  Transformer neural networks are rapidly being integrated into
state-of-the-art solutions for natural language processing (NLP) and computer
vision. However, the complex structure of these models creates challenges for
accelerating their execution on conventional electronic platforms. We propose
the first silicon photonic hardware neural network accelerator called TRON for
transformer-based models such as BERT, and Vision Transformers. Our analysis
demonstrates that TRON exhibits at least 14x better throughput and 8x better
energy efficiency, in comparison to state-of-the-art transformer accelerators.
