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28 September 2023 Nanoelectronic implementation of Equilibrium Propagation
Marie Drouhin, Clément Turck, Kamel-Eddine Harabi, Adrien Renaudineau, Thomas Bersani-Veroni, Elisa Vianello, Jean-Michel Portal, Julie Grollier, Damien Querlioz
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Proceedings Volume PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023; PC1265507 (2023) https://doi.org/10.1117/12.2677568
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States
Abstract
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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Marie Drouhin, Clément Turck, Kamel-Eddine Harabi, Adrien Renaudineau, Thomas Bersani-Veroni, Elisa Vianello, Jean-Michel Portal, Julie Grollier, and Damien Querlioz "Nanoelectronic implementation of Equilibrium Propagation", Proc. SPIE PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023, PC1265507 (28 September 2023); https://doi.org/10.1117/12.2677568
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KEYWORDS
Nanoelectronics
Education and training
Neural networks
Analog electronics
Computer programming
Resistance
Spatial learning
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