Point Cloud Acceleration by Exploiting Geometric Similarity

Deep learning on point clouds has attracted increasing attention for various emerging 3D computer vision applications, such as autonomous driving, robotics, and virtual reality. These applications interact with people in real-time on edge devices and thus require low latency and low energy. To accelerate the execution of deep neural networks (DNNs) on point clouds, some customized accelerators have been proposed, which achieved a significantly higher performance with reduced energy consumption than GPUs and existing DNN accelerators. In this work, we reveal that DNNs execution on geometrically adjacent points exhibits similar values and relations, and exhibits a large amount of redundant computation and communication due to the correlations. To address this issue, we propose GDPCA, a geometry-aware differential point cloud accelerator, which can exploit geometric similarity to reduce these redundancies for point cloud neural networks. GDPCA is supported by an algorithm and architecture co-design. Our proposed algorithm can discover and reduce computation and communication redundancies with geometry-aware and differential execution mechanisms. Then a novel architecture is designed to support the proposed algorithm and transform the redundancy reduction into performance improvement. GDPCA performs the same computations and gives the same accuracy as traditional point cloud neural networks. To the best of our knowledge, GDPCA is the first accelerator that can reduce execution redundancies for point cloud neural networks by exploiting geometric similarity. Our proposed GDPCA system gains an average of 2.9x speedup and 2.7x energy efficiency over state-of-the-art accelerators for point cloud neural networks.