A Low-power 3D Point Clouds Matching Processor with 1D-CNN Prediction and CAM-based In-memory kNN Searching

This paper presents a computing-CAM-in-memory (C
²
IM) system designed for energy-efficient k-nearest neighbor (kNN) operations within 3D point clouds, which is an essential process for autonomous driving applications. In mobile processors, a new paradigm is required to conduct 3D point cloud kNN searching, under the limited hardware resources. Therefore, three features are proposed: i) Prediction using a dilated 1D-CNN, which enables voxel-based partitioning by minimizing the external memory accesses from O(n
²
) to O(n). ii) Vertex clustering, which restructures groups of points into evenly distributed clusters based on the underlying data distribution and effectively reduces the number of points for comparison by 49.8%. iii) Data movement minimization through in-memory kNN with content addressable memory (CAM). Designed with 28 nm CMOS technology, the proposed C
²
IM achieves reductions of 2.07× in memory footprint and 206.68× in power consumption, compared to state-of-the-art (SOTA) architectures.