ChatGPT at the speed of light: Monolithic photonic-electronic linear-algebra accelerators for large language models

Tzu-Chien Hsueh; Yeshaiahu Fainman; Bill Lin

doi:10.1117/12.3027309

3 October 2024 ChatGPT at the speed of light: Monolithic photonic-electronic linear-algebra accelerators for large language models

Tzu-Chien Hsueh, Yeshaiahu Fainman, Bill Lin

Author Affiliations +

Proceedings Volume PC13113, Photonic Computing: From Materials and Devices to Systems and Applications; PC131130I (2024) https://doi.org/10.1117/12.3027309
Event: Nanoscience + Engineering, 2024, San Diego, California, United States

Abstract

This paper proposed to adopt advanced monolithic silicon-photonics integrated-circuits manufacturing capabilities to achieve a system-on-chip photonic-electronic linear-algebra accelerator with the features of broadband incoherent photo-detections and high-dimensional operations of consecutive matrix-matrix multiplications for enabling substantial leaps in computation density and energy efficiency with practical considerations of power/area overhead due to photonic-electronic on-chip conversions, integrations and calibrations through holistic co-design approaches to support attention-head mechanism based deep-learning neural networks used in Large Language Models and other emergent applications.

Conference Presentation

Citation Download Citation

Tzu-Chien Hsueh, Yeshaiahu Fainman, and Bill Lin "ChatGPT at the speed of light: Monolithic photonic-electronic linear-algebra accelerators for large language models", Proc. SPIE PC13113, Photonic Computing: From Materials and Devices to Systems and Applications, PC131130I (3 October 2024); https://doi.org/10.1117/12.3027309

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