KIP publications

 
year 2019
author(s) Sebastian Billaudelle*, Benjamin Cramer*, Mihai A. Petrovici, Korbinian Schreiber, David Kappel, Johannes Schemmel^, Karlheinz Meier^
title Structural plasticity on an accelerated analog neuromorphic hardware system
KIP-Nummer HD-KIP 19-118
KIP-Gruppe(n) F9
document type Paper
Keywords neuromorphic engineering, spiking neurons, physical emulation, inference, plasticity
source arXiv preprint arXiv:1912.12047
Abstract (en)

In computational neuroscience, as well as in machine learning, neuromorphic devices promise an accelerated and scalable alternative to neural network simulations. Their neural connectivity and synaptic capacity depends on their specific design choices, but is always intrinsically limited. Here, we present a strategy to achieve structural plasticity that optimizes resource allocation under these constraints by constantly rewiring the pre- and postsynaptic partners while keeping the neuronal fan-in constant and the connectome sparse. In our implementation, the algorithm is executed on a custom embedded digital processor that accompanies a mixed-signal substrate consisting of spiking neurons and synapse circuits. We evaluated our proposed algorithm in a simple supervised learning scenario, showing its ability to optimize the network topology with respect to the nature of its training data, as well as its overall computational efficiency.

bibtex
@article{billaudelle2019structural,
  author   = {Billaudelle, Sebastian and Cramer, Benjamin and Petrovici, Mihai A and Schreiber, Korbinian and Kappel, David and Schemmel, Johannes and Meier, Karlheinz},
  title    = {Structural plasticity on an accelerated analog neuromorphic hardware system},
  journal  = {arXiv preprint arXiv:1912.12047},
  year     = {2019},
  volume   = {},
  pages    = {}
}
Datei pdf
URL ArXiv
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