Point-X: A Spatial-Locality-Aware Architecture for Energy-Efficient Graph-Based Point-Cloud Deep Learning

Abstract

Deep learning on point clouds has attracted increasing attention in the fields of 3D computer vision and robotics. In particular, graph-based point-cloud deep neural networks (DNNs) have demonstrated promising performance in 3D object classification and scene segmentation tasks. However, the scattered and irregular graph-structured data in a graph-based point-cloud DNN cannot be computed efficiently by existing SIMD architectures and accelerators. We present Point-X, an energy-efficient accelerator architecture that extracts and exploits the spatial locality in point cloud data for efficient processing. Point-X uses a clustering method to extract fine-grained and coarse-grained spatial locality from the input point cloud. The clustering maps the point cloud into distributed compute tiles to maximize intra-tile computational parallelism and minimize inter-tile data movement. Point-X employs a chain network-on-chip (NoC) to further reduce the NoC traffic and achieve up to 3.2 × speedup over a traditional mesh NoC. Point-X’s multi-mode dataflow can support all common operations in a graph-based point-cloud DNN, i.e., edge convolution, shared multi-layer perceptron, and fully-connected layers. Point-X is synthesized in a 28nm technology and it demonstrates a throughput of 1307.1 inference/s and an energy efficiency of 604.5 inference/J on the DGCNN workload. Compared to the Nvidia GTX-1080Ti GPU, Point-X shows 4.5 × and 342.9 × improvement in throughput and efficiency, respectively.