Harnessing Photonic Computing for Next-Generation CPUs and GPUs in High-Performance Computing

Advancements in HPC for AI, scientific modeling, and simulation, as well as big data, have also brought out the shortcomings of conventional silicon-based processors. The use of optical signals as inputs rather than electrical signals provides an innovative approach to solving these issues through photonic computing. This paper, therefore takes a look at the massively enhanced architectures like the Photonic Processing Units (PPUs), photonic electronic compounding architecture, and the optical memory that is proving superior in terms of speed, energy efficiency, and bandwidth. Photonic computing facilitates optical communication through wire and integration of optical interconnects and waveguide-based computation for low energy consumption and heat dissipation. When combined with CPU and GPUs, photonic processors allow for faster computations required in such tasks as deep learning and modeling and cryptographic computations. This paper briefly describes recent experimental advances in stalked graphical models such as VCSEL-based spiking neural networks, Diffractive Optical Neural Networks (DONNs) and Orbital Angular Momentum (OAM) optical vector processing that are reported to be more efficient and computationally precise. Nevertheless, photonic computing still has problems like complexity in fabrication, integration with the current silicon models and thermal concern. However, it is easy to note that hybrid computing models and quantum-enhanced photonics offer certain ways of getting past these issues. In more ways, photonic computing is set to transform data centers, clouds and AI-based computing workloads to a new era of ultra-fast, ultra-energy efficient and scalable computation