C2IM-NN: A Low-Power 3D Point Clouds Matching Processor With 1D-CNN Prediction and CAM-Based In-Memory k-NN Searching

This paper presents a content addressable memory (CAM)-based computing-in-memory (C2IM) system designed for energy-efficient k-nearest neighbor (k-NN) searching in 3D point clouds. For autonomous driving applications, an essential process for perceiving the mobile robot’s movements in 3D space is k-NN searching. Especially with the limited hardware resources of mobile processors, the 3D point cloud is too large to upload onto the chip, leading to $O(N^{2})$ of external memory accesses and distance calculations. The proposed C2IM processor enhances energy efficiency and reduces power consumption through three key features: 1) Dilated 1D-CNN prediction enables voxel-based partitioning, reducing the external memory accesses from $O(N^{2})$ to $O(N)$ ; 2) Vertex clustering reorganizes groups of points into evenly distributed clusters based on the underlying data distribution and reduces the number of points of comparisons by 49.8%; and 3) In-memory k-NN searching with CAM achieves high system energy efficiency while minimizing data transactions between memory and computation logic. Designed with 28 nm CMOS technology, the proposed C2IM achieves up to $23.08\times $ energy efficiency, and 48.4% reduction in memory footprint compared to previous ASIC accelerators, and a 99.51% reduction in power consumption compared to state-of-the-art processor implemented in FPGA with high-bandwidth memory.