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Scientific Computing with Diffractive Optical Neural Networks
Diffractive optical neural networks (DONNs) have been emerging as a high-throughput and energy-efficient hardware platform to perform all-optical machine learning (ML) in machine vision systems.
Physics-aware Differentiable Discrete Codesign for Diffractive Optical Neural Networks
Diffractive optical neural networks (DONNs) have attracted lots of attention as they bring significant advantages in terms of power efficiency, parallelism, and computational speed compared with conventional deep neural networks (DNNs), which have intrinsic limitations when implemented on digital platforms.
Differentiable Discrete Device-to-System Codesign for Optical Neural Networks via Gumbel-Softmax
Specifically, Gumbel-Softmax with a novel complex-domain regularization method is employed to enable differentiable one-to-one mapping from discrete device parameters into the forward function of DONNs, where the physical parameters in DONNs can be trained by simply minimizing the loss function of the ML task.
Real-time Multi-Task Diffractive Deep Neural Networks via Hardware-Software Co-design
Deep neural networks (DNNs) have substantial computational requirements, which greatly limit their performance in resource-constrained environments.
