Spatially varying nanophotonic neural networks.

ABSTRACT: The explosive growth in computation and energy cost of artificial intelligence has spurred interest in alternative computing modalities to conventional electronic processors. Photonic processors, which use photons instead of electrons, promise optical neural networks with ultralow latency and power consumption. However, existing optical neural networks, limited by their designs, have not achieved the recognition accuracy of modern electronic neural networks. In this work, we bridge this gap by embedding parallelized optical computation into flat camera optics that perform neural network computations during capture, before recording on the sensor. We leverage large kernels and propose a spatially varying convolutional network learned through a low-dimensional reparameterization. We instanti

SUBMITTER: Wei K 

PROVIDER: S-EPMC11546815 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature