In this work, we proposed a simple deep neural network architecture augmented by a physical local learning (PhyLL) algorithm, enabling supervised and unsupervised training of deep physical neural networks without detailed knowledge of the nonlinear physical layer’s properties. We trained diverse wave-based physical neural networks in vowel and image classification experiments, showcasing our approach’s universality. Our method shows advantages over other hardware-aware training schemes by improving training speed, enhancing robustness, and reducing power consumption through eliminating the need for system modeling and thus decreasing digital computation.
If you use this code for your research, please cite the following work:
@article{momeni2023backpropagation,
title={Backpropagation-free Training of Deep Physical Neural Networks},
author={Momeni, Ali and Rahmani, Babak and Mallejac, Matthieu and Del Hougne, Philipp and Fleury, Romain},
journal={Science},
year={2023}
}
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