Published Paper Details:

The increasing complexity of artificial intelligence models has significantly amplified the demand for high-performance computing systems. Conventional electronic processors such as CPUs and GPUs face critical limitations related to power consumption, heat generation, and data movement bottlenecks. These challenges restrict the scalability of modern deep learning applications. Photonic Neural Networks (PNNs) have emerged as a promising alternative that utilizes optical signals instead of electrical currents to perform neural computations. By exploiting the high speed and parallel nature of light propagation, photonic systems can perform matrix operations with extremely low latency and energy consumption. This paper explores the architecture and operational principles of hybrid photonic neural networks designed for ultra-fast AI acceleration. The study discusses optical matrix multiplication, silicon photonic integration, system advantages, and technical challenges associated with photonic AI hardware. The analysis indicates that hybrid photonic computing architectures can significantly enhance computational speed while reducing energy usage compared to conventional electronic processors. These capabilities make photonic neural networks a promising technology for next-generation artificial intelligence systems.