Accelerating Neural Networks for Large Language Models and Graph
  Processing with Silicon Photonics
Salma Afifi Febin Sunny Mahdi Nikdast Sudeep Pasricha
In the rapidly evolving landscape of artificial intelligence, large language
models (LLMs) and graph processing have emerged as transformative technologies
for natural language processing (NLP), computer vision, and graph-structured
data applications. However, the complex structures of these models pose
challenges for acceleration on conventional electronic platforms. In this
paper, we describe novel hardware accelerators based on silicon photonics to
accelerate transformer neural networks that are used in LLMs and graph neural
networks for graph data processing. Our analysis demonstrates that both
hardware accelerators achieve at least 10.2x throughput improvement and 3.8x
better energy efficiency over multiple state-of-the-art electronic hardware
accelerators designed for LLMs and graph processing.