Sustainable Acceleration of Generative AI Neural Network Models with Silicon Photonics

Generative AI models such as Generative Adversarial Networks (GANs) and Diffusion Models (DMs), have demonstrated remarkable capabilities in producing high-quality synthetic data for applications ranging from image synthesis and medical imaging to data augmentation. However, the complex model architectures and unique computational operations pose significant challenges for traditional electronic accelerators. To address energy/sustainability bottlenecks with conventional electronic hardware, we present a novel silicon photonic accelerator targeting both GANs and DMs. Experimental evaluations show that our photonic accelerator achieves at least 2.18× lower energy consumption and at least 4.4× throughout improvement compared to several state-of-theart CPU, GPU, FPGA, ReRAM, and ASIC-based accelerators.