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5 October 2023 Adapting equilibrium propagation to physical systems
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 PC12656, Spintronics XVI; PC126560M (2023) https://doi.org/10.1117/12.2677544
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States
Abstract
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.
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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 "Adapting equilibrium propagation to physical systems", Proc. SPIE PC12656, Spintronics XVI, PC126560M (5 October 2023); https://doi.org/10.1117/12.2677544
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KEYWORDS
Education and training
Algorithm development
Neural networks
Evolutionary algorithms
Spatial learning
Spintronics
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