Real-time multi-task diffractive deep neural networks via hardware-software co-design.

ABSTRACT: Deep neural networks (DNNs) have substantial computational requirements, which greatly limit their performance in resource-constrained environments. Recently, there are increasing efforts on optical neural networks and optical computing based DNNs hardware, which bring significant advantages for deep learning systems in terms of their power efficiency, parallelism and computational speed. Among them, free-space diffractive deep neural networks (D²NNs) based on the light diffraction, feature millions of neurons in each layer interconnected with neurons in neighboring layers. However, due to the challenge of implementing reconfigurability, deploying different DNNs algorithms requires re-building and duplicating the physical diffractive systems, which significantly degrades the har

SUBMITTER: Li Y 

PROVIDER: S-EPMC8155121 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature