PhotoGAN: Generative Adversarial Neural Network Acceleration with Silicon Photonics

TL;DR

PhotoGAN is a silicon-photonic accelerator specifically designed for GANs, significantly improving performance and energy efficiency over traditional electronic accelerators by leveraging photonics and sparse computation optimization.

Contribution

This paper introduces PhotoGAN, the first silicon-photonic accelerator tailored for GAN operations, achieving substantial performance and energy efficiency improvements.

Findings

PhotoGAN achieves at least 4.4x higher GOPS than state-of-the-art accelerators.

PhotoGAN reduces energy-per-bit by 2.18x compared to GPUs and TPUs.

The architecture effectively accelerates GAN-specific layers like transposed convolutions.

Abstract

Generative Adversarial Networks (GANs) are at the forefront of AI innovation, driving advancements in areas such as image synthesis, medical imaging, and data augmentation. However, the unique computational operations within GANs, such as transposed convolutions and instance normalization, introduce significant inefficiencies when executed on traditional electronic accelerators, resulting in high energy consumption and suboptimal performance. To address these challenges, we introduce PhotoGAN, the first silicon-photonic accelerator designed to handle the specialized operations of GAN models. By leveraging the inherent high throughput and energy efficiency of silicon photonics, PhotoGAN offers an innovative, reconfigurable architecture capable of accelerating transposed convolutions and other GAN-specific layers. The accelerator also incorporates a sparse computation optimization technique to reduce redundant operations, improving computational efficiency. Our experimental results demonstrate that PhotoGAN achieves at least 4.4x higher GOPS and 2.18x lower energy-per-bit (EPB) compared to state-of-the-art accelerators, including GPUs and TPUs. These findings showcase PhotoGAN as a promising solution for the next generation of GAN acceleration, providing substantial gains in both performance and energy efficiency.