CAMPER: Exploring the Potential of Content Addressable Memory for 3D Point Cloud Efficient Range Search

The use of Light Detection and Ranging (LiDAR) for sensing has continuously improved the precision and performance of autonomous driving. At the same time, the large number of high-precision point clouds generated by LiDAR require real-time processing, and the range search is the key part of the processing pipeline. Content-Addressable Memory (CAM) has proven its efficiency for search tasks on switches and routers, but so far, there is still a lack of exploration on its application in point cloud range search. In this work, we propose CAMPER, a CAM-centered accelerator, aiming to explore the potential of CAM for point cloud range search. We developed a ripple comparison 13T (RC-13T) CAM cell for distance comparison, designed a spatial approximation search algorithm based on Chebyshev distance, and discussed the flexibility and scalability of the architecture. The results show that in the range search task of 64k@64k points, CAMPER achieves a latency of 0.83ms and a power consumption of 114.6mW. Compared with GPU, the throughput is increased by 10.4×; compared with SOTA accelerator, the energy efficiency is increased by about 228×.