All-optical graph representation learning using integrated diffractive photonic computing units.

ABSTRACT: Photonic neural networks perform brain-inspired computations using photons instead of electrons to achieve substantially improved computing performance. However, existing architectures can only handle data with regular structures but fail to generalize to graph-structured data beyond Euclidean space. Here, we propose the diffractive graph neural network (DGNN), an all-optical graph representation learning architecture based on the diffractive photonic computing units (DPUs) and on-chip optical devices to address this limitation. Specifically, the graph node attributes are encoded into strip optical waveguides, transformed by DPUs, and aggregated by optical couplers to extract their feature representations. DGNN captures complex dependencies among node neighborhoods during the light-speed o

SUBMITTER: Yan T 

PROVIDER: S-EPMC9200271 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature