The neurobench framework for benchmarking neuromorphic computing algorithms and systems
Antal upphovsmän: 1002025 (Engelska)Ingår i: Nature Communications, E-ISSN 2041-1723, Vol. 16, nr 1, artikel-id 1545Artikel i tidskrift (Refereegranskat) Published
Abstract [en]
Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. This article presents NeuroBench, a benchmark framework for neuromorphic algorithms and systems, which is collaboratively designed from an open community of researchers across industry and academia. NeuroBench introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent and hardware-dependent settings. For latest project updates, visit the project website ( neurobench.ai ).
Ort, förlag, år, upplaga, sidor
Springer, 2025. Vol. 16, nr 1, artikel-id 1545
Nationell ämneskategori
Den kondenserade materiens fysik
Identifikatorer
URN: urn:nbn:se:oru:diva-119238DOI: 10.1038/s41467-025-56739-4ISI: 001420017600023PubMedID: 39934126Scopus ID: 2-s2.0-85218828097OAI: oai:DiVA.org:oru-119238DiVA, id: diva2:1936875
Forskningsfinansiär
EU, Horisont 2020, 101001448Europeiska kommissionen, 101135656
Anmärkning
Funding Agencies:
Authors 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.
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