Deploying Diffusion Models with Scheduling Space Search and Memory Overflow Prevention Based on Graph Optimization
Hao Zhou, Yang Liu, Hongji Wang, Enhao Tang, Shun Li, Yifan Zhang, Guohao Dai, Yongpan Liu, Kun Wang · 2025
In recent years, Neural Networks developed rapidly to deal with tasks in the field of Computer Vision and Natural Language Process, etc. With the development of AI Generated Content, U-Net based Diffusion Models (DM) take image synthesis to new heights. U-Net performs the noise prediction of DM, the latency of which accounts for the majority of the end-to-end latency. Although FPGA has been proven to be a high performance platform to deploy NN, a series of facts still pose challenges for efficient U-Net based DMs deployment based on FPGA. The input vector length and type of the special function vary between different layers. The absence of model periodicity increases the granularity and complexity of operator scheduling. Skip-connection and residual connection inside model cause meta-data retaining in the memory, which is not conductive to avoiding memory overflow and decreasing total off-chip memory access.