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Three-dimensional generative adversarial networks (3D-GAN) have attractedwidespread attention in three-dimension (3D) visual tasks. 3D deconvolution(DeConv), as an important computation of 3D-GAN, significantly increasescomputational complexity compared with 2D DeConv. 3D DeConv has become abottleneck for the acceleration of 3D-GAN. Previous accelerators suffer fromseveral problems, such as large memory requirements and resourceunderutilization. To handle the above issues, a fast algorithm for 3D DeConv(F3DC) is proposed in this paper. F3DC applies a fast algorithm to reduce thenumber of multiplications and achieves a significant algorithmic strengthreduction. Besides, F3DC removes the extra memory requirement for overlappedpartial sums and avoids computational imbalance to fully utilize resources.Moreover, we design an F3DC-based hardware architecture, which consists of fourfast processing units (FPUs). Each FPU includes a pre-process module, a EWMMmodule and a post-process module for F3DC transformation. By implementing ourdesign on the Xilinx VC709 platform for 3D-GAN, we achieve a throughput up to1700 GOPS and 4$\times$ computational efficiency improvement compared withprior works.

Accelerate Three-Dimensional Generative Adversarial Networks Using Fast Algorithm