QuickNN: Memory and Performance Optimization of k-d Tree Based Nearest Neighbor Search for 3D Point Clouds

The use of Light Detection And Ranging (LiDAR) has enabled the continued improvement in accuracy and performance of autonomous navigation. The latest applications require LiDAR's of the highest spatial resolution, which generate a massive amount of 3D point clouds that need to be processed in real time. In this work, we investigate the architecture design for k-Nearest Neighbor (kNN) search, an important processing kernel for 3D point clouds. An approximate kNN search based on a k-dimensional (k-d) tree is employed to improve performance. However, even for today's moderate-sized problems, this approximate kNN search is severely hindered by memory bandwidth due to numerous random accesses and minimal data reuse opportunities. We apply several memory optimization schemes to alleviate the bandwidth bottleneck: 1) the k-d tree data structure is partitioned to two sets: tree nodes and point buckets, based on their distinct characteristics – tree nodes that have high reuse are cached for their lifetime to facilitate search, while point buckets with low reuse are organized in regular contiguous segments in external memory to facilitate efficient burst access; 2) write and read caches are added to gather random accesses to transform them to sequential accesses; and 3) tree construction and tree search are interleaved to cut redundant access streams. With optimized memory bandwidth, the kNN search can be further accelerated by two new processing schemes: 1) parallel tree traversal that utilizes multiple workers with minimal tree duplication overhead, and 2) incremental tree building that minimizes the overhead of tree construction by dynamically updating the tree instead of building it from scratch every time. We demonstrate the performance and memory-optimized QuickNN architecture on FPGA and perform exhaustive benchmarking, showing that up to a 19× and 7.3× speedup over k-d tree searches performed on a modern CPU and GPU, respectively, and a 14.5× speedup over a comparable sized architecture performing an exact search. Finally, we show that QuickNN achieves two orders of magnitude performance per watt increase over CPU and GPU methods.