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This content will become publicly available on July 9, 2025

Title: Optical Comb-Based Monolithic Photonic-Electronic Accelerators for Self-Attention Computation
This paper adopts advanced monolithic silicon-photonics integrated-circuits manufacturing capabilities to realize system-on-chip photonic-electronic linear-algebra accelerators for self-attention computation in various applications of deep-learning neural networks and Large Language Models. With the features of holistic co-design approaches, optical comb-based broadband modulations, and consecutive matrix-multiplication architecture, the system/circuit/device-level simulations of the proposed accelerator can achieve 2.14-TMAC/s/mm2 computation density and 27.9-fJ/MAC energy efficiency with practical considerations of power/area overhead due to photonic-electronic on-chip conversions, integrations, and calibrations.  more » « less
Award ID(s):
2023730 2410053 2217453 2045935
PAR ID:
10553811
Author(s) / Creator(s):
; ;
Corporate Creator(s):
Editor(s):
Capmany, José
Publisher / Repository:
IEEE
Date Published:
Journal Name:
IEEE Journal of Selected Topics in Quantum Electronics
Edition / Version:
1
Volume:
30
Issue:
5
ISSN:
1077-260X
Page Range / eLocation ID:
1-17
Subject(s) / Keyword(s):
Frequency comb, large language model, linear algebra, matrix-matrix multiplication, matrix-vector multiplication, micro-resonator, monolithic integration, racetrack resonator, self-attention, silicon photonics, transformer model.
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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