  Generative algorithms such as GANs are at the cusp of next revolution in the
field of unsupervised learning and large-scale artificial data generation.
However, the adversarial (competitive) co-training of the discriminative and
generative networks in GAN makes them computationally intensive and hinders
their deployment on the resource-constrained IoT edge devices. Moreover, the
frequent data transfer between the discriminative and generative networks
during training significantly degrades the efficacy of the von-Neumann GAN
accelerators such as those based on GPU and FPGA. Therefore, there is an urgent
need for development of ultra-compact and energy-efficient hardware
accelerators for GANs. To this end, in this work, we propose to exploit the
passive RRAM crossbar arrays for performing key operations of a fully-connected
GAN: (a) true random noise generation for the generator network, (b)
vector-by-matrix-multiplication with unprecedented energy-efficiency during the
forward pass and backward propagation and (C) in-situ adversarial training
using a hardware friendly Manhattan's rule. Our extensive analysis utilizing an
experimentally calibrated phenomological model for passive RRAM crossbar array
reveals an unforeseen trade-off between the accuracy and the energy dissipated
while training the GAN network with different noise inputs to the generator.
Furthermore, our results indicate that the spatial and temporal variations and
true random noise, which are otherwise undesirable for memory application,
boost the energy-efficiency of the GAN implementation on passive RRAM crossbar
arrays without degrading its accuracy.
