Asymmetrical estimator for training encapsulated deep photonic neural networks.

ABSTRACT: Photonic neural networks (PNNs) are fast in-propagation and high bandwidth paradigms that aim to popularize reproducible NN acceleration with higher efficiency and lower cost. However, the training of PNN is known to be challenging, where the device-to-device and system-to-system variations create imperfect knowledge of the PNN. Despite backpropagation (BP)-based training algorithms being the industry standard for their robustness, generality, and fast gradient convergence for digital training, existing PNN-BP methods rely heavily on accurate intermediate state extraction or extensive computational resources for deep PNNs (DPNNs). The truncated photonic signal propagation and the computation overhead bottleneck DPNN's operation efficiency and increase system construction cost. Here, we int

SUBMITTER: Wang Y 

PROVIDER: S-EPMC11876688 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature