The neurobench framework for benchmarking neuromorphic computing algorithms and systems
Number of Authors: 1002025 (English)In: Nature Communications, E-ISSN 2041-1723, Vol. 16, no 1, article id 1545Article in journal (Refereed) 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).
Place, publisher, year, edition, pages
Nature Research , 2025. Vol. 16, no 1, article id 1545
Keywords [en]
accuracy assessment; algorithm; benchmarking; hardware; performance assessment; technological development; academia; algorithm; artificial intelligence software; benchmarking; controlled study; diagnosis; review
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:ri:diva-78354DOI: 10.1038/s41467-025-56739-4Scopus ID: 2-s2.0-85218828097OAI: oai:DiVA.org:ri-78354DiVA, id: diva2:1999847
Note
Authors of this work have been supported in parts by SemiconductorResearch Corporation (JY), the European Research Council (ERC) underthe European Union’s Horizon 2020 research and innovation programme (grant agreement No. 101001448), a grant from the ResearchGrants 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 acknowledgethe financial support of the CogniGron research center and the UbboEmmius Funds (Univ. of Groningen). We acknowledge a contributionfrom 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 waspartially supported by the European Commission, under the Horizongrant Ferro4Edge AI (grant agreement 101135656). This work is partlyfunded by the German Federal Ministry of Education and Research(BMBF) and the free state of Saxony within the ScaDS.AI center ofexcellence for AI research and by the German Federal Ministry forEconomic Affairs and Climate Action (BMWK) under contract01MN23004F (ESCADE). This work is partially supported by NSF Grant2020624 AccelNet:Accelerating Research on Neuromorphic Perception, Action, and Cognition and NSF Grant 2332166 RCN-SC: ResearchCoordination Network for Neuromorphic Integrated Circuits. SandiaNational 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., forthe U.S. Department of Energy’s National Nuclear Security Administration (DOE/NNSA) under contract DE-NA0003525. This written work isauthored by an employee of NTESS. The employee, not NTESS, owns theright, title and interest in and to the written work and is responsible for itscontents. Any subjective views or opinions that might be expressed inthe written work do not necessarily represent the views of the U.S.Government. The publisher acknowledges that the U.S. Governmentretains a non-exclusive, paid-up, irrevocable, world-wide license topublish or reproduce the published form of this written work or allowothers to do so, for U.S. Government purposes. The DOE will providepublic access to results of federally sponsored research in accordancewith the DOE Public Access Plan. This paper describes objective technical results and analysis. Any subjective views or opinions that might beexpressed in the paper do not necessarily represent the views of the U.S.Department of Energy or the United States Government.
2025-09-222025-09-222025-09-23Bibliographically approved