Presentation
KCD-CAM: A K-Nearest-Neighbor Accelerator Based on Charge-Domain Content-Addressable-Memory for Point Cloud Processing
DescriptionThree-dimensional (3D) point cloud models have been widely employed in modern 3D perception tasks such as robotics, autonomous driving, and virtual reality. The k-nearest-neighbor (KNN) search serves as a cornerstone operation for point cloud models, providing the essential mechanism for defining and exploiting local spatial relationships within unstructured data. However, the massive scale of point cloud data and the high computational complexity of Euclidean distance-based top-k searches in KNN impose substantial computational overhead. Conventional edge computing platforms struggle to achieve real-time and high efficiency of point cloud processing. In this work, we propose KCD-CAM, a KNN accelerator using ReRAM-based charge-domain content-addressable memory (CAM) for efficient point cloud processing. The proposed KCD-CAM employs a 4T2R CAM cell capable of performing in-situ range search, effectively replacing complex Euclidean distance calculations with massively parallel operations. In addition, corner-clipped (CC) iterative top-k search scheme and dual-granularity voxel hashing (DG-VH) are employed to enhance accuracy and parallelism. Performance benchmarks in real-world datasets demonstrate that KCD-CAM achieves 279.79× higher speed and 3282× greater energy efficiency than GPU implementations. Compared to the SOTA KNN accelerators, our KCD-CAM also achieved 8.51× and 4.76× improvements in speed and energy efficiency, respectively.
Event Type
Research Manuscript
TimeMonday, July 2711:36am - 11:50am PDT
LocationMtg Room 203AB
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