Space-efficient optical computing with an integrated chip diffractive neural network.

ABSTRACT: Large-scale, highly integrated and low-power-consuming hardware is becoming progressively more important for realizing optical neural networks (ONNs) capable of advanced optical computing. Traditional experimental implementations need N² units such as Mach-Zehnder interferometers (MZIs) for an input dimension N to realize typical computing operations (convolutions and matrix multiplication), resulting in limited scalability and consuming excessive power. Here, we propose the integrated diffractive optical network for implementing parallel Fourier transforms, convolution operations and application-specific optical computing using two ultracompact diffractive cells (Fourier transform operation) and only N MZIs. The footprint and energy consumption scales linearly with the input da

SUBMITTER: Zhu HH 

PROVIDER: S-EPMC8873412 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature