ApGAN: Approximate GAN for Robust Low Energy Learning from Imprecise Components

Arman Roohi, Shadi Sheikhfaal, Shaahin Angizi, Deliang Fan, Ronald F. Demara

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


A Generative Adversarial Network (GAN) is an adversarial learning approach which empowers conventional deep learning methods by alleviating the demands of massive labeled datasets. However, GAN training can be computationally-intensive limiting its feasibility in resource-limited edge devices. In this paper, we propose an approximate GAN (ApGAN) for accelerating GANs from both algorithm and hardware implementation perspectives. First, inspired by the binary pattern feature extraction method along with binarized representation entropy, the existing Deep Convolutional GAN (DCGAN) algorithm is modified by binarizing the weights for a specific portion of layers within both the generator and discriminator models. Further reduction in storage and computation resources is achieved by leveraging a novel hardware-configurable in-memory addition scheme, which can operate in the accurate and approximate modes. Finally, a memristor-based processing-in-memory accelerator for ApGAN is developed. The performance of the ApGAN accelerator on different data-sets such as Fashion-MNIST, CIFAR-10, STL-10, and celeb-A is evaluated and compared with recent GAN accelerator designs. With almost the same Inception Score (IS) to the baseline GAN, the ApGAN accelerator can increase the energy-efficiency by sim 28.6×∼28.6× achieving 35-fold speedup compared with a baseline GPU platform. Additionally, it shows 2.5× and 5.8× higher energy-efficiency and speedup over CMOS-ASIC accelerator subject to an 11 percent reduction in IS.

Original languageEnglish (US)
Article number8880521
Pages (from-to)349-360
Number of pages12
JournalIEEE Transactions on Computers
Issue number3
StatePublished - Mar 1 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computational Theory and Mathematics


  • Generative adversarial network
  • hardware mapping
  • in-memory processing platform
  • neural network acceleration


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