FPGA-Optimized Hardware Accelerator for Fast Fourier Transform and Singular Value Decomposition in AI
Hong Ding, Chia Chao Kang, Suyang Xi, Zehang Liu, Xuan Zhang, Yi Ding · 2024
This research introduces an FPGA-based hardware accelerator to optimize the Singular Value Decomposition (SVD) and Fast Fourier transform (FFT) operations in AI models. The proposed design aims to improve processing speed and reduce computational latency. Through experiments, the paper validates the performance benefits of the hardware accelerator and shows how well it handles FFT and SVD operations. With its strong security and durability, the accelerator design achieves significant speedups over software implementations based on its modules for data flow control, watermark embedding, FFT and SVD.