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:10.1038/s41467-025-56739-4

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 MicheliAnurag 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

MARCS Institute for Brain, Behaviour & Development

Research output: Contribution to journal › Article › peer-review

17 Citations (Scopus)

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 ).

Original language	English
Article number	1545
Pages (from-to)	1545
Number of pages	1
Journal	Nature Communications
Volume	16
Issue number	1
DOIs	https://doi.org/10.1038/s41467-025-56739-4
Publication status	Published - Dec 2025

Bibliographical note

Publisher Copyright:
© The Author(s) 2025.

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Yik, J., Van den Berghe, K., den Blanken, D., Bouhadjar, Y., Fabre, M., Hueber, P., Ke, W., Khoei, M. A., Kleyko, D., Pacik-Nelson, N., Pierro, A., Stratmann, P., Sun, P. S. V., Tang, G., Wang, S., Zhou, B., Ahmed, S. H., Vathakkattil Joseph, G., Leto, B., ... Reddi, V. J. (2025). The neurobench framework for benchmarking neuromorphic computing algorithms and systems. Nature Communications, 16(1), 1545. Article 1545. https://doi.org/10.1038/s41467-025-56739-4

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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 ).",

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Yik, J, Van den Berghe, K, den Blanken, D, Bouhadjar, Y, Fabre, M, Hueber, P, Ke, W, Khoei, MA, Kleyko, D, Pacik-Nelson, N, Pierro, A, Stratmann, P, Sun, PSV, Tang, G, Wang, S, Zhou, B, Ahmed, SH, Vathakkattil Joseph, G, Leto, B, Micheli, A, Mishra, AK, Lenz, G, Sun, T, Ahmed, Z, Akl, M, Anderson, B, Andreou, AG, Bartolozzi, C, Basu, A, Bogdan, P, Bohte, S, Buckley, S, Cauwenberghs, G, Chicca, E, Corradi, F, de Croon, G, Danielescu, A, Daram, A, Davies, M, Demirag, Y, Eshraghian, J, Fischer, T, Forest, J, Fra, V, Furber, S, Furlong, PM, Gilpin, W, Gilra, A, Gonzalez, HA, Indiveri, G, Joshi, S, Karia, V, Khacef, L, Knight, JC, Kriener, L, Kubendran, R, Kudithipudi, D, Liu, SC, Liu, YH, Ma, H, Manohar, R, Margarit-Taulé, JM, Mayr, C, Michmizos, K, Muir, DR, Neftci, E, Nowotny, T, Ottati, F, Ozcelikkale, A, Panda, P, Park, J, Payvand, M, Pehle, C, Petrovici, MA, Posch, C, Renner, A, Sandamirskaya, Y, Schaefer, CJS, van Schaik, A, Schemmel, J, Schmidgall, S, Schuman, C, Seo, JS, Sheik, S, Shrestha, SB, Sifalakis, M, Sironi, A, Stewart, K, Stewart, M, Stewart, TC, Timcheck, J, Tömen, N, Urgese, G, Verhelst, M, Vineyard, CM, Vogginger, B, Yousefzadeh, A, Zohora, FT, Frenkel, C & Reddi, VJ 2025, 'The neurobench framework for benchmarking neuromorphic computing algorithms and systems', Nature Communications, vol. 16, no. 1, 1545, pp. 1545. https://doi.org/10.1038/s41467-025-56739-4

TY - JOUR

T1 - The neurobench framework for benchmarking neuromorphic computing algorithms and systems

AU - Yik, Jason

AU - Van den Berghe, Korneel

AU - den Blanken, Douwe

AU - Bouhadjar, Younes

AU - Fabre, Maxime

AU - Hueber, Paul

AU - Ke, Weijie

AU - Khoei, Mina A.

AU - Kleyko, Denis

AU - Pacik-Nelson, Noah

AU - Pierro, Alessandro

AU - Stratmann, Philipp

AU - Sun, Pao Sheng Vincent

AU - Tang, Guangzhi

AU - Wang, Shenqi

AU - Zhou, Biyan

AU - Ahmed, Soikat Hasan

AU - Vathakkattil Joseph, George

AU - Leto, Benedetto

AU - Micheli, Aurora

AU - Mishra, Anurag Kumar

AU - Lenz, Gregor

AU - Sun, Tao

AU - Ahmed, Zergham

AU - Akl, Mahmoud

AU - Anderson, Brian

AU - Andreou, Andreas G.

AU - Bartolozzi, Chiara

AU - Basu, Arindam

AU - Bogdan, Petrut

AU - Bohte, Sander

AU - Buckley, Sonia

AU - Cauwenberghs, Gert

AU - Chicca, Elisabetta

AU - Corradi, Federico

AU - de Croon, Guido

AU - Danielescu, Andreea

AU - Daram, Anurag

AU - Davies, Mike

AU - Demirag, Yigit

AU - Eshraghian, Jason

AU - Fischer, Tobias

AU - Forest, Jeremy

AU - Fra, Vittorio

AU - Furber, Steve

AU - Furlong, P. Michael

AU - Gilpin, William

AU - Gilra, Aditya

AU - Gonzalez, Hector A.

AU - Indiveri, Giacomo

AU - Joshi, Siddharth

AU - Karia, Vedant

AU - Khacef, Lyes

AU - Knight, James C.

AU - Kriener, Laura

AU - Kubendran, Rajkumar

AU - Kudithipudi, Dhireesha

AU - Liu, Shih Chii

AU - Liu, Yao Hong

AU - Ma, Haoyuan

AU - Manohar, Rajit

AU - Margarit-Taulé, Josep Maria

AU - Mayr, Christian

AU - Michmizos, Konstantinos

AU - Muir, Dylan R.

AU - Neftci, Emre

AU - Nowotny, Thomas

AU - Ottati, Fabrizio

AU - Ozcelikkale, Ayca

AU - Panda, Priyadarshini

AU - Park, Jongkil

AU - Payvand, Melika

AU - Pehle, Christian

AU - Petrovici, Mihai A.

AU - Posch, Christoph

AU - Renner, Alpha

AU - Sandamirskaya, Yulia

AU - Schaefer, Clemens J.S.

AU - van Schaik, André

AU - Schemmel, Johannes

AU - Schmidgall, Samuel

AU - Schuman, Catherine

AU - Seo, Jae Sun

AU - Sheik, Sadique

AU - Shrestha, Sumit Bam

AU - Sifalakis, Manolis

AU - Sironi, Amos

AU - Stewart, Kenneth

AU - Stewart, Matthew

AU - Stewart, Terrence C.

AU - Timcheck, Jonathan

AU - Tömen, Nergis

AU - Urgese, Gianvito

AU - Verhelst, Marian

AU - Vineyard, Craig M.

AU - Vogginger, Bernhard

AU - Yousefzadeh, Amirreza

AU - Zohora, Fatima Tuz

AU - Frenkel, Charlotte

AU - Reddi, Vijay Janapa

PY - 2025/12

Y1 - 2025/12

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AB - 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 ).

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DO - 10.1038/s41467-025-56739-4

M3 - Article

C2 - 39934126

AN - SCOPUS:85218828097

SN - 2041-1723

VL - 16

SP - 1545

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 1545

ER -

The neurobench framework for benchmarking neuromorphic computing algorithms and systems

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