Training large-scale optoelectronic neural networks with dual-neuron optical-artificial learning.

ABSTRACT: Optoelectronic neural networks (ONN) are a promising avenue in AI computing due to their potential for parallelization, power efficiency, and speed. Diffractive neural networks, which process information by propagating encoded light through trained optical elements, have garnered interest. However, training large-scale diffractive networks faces challenges due to the computational and memory costs of optical diffraction modeling. Here, we present DANTE, a dual-neuron optical-artificial learning architecture. Optical neurons model the optical diffraction, while artificial neurons approximate the intensive optical-diffraction computations with lightweight functions. DANTE also improves convergence by employing iterative global artificial-learning steps and local optical-learning steps. In si

SUBMITTER: Yuan X 

PROVIDER: S-EPMC10625607 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature