  This paper proposes 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 optical
comb-based broadband incoherent photo-detections and high-dimensional
operations of consecutive matrix-matrix multiplications to enable 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.
