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.
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