Finding a roadmap to achieve large neuromorphic hardware systems
Identifieur interne : 001C03 ( Main/Merge ); précédent : 001C02; suivant : 001C04Finding a roadmap to achieve large neuromorphic hardware systems
Auteurs : Jennifer Hasler ; Bo MarrSource :
- Frontiers in Neuroscience [ 1662-4548 ] ; 2013.
Abstract
Neuromorphic systems are gaining increasing importance in an era where CMOS digital computing techniques are reaching physical limits. These silicon systems mimic extremely energy efficient neural computing structures, potentially both for solving engineering applications as well as understanding neural computation. Toward this end, the authors provide a glimpse at what the technology evolution roadmap looks like for these systems so that Neuromorphic engineers may gain the same benefit of anticipation and foresight that IC designers gained from Moore's law many years ago. Scaling of energy efficiency, performance, and size will be discussed as well as how the implementation and application space of Neuromorphic systems are expected to evolve over time.
Url:
DOI: 10.3389/fnins.2013.00118
PubMed: 24058330
PubMed Central: 3767911
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Finding a roadmap to achieve large neuromorphic hardware systems</title><author><name sortKey="Hasler, Jennifer" sort="Hasler, Jennifer" uniqKey="Hasler J" first="Jennifer" last="Hasler">Jennifer Hasler</name></author><author><name sortKey="Marr, Bo" sort="Marr, Bo" uniqKey="Marr B" first="Bo" last="Marr">Bo Marr</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">24058330</idno><idno type="pmc">3767911</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3767911</idno><idno type="RBID">PMC:3767911</idno><idno type="doi">10.3389/fnins.2013.00118</idno><date when="2013">2013</date><idno type="wicri:Area/Pmc/Corpus">001E43</idno><idno type="wicri:Area/Pmc/Curation">001E43</idno><idno type="wicri:Area/Pmc/Checkpoint">001260</idno><idno type="wicri:Area/Ncbi/Merge">002965</idno><idno type="wicri:Area/Ncbi/Curation">002965</idno><idno type="wicri:Area/Ncbi/Checkpoint">002965</idno><idno type="wicri:doubleKey">1662-4548:2013:Hasler J:finding:a:roadmap</idno><idno type="wicri:Area/Main/Merge">001C03</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Finding a roadmap to achieve large neuromorphic hardware systems</title><author><name sortKey="Hasler, Jennifer" sort="Hasler, Jennifer" uniqKey="Hasler J" first="Jennifer" last="Hasler">Jennifer Hasler</name></author><author><name sortKey="Marr, Bo" sort="Marr, Bo" uniqKey="Marr B" first="Bo" last="Marr">Bo Marr</name></author></analytic><series><title level="j">Frontiers in Neuroscience</title><idno type="ISSN">1662-4548</idno><idno type="eISSN">1662-453X</idno><imprint><date when="2013">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Neuromorphic systems are gaining increasing importance in an era where CMOS digital computing techniques are reaching physical limits. These silicon systems mimic extremely energy efficient neural computing structures, potentially both for solving engineering applications as well as understanding neural computation. Toward this end, the authors provide a glimpse at what the technology evolution roadmap looks like for these systems so that Neuromorphic engineers may gain the same benefit of anticipation and foresight that IC designers gained from Moore's law many years ago. Scaling of energy efficiency, performance, and size will be discussed as well as how the implementation and application space of Neuromorphic systems are expected to evolve over time.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Allman, J" uniqKey="Allman J">J. Allman</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Azevedo, F A" uniqKey="Azevedo F">F. A. Azevedo</name></author><author><name sortKey="Carvalho, L R" uniqKey="Carvalho L">L. R. 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