An Ultra-High Performance and Scalable Optical Flow Hardware Accelerator Based on FPGA for Autonomous Driving
Ye Liu, Shuang Hao, Xiuyuan Qi, Kun Huang, Mingli Yang, Zili Huang, Yiting Li, Liang Juan Chang, Liang Zhou, Yu Long, Jun Zhou · IEEE Transactions on Circuits and Systems I Regular Papers · 2025
Optical flow plays an extremely important role in the field of computer vision and extremely high real-time performance is required especially in autonomous driving. Traditional optical flow methods generally improve the accuracy of optical flow through the image pyramid technique. Nevertheless, the incorporation of the image pyramid elevates the computational complexity. Moreover, the data relationships between pyramid layers result in strong data dependencies, which renders it difficult to accelerate via parallel processing and makes it challenging to fulfill real-time demands in practical scenarios. To address this issue, in this paper, we propose an ultra-high performance and scalable optical flow hardware accelerator based on FPGA with several techniques, including an adaptive optical flow computation technique based on dynamic direction prediction to reduce computation without accuracy degradation, a highly scalable computing architecture with configurable numbers of PEs to improve the flexibility and hardware utilization under different hardware resource constraints, and a reconfigurable pyramid-layer pipeline technique to improve performance and reduce memory size. The proposed hardware accelerator was implemented and evaluated on a Xilinx FPGA ZCU104 achieving ultra-high performance (405 FPS) while maintaining high accuracy (AEE 1.02) compared with SOTA hardware accelerators.