Yik, Jason van den Berghe, Korneel den Blanken, Douwe Bouhadjar, Younes Fabre, Maxime Hueber, Paul Ke, Weijie Khoei, Mina A. Kleyko, Denis Pacik-Nelson, Noah Pierro, Alessandro Stratmann, Philipp Sun, Pao-Sheng Vincent Tang, Guangzhi Wang, Shenqi Zhou, Biyan Ahmed, Soikat Hasan Vathakkattil Joseph, George Leto, Benedetto Micheli, Aurora Mishra, Anurag Kumar Lenz, Gregor Sun, Tao Ahmed, Zergham Akl, Mahmoud Anderson, Brian Andreou, Andreas G. Bartolozzi, Chiara Basu, Arindam Bogdan, Petrut Bohte, Sander Buckley, Sonia Cauwenberghs, Gert Chicca, Elisabetta Corradi, Federico de Croon, Guido Danielescu, Andreea Daram, Anurag Davies, Mike Demirag, Yigit Eshraghian, Jason Fischer, Tobias Forest, Jeremy Fra, Vittorio Furber, Steve Furlong, P. Michael Gilpin, William Gilra, Aditya Gonzalez, Hector A. Indiveri, Giacomo Joshi, Siddharth Karia, Vedant Khacef, Lyes Knight, James C. Kriener, Laura Kubendran, Rajkumar Kudithipudi, Dhireesha Liu, Shih-Chii Liu, Yao-Hong Ma, Haoyuan Manohar, Rajit Margarit-Taule, Josep Maria Mayr, Christian Michmizos, Konstantinos Muir, Dylan R. Neftci, Emre Nowotny, Thomas Ottati, Fabrizio Ozcelikkale, Ayca Panda, Priyadarshini Park, Jongkil Payvand, Melika Pehle, Christian Petrovici, Mihai A. Posch, Christoph Renner, Alpha Sandamirskaya, Yulia Schaefer, Clemens J. S. van Schaik, Andre Schemmel, Johannes Schmidgall, Samuel Schuman, Catherine Seo, Jae-sun Sheik, Sadique Shrestha, Sumit Bam Sifalakis, Manolis Sironi, Amos Stewart, Kenneth Stewart, Matthew Stewart, Terrence C. Timcheck, Jonathan Tomen, Nergis Urgese, Gianvito Verhelst, Marian Vineyard, Craig M. Vogginger, Bernhard Yousefzadeh, Amirreza Zohora, Fatima Tuz Frenkel, Charlotte Reddi, Vijay Janapa 2025-03-19T15:00:31Z 2025-03-19T15:00:31Z 2025-03-19 2025-02 https://pubs.kist.re.kr/handle/201004/151897 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). English Nature Publishing Group The neurobench framework for benchmarking neuromorphic computing algorithms and systems Article 10.1038/s41467-025-56739-4 1 Nature Communications, v.16, no.1 Nature Communications 16 1 Y scie scopus 001420017600023 Multidisciplinary Sciences Science & Technology - Other Topics Review NETWORK