager and a dichotomy-based output feature sparsity encoder to enhance computation efficiency and reduce RAM consumption based on the Zero-Value Compression algorithm. Finally, the average memory footprint of the feature is reduced to $54.98\%$ . we implement the HISPOC on Xilinx Virtex VC707 board, achieving $3.3-1272.1$ GOPS, and 11.8 FPS under 109.8 KB RAM consumption, which are $3.34\times $ , $1.39\times $ , and $0.62\times $ compared with the state-of-the-art works.