Methodology for Efficient Reconfigurable Architecture of Generative Neural Network

Generative neural networks have been developing rapidly in the field of deep learning nowadays. Generative models have obtained much popularity in various applications such as image generation, reading comprehension and style transfer. Convolutional (CONV) and deconvolutional (DeCONV) layers are typical components of generative neural networks. The use of traditional convolution accelerators will cause problems of overlapping and resource under-utilization while doing deconvolutions. There is little research on acceleration of deconvolution implementations. In this paper, we propose efficient reconfigurable architecture of generative neural networks. Firstly, the fast reconfigurable unit (FRU) based on cascaded fast FIR algorithm (CFFA) is proposed to support both convolutions and deconvolutions. The problems of overlapping and resource under-utilization are solved. Secondly, the reconfigurable architecture on the basis of FRUs for CONV and DeCONV layers is proposed accordingly. Thirdly, a novel shift scale quantization method is proposed to uniformly quantize CONV and DeCONV layers. Only integer computations are required with the quantization method. Finally, we choose a typical generative neural network and implement it on Xilinx Zynq ZC706. It is estimated that the performance reaches 62.85 GOPS under 330MHz working frequency on Xilinx ZC706. In brief, the proposed design outperforms existing works significantly, particularly surpasses related reconfigurable design by more than 20 times in terms of performance density.