Afifi, Salma, authorSunny Febin, authorNikdast, Mahdi, authorPasricha, Sudeep, authorACM, publisher2024-11-112024-11-112023-06-05Salma Afifi, Febin Sunny, Mahdi Nikdast and Sudeep Pasricha. 2023. TRON: Transformer Neural Network Acceleration with Non-Coherent Silicon Photonics. In Proceedings of the Great Lakes Symposium on VLSI 2023 (GLSVLSI '23), June 5–7, 2023, Knoxville, TN, USA. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3583781.3590259.https://hdl.handle.net/10217/239519Transformer 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 14× better throughput and 8× better energy efficiency, in comparison to state-of-the-art transformer accelerators.born digitalarticleseng© Salma Afifi, et al. | ACM 2023. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in GLSVLSI '23, https://dx.doi.org/10.1145/3583781.3590259.photonic computingtransformer neural networkinference accelerationoptical computingTexthttps://doi.org/10.1145/3583781.3590259