中国邮电高校学报(英文) ›› 2023, Vol. 30 ›› Issue (4): 10-20.doi: 10.19682/j.cnki.1005-8885.2023.2012

• Artificial Intelligence • 上一篇    下一篇

Hardware-friendly Cycle-GAN and reconfigurable design

Xie Xiaoyan, Chai Miaomiao, Deng Junyong, Du Zhuolin, Yang Kun, Yin Shaorun   

  1. 1. School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, China 2. School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China 3. College of Safety Science and Engineering,Xi'an University of Science and Technology, Xi'an 710054, China
  • 收稿日期:2022-01-24 修回日期:2022-11-06 接受日期:2023-08-31 出版日期:2023-08-31 发布日期:2023-08-31
  • 通讯作者: Xie Xiaoyan, E-mail: xxy@xupt.edu.cn E-mail:xxy@xupt.edu.cn
  • 基金资助:
    This work was supported by the National Natural Science Foundation of China ( 61834005, 61772417 ), the National Science and Technology Major Project (2020AAA0104603) and the Key Research and Development Program of Shaanxi (2021GY-029 and 2022GY-027).

Hardware-friendly Cycle-GAN and reconfigurable design

Xie Xiaoyan, Chai Miaomiao, Deng Junyong, Du Zhuolin, Yang Kun, Yin Shaorun   

  1. 1. School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, China 2. School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China 3. College of Safety Science and Engineering,Xi'an University of Science and Technology, Xi'an 710054, China
  • Received:2022-01-24 Revised:2022-11-06 Accepted:2023-08-31 Online:2023-08-31 Published:2023-08-31
  • Contact: Xie Xiaoyan, E-mail: xxy@xupt.edu.cn E-mail:xxy@xupt.edu.cn
  • Supported by:
    This work was supported by the National Natural Science Foundation of China ( 61834005, 61772417 ), the National Science and Technology Major Project (2020AAA0104603) and the Key Research and Development Program of Shaanxi (2021GY-029 and 2022GY-027).

摘要: As a kind of generative adversarial network (GAN), Cycle-GAN shows an apparent superiority in image style translation. The more complicated architectures with large number of parameters and huge computational complexities, cause a big challenge in deployment on resource-constrained platform. To make full use of the parallelism of hardware under guaranteed image quality, this paper improves the generator network to a hardware-friendly Inception module. The optimized framework is named simplified Cycle-GAN (S-CycleGAN), with greatly reduced parameters of convolution, while avoiding the degradation of image quality from structural compression. Testing with the apple2organge and horse2zebra datasets, the experiment results show that the images generated by S-CycleGAN outperform the baseline and other models. The number of parameters reduces by 19.54%, memory usage cuts down by 9.11%, theoretical amount of multiply-adds (Madds) decreases by 17.96%, and floating-point operations per second (FLOPS) diminishes by 18.91%. Finally, the S-CycleGAN was mapped on the dynamic programmable reconfigurable array processor ( DPRAP ), which calculate the convolution and deconvolution in a unified architecture, and support flexible runtime switching. The prototype systems are implemented on Xilinx field programmable gate array (FPGA) XC6 VLX550 T-FF1759. The synthesized results show that, with 150 MHz, the hardware resource consumption is reduced by 52% compared to the recent FPGA scheme.

关键词: Cycle-GAN, generator, Inception, hardware-friendly, reconfiguration

Abstract: As a kind of generative adversarial network (GAN), Cycle-GAN shows an apparent superiority in image style translation. The more complicated architectures with large number of parameters and huge computational complexities, cause a big challenge in deployment on resource-constrained platform. To make full use of the parallelism of hardware under guaranteed image quality, this paper improves the generator network to a hardware-friendly Inception module. The optimized framework is named simplified Cycle-GAN (S-CycleGAN), with greatly reduced parameters of convolution, while avoiding the degradation of image quality from structural compression. Testing with the apple2organge and horse2zebra datasets, the experiment results show that the images generated by S-CycleGAN outperform the baseline and other models. The number of parameters reduces by 19.54%, memory usage cuts down by 9.11%, theoretical amount of multiply-adds (Madds) decreases by 17.96%, and floating-point operations per second (FLOPS) diminishes by 18.91%. Finally, the S-CycleGAN was mapped on the dynamic programmable reconfigurable array processor ( DPRAP ), which calculate the convolution and deconvolution in a unified architecture, and support flexible runtime switching. The prototype systems are implemented on Xilinx field programmable gate array (FPGA) XC6 VLX550 T-FF1759. The synthesized results show that, with 150 MHz, the hardware resource consumption is reduced by 52% compared to the recent FPGA scheme.

Key words: Cycle-GAN, generator, Inception, hardware-friendly, reconfiguration