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dc.contributor.author
Qiao, Ning
dc.contributor.author
Mostafa, Hesham
dc.contributor.author
Corradi, Federico
dc.contributor.author
Osswald, Marc
dc.contributor.author
Stefanini, Fabio
dc.contributor.author
Sumislawska, Dora
dc.contributor.author
Indiveri, Giacomo
dc.date.accessioned
2019-06-05T09:33:35Z
dc.date.available
2017-06-11T17:30:58Z
dc.date.available
2019-06-05T09:33:35Z
dc.date.issued
2015-04-29
dc.identifier.issn
1662-453X
dc.identifier.issn
1662-4548
dc.identifier.other
10.3389/fnins.2015.00141
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/100988
dc.identifier.doi
10.3929/ethz-b-000100988
dc.description.abstract
Implementing compact, low-power artificial neural processing systems with real-time on-line learning abilities is still an open challenge. In this paper we present a full-custom mixed-signal VLSI device with neuromorphic learning circuits that emulate the biophysics of real spiking neurons and dynamic synapses for exploring the properties of computational neuroscience models and for building brain-inspired computing systems. The proposed architecture allows the on-chip configuration of a wide range of network connectivities, including recurrent and deep networks, with short-term and long-term plasticity. The device comprises 128 K analog synapse and 256 neuron circuits with biologically plausible dynamics and bi-stable spike-based plasticity mechanisms that endow it with on-line learning abilities. In addition to the analog circuits, the device comprises also asynchronous digital logic circuits for setting different synapse and neuron properties as well as different network configurations. This prototype device, fabricated using a 180 nm 1P6M CMOS process, occupies an area of 51.4 mm2, and consumes approximately 4 mW for typical experiments, for example involving attractor networks. Here we describe the details of the overall architecture and of the individual circuits and present experimental results that showcase its potential. By supporting a wide range of cortical-like computational modules comprising plasticity mechanisms, this device will enable the realization of intelligent autonomous systems with on-line learning capabilities.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Frontiers Media
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Spike-based learning
en_US
dc.subject
Spike-Timing Dependent Plasticity (STDP)
en_US
dc.subject
Real-time
en_US
dc.subject
Analog VLSI
en_US
dc.subject
Winner-Take-All (WTA)
en_US
dc.subject
Attractor network
en_US
dc.subject
Asynchronous
en_US
dc.subject
Brain-inspired computing
en_US
dc.title
A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
Frontiers in Neuroscience
ethz.journal.volume
9
en_US
ethz.journal.abbreviated
Front Neurosci
ethz.pages.start
141
en_US
ethz.size
17 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.nebis
009497874
ethz.publication.place
Lausanne
ethz.publication.status
published
en_US
ethz.leitzahl
03453 - Douglas, Rodney J.
en_US
ethz.leitzahl.certified
03453 - Douglas, Rodney J.
ethz.date.deposited
2017-06-11T17:31:56Z
ethz.source
ECIT
ethz.identifier.importid
imp5936532ed24fa39349
ethz.ecitpid
pub:158637
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-13T02:45:15Z
ethz.rosetta.lastUpdated
2024-02-02T08:12:12Z
ethz.rosetta.versionExported
true
ethz.COinS
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