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Yousefzadeh et al., 2018 - Google Patents

On practical issues for stochastic STDP hardware with 1-bit synaptic weights

Yousefzadeh et al., 2018

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Document ID
411390498761122322
Author
Yousefzadeh A
Stromatias E
Soto M
Serrano-Gotarredona T
Linares-Barranco B
Publication year
Publication venue
Frontiers in neuroscience

External Links

Snippet

In computational neuroscience, synaptic plasticity learning rules are typically studied using the full 64-bit floating point precision computers provide. However, for dedicated hardware implementations, the precision used not only penalizes directly the required memory …
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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
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    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F15/00—Digital computers in general; Data processing equipment in general
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
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