Point cloud diffusion models, as an emerging 3D generation method, hold broad prospects in 3D modeling, AR/VR, and so on. However, their reliance on costly full-precision neural network computations during extended denoising process limits their practical application. To address this challenge, we propose RAPID, an accelerator co-designed with a space-aware quantization method. First, RAPID uses K-means to partition points into groups and computes scaling factors in each, mitigating accuracy issues caused by uneven distribution. Second, it employs a mixed-precision quantization scheme that uses low precision for internal point groups and high precision for detail-rich edge groups, ensuring generation quality while minimizing bit-width. Third, it reuses computation results for groups with little change between timesteps, reducing redundant calculations. Moreover, RAPID’s hardware features a mixed-precision PE array for efficient computations at various bit-widths, and a filter for dynamic bit-width allocation and result reuse. Evaluations show that, compared to the NVIDIA RTX A5000 GPU and state-of-the-art accelerators, RAPID achieves average speedups of 9.22×, 4.66×, 3.69×, and 3.01×, and energy savings of 61.74×, 4.30×, 3.94×, and 2.76×, with negligible accuracy loss.
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