用硅光子芯片加速生成对抗网络，性能提升4.4倍，能耗降低2.18倍。
PhotoGAN: Generative Adversarial Neural Network Acceleration with Silicon Photonics
- 采用可重构硅光子架构，专为GAN的转置卷积等操作设计。
- 相比主流加速器，算力提升4.4倍，每比特能耗降低2.18倍。
- 适合需要高能效图像生成的AI系统，如医疗影像与数据增强场景。
生成对抗网络（GAN）是人工智能创新的核心，广泛应用于图像合成、医学成像和数据增强等领域。然而，GAN中特有的转置卷积和实例归一化等计算操作在传统电子加速器上执行时效率低下，导致高能耗与性能瓶颈。为此，本文提出PhotoGAN，首个面向GAN模型专用操作的硅光子加速器。利用硅光子固有的高吞吐与低功耗优势，PhotoGAN构建了可重构架构，有效加速转置卷积及其他GAN特定层，并引入稀疏计算优化技术减少冗余运算。实验结果表明，PhotoGAN相较现有先进加速器（包括GPU和TPU）实现至少4.4倍更高的GOPs，且能耗每比特降低2.18倍。这些成果证明PhotoGAN是下一代GAN加速的有力候选方案，在性能与能效上均实现显著突破。
原文摘要 · Abstract (English)
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.
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