As the field of deep learning continues to expand, it has become increasingly important to develop energy-efficient hardware that can adapt to these advances. However, achieving learning on a chip requires the use of algorithms that are compatible with hardware and can be implemented on imperfect devices. One promising training technique is Equilibrium Propagation, which was introduced in 2017 by Yoshua Bengio. This approach provides gradient estimates based on a spatially local learning rule, making it more biologically plausible and better suited for hardware than backpropagation. However, the mathematical equations of this algorithm cannot be directly transposed to a physical system. In this study, Equilibrium Propagation algorithm is adapted to the use of a real physical system, and its potential application to spintronics devices is discussed.
As deep learning continues to grow, developing adapted energy-efficient hardware becomes crucial. Learning on a chip requires hardware-compatible learning algorithms and their realization with physically imperfect devices. Equilibrium Propagation is a training technique introduced in 2017 by Yoshua Bengio which gives gradient estimates based on a spatially local learning rule, making it both more biologically plausible and more hardware compatible than backpropagation. This work uses the Equilibrium Propagation algorithm to train a neural network with hardware-in-the-loop simulations using hafnium oxide memristor synapses. Realizing this type of learning with imperfect and noisy devices paves the way for on-chip learning at very low energy.
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