Panel-Scale Reconfigurable Photonic Interconnects for Scalable AI Computation
Tzu-Chien Hsueh, Bill Lin, Zijun Chen, Yeshaiahu Fainman

TL;DR
This paper proposes a novel, large-scale reconfigurable photonic interconnect system on glass substrates that enables high-bandwidth, low-energy optical data links for scalable AI computing, integrating advanced photonic components.
Contribution
It introduces a new panel-scale photonic switch fabric enabling all-directional inter-panel communication without active repeaters, suitable for heterogeneous AI system integration.
Findings
Supports panel-edge-to-panel-edge communication over large areas
Achieves high data bandwidth density with WDM optical links
Offers low energy consumption and reconfigurability
Abstract
Panel-scale reconfigurable photonic interconnects on a glass substrate up to 500-mm x 500-mm or larger are envisioned by proposing a novel photonic switch fabric that enables all directional panel-edge-to-panel-edge reach without the need for active repeaters while offering high communication bandwidth, planar-direction reconfigurability, low energy consumption, and compelling data bandwidth density for heterogeneous integration of an in-package AI computing system on a single glass-substrate photonic interposer exceeding thousands of centimeters square. The proposed approach focuses on reconfigurable photonic interconnects, which are integration-compatible with commercial processor chiplets and 3D high-bandwidth memory (HBM) stacks on a large-area glass substrate, to create a novel panel-scale heterogeneously integrated interposer or package enabling low-energy and high-capacity…
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Taxonomy
TopicsPhotonic and Optical Devices · Neural Networks and Reservoir Computing · Photonic Crystals and Applications
