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AnalogVNN: A Fully Modular Framework for Photonic Analog Neural Networks
Optimal hyperparameters and inference accuracy for analog-based deep learning hardware is highly dependent on system architecture and component noise. We present AnalogVNN as a fully modular framework to easily model and train arbitrary analog photonic neural networks using the simple modular layer structures of PyTorch.
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