Show simple item record

dc.contributor.authorToomey, Emily Anne
dc.contributor.authorSegall, Ken
dc.contributor.authorBerggren, Karl K.
dc.date.accessioned2020-04-22T14:28:43Z
dc.date.available2020-04-22T14:28:43Z
dc.date.issued2019-09
dc.date.submitted2019-06
dc.identifier.issn2381-2710
dc.identifier.issn2154-5723
dc.identifier.urihttps://hdl.handle.net/1721.1/124787
dc.description.abstractWith the rising societal demand for more information-processing capacity with lower power consumption, alternative architectures inspired by the parallelism and robustness of the human brain have recently emerged as possible solutions. In particular, spiking neural networks (SNNs) offer a bio-realistic approach, relying on pulses, analogous to action potentials, as units of information. While software encoded networks provide flexibility and precision, they are often computationally expensive. As a result, hardware SNNs based on the spiking dynamics of a device or circuit represent an increasingly appealing direction. Here, we propose to use superconducting nanowires as a platform for the development of an artificial neuron. Building on an architecture first proposed for Josephson junctions, we rely on the intrinsic non-linearity of two coupled nanowires to generate spiking behavior, and use electrothermal circuit simulations to demonstrate that the nanowire neuron reproduces multiple characteristics of biological neurons. Furthermore, by harnessing the non-linearity of the superconducting nanowire’s inductance, we develop a design for a variable inductive synapse capable of both excitatory and inhibitory control. We demonstrate that this synapse design supports direct fan-out, a feature that has been difficult to achieve in other superconducting architectures, and that the nanowire neuron’s nominal energy performance is competitive with that of current technologies.en_US
dc.description.sponsorshipNational Science Foundation (U.S.). GraduateResearch Fellowship Program (NSF GRFP) (Grant No. 1122374).en_US
dc.language.isoen
dc.publisherFrontiers Media SAen_US
dc.relation.isversionofhttp://dx.doi.org/10.3389/fnins.2019.00933en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceFrontiersen_US
dc.titleDesign of a Power Efficient Artificial Neuron Using Superconducting Nanowiresen_US
dc.typeArticleen_US
dc.identifier.citationToomey, Emily, Segall, Ken, and Berggren, Karl K. (2019) Design of a Power Efficient Artificial Neuron Using Superconducting Nanowires. Front. Neurosci. 13:933. © Copyright © 2019 Toomey, Segall and Berggren.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.relation.journalFrontiers in Neuroscienceen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2019-11-26T13:46:28Z
dspace.orderedauthorsToomey, Emily; Segall, Ken; Berggren, Karl K.en_US
dspace.date.submission2019-11-26T13:46:30Z
mit.journal.volume13en_US
mit.metadata.statusComplete


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record