Nature Communications (Feb 2025)

The neurobench framework for benchmarking neuromorphic computing algorithms and systems

  • Jason Yik,
  • Korneel Van den Berghe,
  • Douwe den Blanken,
  • Younes Bouhadjar,
  • Maxime Fabre,
  • Paul Hueber,
  • Weijie Ke,
  • Mina A. Khoei,
  • Denis Kleyko,
  • Noah Pacik-Nelson,
  • Alessandro Pierro,
  • Philipp Stratmann,
  • Pao-Sheng Vincent Sun,
  • Guangzhi Tang,
  • Shenqi Wang,
  • Biyan Zhou,
  • Soikat Hasan Ahmed,
  • George Vathakkattil Joseph,
  • Benedetto Leto,
  • Aurora Micheli,
  • Anurag Kumar Mishra,
  • Gregor Lenz,
  • Tao Sun,
  • Zergham Ahmed,
  • Mahmoud Akl,
  • Brian Anderson,
  • Andreas G. Andreou,
  • Chiara Bartolozzi,
  • Arindam Basu,
  • Petrut Bogdan,
  • Sander Bohte,
  • Sonia Buckley,
  • Gert Cauwenberghs,
  • Elisabetta Chicca,
  • Federico Corradi,
  • Guido de Croon,
  • Andreea Danielescu,
  • Anurag Daram,
  • Mike Davies,
  • Yigit Demirag,
  • Jason Eshraghian,
  • Tobias Fischer,
  • Jeremy Forest,
  • Vittorio Fra,
  • Steve Furber,
  • P. Michael Furlong,
  • William Gilpin,
  • Aditya Gilra,
  • Hector A. Gonzalez,
  • Giacomo Indiveri,
  • Siddharth Joshi,
  • Vedant Karia,
  • Lyes Khacef,
  • James C. Knight,
  • Laura Kriener,
  • Rajkumar Kubendran,
  • Dhireesha Kudithipudi,
  • Shih-Chii Liu,
  • Yao-Hong Liu,
  • Haoyuan Ma,
  • Rajit Manohar,
  • Josep Maria Margarit-Taulé,
  • Christian Mayr,
  • Konstantinos Michmizos,
  • Dylan R. Muir,
  • Emre Neftci,
  • Thomas Nowotny,
  • Fabrizio Ottati,
  • Ayca Ozcelikkale,
  • Priyadarshini Panda,
  • Jongkil Park,
  • Melika Payvand,
  • Christian Pehle,
  • Mihai A. Petrovici,
  • Christoph Posch,
  • Alpha Renner,
  • Yulia Sandamirskaya,
  • Clemens J. S. Schaefer,
  • André van Schaik,
  • Johannes Schemmel,
  • Samuel Schmidgall,
  • Catherine Schuman,
  • Jae-sun Seo,
  • Sadique Sheik,
  • Sumit Bam Shrestha,
  • Manolis Sifalakis,
  • Amos Sironi,
  • Kenneth Stewart,
  • Matthew Stewart,
  • Terrence C. Stewart,
  • Jonathan Timcheck,
  • Nergis Tömen,
  • Gianvito Urgese,
  • Marian Verhelst,
  • Craig M. Vineyard,
  • Bernhard Vogginger,
  • Amirreza Yousefzadeh,
  • Fatima Tuz Zohora,
  • Charlotte Frenkel,
  • Vijay Janapa Reddi

DOI
https://doi.org/10.1038/s41467-025-56739-4
Journal volume & issue
Vol. 16, no. 1
pp. 1 – 24

Abstract

Read online

Abstract 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 ).