Autonomous Driving (AD) systems extensively manipulate 3D point clouds for object detection and vehicle localization. Thereby, efficient processing of 3D point clouds is crucial in these systems. In this work we propose K-D Bonsai, a technique to cut down memory usage during radius search, a critical building block of point cloud processing. K-D Bonsai exploits value similarity in the data structure that holds the point cloud (a k-d tree) to compress the data in memory. K-D Bonsai further compresses the data using a reduced floating-point representation, exploiting the physically limited range of point cloud values. For easy integration into nowadays systems, we implement K-D Bonsai through Bonsai-extensions, a small set of new CPU instructions to compress, decompress, and operate on points. To maintain baseline safety levels, we carefully craft the Bonsai-extensions to detect precision loss due to compression, allowing re-computation in full precision to take place if necessary. Therefore, K-D Bonsai reduces data movement, improving performance and energy efficiency, while guaranteeing baseline accuracy and programmability. We evaluate K-D Bonsai over the euclidean cluster task of Autoware.ai, a state-of-the-art software stack for AD. We achieve an average of 9.26% improvement in end-to-end latency, 12.19% in tail latency, and a reduction of 10.84% in energy consumption. Differently from expensive accelerators proposed in related work, K-D Bonsai improves radius search with minimal area increase (0.36%).

翻译：自动驾驶系统广泛处理三维点云以实现目标检测与车辆定位。在此类系统中，高效处理三维点云至关重要。本文提出K-D Bonsai技术，通过减少半径搜索（点云处理的关键构建模块）中的内存占用来实现优化。K-D Bonsai利用存储点云的数据结构（k-d树）中的数值相似性压缩内存数据，并进一步采用降低精度的浮点表示法压缩数据，充分利用点云数值在物理上的有限范围。为便于集成至现有系统，我们通过Bonsai扩展（一组用于压缩、解压和操作点云的新增CPU指令集）实现K-D Bonsai。为维持基准安全水平，我们精心设计Bonsai扩展以检测压缩导致的精度损失，并在必要时允许重新以全精度计算。因此，K-D Bonsai在保证基准精度与可编程性的同时，通过减少数据移动提升性能与能效。基于当代自动驾驶软件栈Autoware.ai的欧几里得聚类任务进行评估，我们实现了端到端延迟平均降低9.26%、尾延迟降低12.19%、能耗减少10.84%。与相关研究中提出的高开销加速器不同，K-D Bonsai以极小的面积增量（0.36%）优化了半径搜索性能。