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

K-D Bonsai: ISA-Extensions to Compress K-D Trees for Autonomous Driving Tasks