Image Synthesis Using Spintronic Deep Convolutional Generative Adversarial Network

TL;DR

This paper introduces a hybrid CMOS-spintronic DCGAN architecture for energy-efficient synthetic image generation, integrating spintronic hardware with neural network components to improve performance and reduce energy consumption.

Contribution

It presents a novel spintronic hardware implementation of DCGAN, including restructured deconvolution layers and hybrid CMOS spintronic activation functions, enabling efficient image synthesis.

Findings

Achieved FID scores of 27.5 for Fashion MNIST and 45.4 for Anime Face datasets.

Demonstrated low energy consumption of 0.192 pJ for activation functions.

Enabled adaptability to grayscale and colored datasets.

Abstract

The computational requirements of generative adversarial networks (GANs) exceed the limit of conventional Von Neumann architectures, necessitating energy efficient alternatives such as neuromorphic spintronics. This work presents a hybrid CMOS-spintronic deep convolutional generative adversarial network (DCGAN) architecture for synthetic image generation. The proposed generative vision model approach follows the standard framework, leveraging generator and discriminators adversarial training with our designed spintronics hardware for deconvolution, convolution, and activation layers of the DCGAN architecture. To enable hardware aware spintronic implementation, the generator's deconvolution layers are restructured as zero padded convolution, allowing seamless integration with a 6-bit skyrmion based synapse in a crossbar, without compromising training performance. Nonlinear activation functions are implemented using a hybrid CMOS domain wall based Rectified linear unit (ReLU) and Leaky ReLU units. Our proposed tunable Leaky ReLU employs domain wall position coded, continuous resistance states and a piecewise uniaxial parabolic anisotropy profile with a parallel MTJ readout, exhibiting energy consumption of 0.192 pJ. Our spintronic DCGAN model demonstrates adaptability across both grayscale and colored datasets, achieving Fr'echet Inception Distances (FID) of 27.5 for the Fashion MNIST and 45.4 for Anime Face datasets, with testing energy (training energy) of 4.9 nJ (14.97~nJ/image) and 24.72 nJ (74.7 nJ/image).