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The neurobench framework for benchmarking neuromorphic computing algorithms and systems

Date

2025-FEB 11
Journal Article Review
https://doi.org/10.1038/s41467-025-56739-4
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dc.contributor.authorYik, Jason
dc.contributor.authorvan den Berghe, Korneel
dc.contributor.authorden Blanken, Douwe
dc.contributor.authorBouhadjar, Younes
dc.contributor.authorFabre, Maxime
dc.contributor.authorHueber, Paul
dc.contributor.authorKe, Weijie
dc.contributor.authorKhoei, Mina A.
dc.contributor.authorKleyko, Denis
dc.contributor.authorPacik-Nelson, Noah
dc.contributor.authorPierro, Alessandro
dc.contributor.authorStratmann, Philipp
dc.contributor.authorSun, Pao-Sheng Vincent
dc.contributor.authorTang, Guangzhi
dc.contributor.authorWang, Shenqi
dc.contributor.authorZhou, Biyan
dc.contributor.authorAhmed, Soikat Hasan
dc.contributor.authorVathakkattil Joseph, George
dc.contributor.authorLeto, Benedetto
dc.contributor.authorMicheli, Aurora
dc.contributor.imecauthorHueber, Paul
dc.contributor.imecauthorTang, Guangzhi
dc.contributor.imecauthorWang, Shenqi
dc.contributor.imecauthorLiu, Yao-Hong
dc.contributor.imecauthorSifalakis, Manolis
dc.contributor.imecauthorVerhelst, Marian
dc.contributor.imecauthorYousefzadeh, Amirreza
dc.contributor.orcidimecLiu, Yao-Hong::0000-0002-3256-6741
dc.contributor.orcidimecSifalakis, Manolis::0000-0002-0949-2094
dc.contributor.orcidimecVerhelst, Marian::0000-0003-3495-9263
dc.contributor.orcidimecYousefzadeh, Amirreza::0000-0002-2967-5090
dc.date.accessioned2025-02-24T17:58:32Z
dc.date.available2025-02-24T17:58:32Z
dc.date.issued2025-FEB 11
dc.description.wosFundingTextAuthors of this work have been supported in parts by Semiconductor Research Corporation (JY), the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 101001448), a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China [Project No. CityU 11200922], ARC Laureate Fellowship FL210100156, and the EU H2020 project BeFerroSynaptic (871737). We acknowledge the financial support of the CogniGron research center and the Ubbo Emmius Funds (Univ. of Groningen). We acknowledge a contribution from the Italian National Recovery and Resilience Plan (NRRP), M4C2, funded by the European Union -NextGenerationEU (Project IR0000011, CUP B51E22000150006, "EBRAINS-Italy"). The work of SynSense was partially supported by the European Commission, under the Horizon grant Ferro4Edge AI (grant agreement 101135656). This work is partly funded by the German Federal Ministry of Education and Research (BMBF) and the free state of Saxony within the ScaDS.AI center of excellence for AI research and by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) under contract 01MN23004F (ESCADE). This work is partially supported by NSF Grant 2020624 AccelNet:Accelerating Research on Neuromorphic Perception, Action, and Cognition and NSF Grant 2332166 RCN-SC: Research Coordination Network for Neuromorphic Integrated Circuits. Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC (NTESS), a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration (DOE/NNSA) under contract DE-NA0003525. This written work is authored by an employee of NTESS. The employee, not NTESS, owns the right, title and interest in and to the written work and is responsible for its contents. Any subjective views or opinions that might be expressed in the written work do not necessarily represent the views of the U.S. Government. The publisher acknowledges that the U.S. Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this written work or allow others to do so, for U.S. Government purposes. The DOE will provide public access to results of federally sponsored research in accordance with the DOE Public Access Plan. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government.
dc.identifier.doi10.1038/s41467-025-56739-4
dc.identifier.pmidMEDLINE:39934126
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/45251
dc.publisherNATURE PORTFOLIO
dc.source.issue1
dc.source.journalNATURE COMMUNICATIONS
dc.source.numberofpages24
dc.source.volume16
dc.subject.keywordsNETWORK
dc.title

The neurobench framework for benchmarking neuromorphic computing algorithms and systems

dc.typeJournal article review
dspace.entity.typePublication
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