  We consider the task of photo-realistic unconditional image generation
(generate high quality, diverse samples that carry the same visual content as
the image) on mobile platforms using Generative Adversarial Networks (GANs). In
this paper, we propose a novel approach to trade-off image generation accuracy
of a GAN for the energy consumed (compute) at run-time called Scale-Energy
Tradeoff GAN (SETGAN). GANs usually take a long time to train and consume a
huge memory hence making it difficult to run on edge devices. The key idea
behind SETGAN for an image generation task is for a given input image, we train
a GAN on a remote server and use the trained model on edge devices. We use
SinGAN, a single image unconditional generative model, that contains a pyramid
of fully convolutional GANs, each responsible for learning the patch
distribution at a different scale of the image. During the training process, we
determine the optimal number of scales for a given input image and the energy
constraint from the target edge device. Results show that with SETGAN's unique
client-server-based architecture, we were able to achieve a 56% gain in energy
for a loss of 3% to 12% SSIM accuracy. Also, with the parallel multi-scale
training, we obtain around 4x gain in training time on the server.
