COPIC: A Codesigned Accelerator for Efficient Octree-Based Learned Point Cloud Compression at Edge

With the growing adoption of light detection and ranging (LiDAR) technology and the increasing resolution of captured data, efficient point cloud compression (PCC) has become a critical challenge. While PCC is essential for reducing bandwidth and storage demands, existing octree-based learned PCC methods face two fundamental bottlenecks: 1) octree construction and serialization that involve irregular, pointwise memory accesses that are difficult to parallelize and 2) autoregressive entropy model inference that requires heavy computation. These issues lead to high latency and energy consumption, making real-time deployment on edge devices impractical. This article introduces COPIC, a hardware accelerator designed for real-time PCC on edge devices. COPIC integrates two key components. The first is OctCAM, a content-addressable-memory (CAM)-based module for rapid octree construction and serialization. By enabling hardware-level parallel search, OctCAM significantly reduces the latency and energy consumption caused by irregular memory access patterns in octree processing. The second is PCC-Engine, a dedicated neural network inference module. Combined with a lightweight PCC-TinyAttention network and a window-based key-value caching approach, it further minimizes execution latency and power usage. Experimental results show that COPIC achieves a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$25.9\times $</tex-math> </inline-formula> speedup compared to GPU implementations, with a total processing latency of 40.1 ms and an average energy consumption of 0.23 J per frame, while delivering a compression rate 12.4% higher than standard proposals.