Energy-Efficient Implementation of Generative Adversarial Networks on Passive RRAM Crossbar Arrays

TL;DR

This paper presents an energy-efficient hardware implementation of GANs using passive RRAM crossbar arrays, enabling low-power, in-situ training suitable for resource-constrained IoT devices.

Contribution

It introduces a novel approach leveraging passive RRAM crossbars for key GAN operations, including random noise generation and in-situ adversarial training, with detailed analysis of accuracy-energy trade-offs.

Findings

Passive RRAM arrays enable energy-efficient GAN operations.

True random noise and variations improve energy efficiency without accuracy loss.

Trade-off identified between training accuracy and energy consumption.

Abstract

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.