Training Dynamical Binary Neural Networks with Equilibrium Propagation

@article{Laydevant2021TrainingDB,
  title={Training Dynamical Binary Neural Networks with Equilibrium Propagation},
  author={J'er'emie Laydevant and Maxence Ernoult and Damien Querlioz and Julie Grollier},
  journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
  year={2021},
  pages={4635-4644}
}
Equilibrium Propagation (EP) is an algorithm intrinsically adapted to the training of physical networks, thanks to the local updates of weights given by the internal dynamics of the system. However, the construction of such a hardware requires to make the algorithm compatible with existing neuromorphic CMOS technologies, which generally exploit digital communication between neurons and offer a limited amount of local memory. In this work, we demonstrate that EP can train dynamical networks with… Expand
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