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Martin et al., 2021 - Google Patents

Eqspike: spike-driven equilibrium propagation for neuromorphic implementations

Martin et al., 2021

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Document ID
15640103050922177431
Author
Martin E
Ernoult M
Laydevant J
Li S
Querlioz D
Petrisor T
Grollier J
Publication year
Publication venue
Iscience

External Links

Snippet

Finding spike-based learning algorithms that can be implemented within the local constraints of neuromorphic systems, while achieving high accuracy, remains a formidable challenge. Equilibrium propagation is a promising alternative to backpropagation as it only …
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    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G06N3/0635—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
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