Nonlinear processing with linear optics and optical generative AI

As artificial intelligence (AI) use grows, the demand for computing resources has increased dramatically. For example, a large language model like ChatGPT, requires a large amount of computing power to operate. The server cost alone is $100,000 per day as it performs calculations on tens of thousands graphical processors (GPU). These costs are significant and highlight the need for more energy-efficient and cost-effective computing platforms for AI applications and future growth. Optical transceiver technology products using silicon photonics has been announced in March 2025 by NVIDIA to communicate between GPUs with more power efficiency and higher speed. In addition to communication, can Optics also be used efficiently to compute ? Optical computing hardware has several advantages, such as high bandwidth parallelism and energy efficiency. However, one major limitation is the implementation of nonlinear calculations in the optical domain. Current solutions for nonlinearity require the use of digital computers in conjunction with linear optical hardware, which is costly and inefficient in terms of energy consumption due to the costly and power-consuming Optical-Electrical-Optical (