NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems
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. Prior neuromorphic computing benchmark efforts have not seen widespread adoption due to a lack of inclusive, actionable, and iterative benchmark design and guidelines. To address these shortcomings, we present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems. NeuroBench is a collaboratively-designed effort from an open community of researchers across industry and academia, aiming to provide a representative structure for standardizing the evaluation of neuromorphic approaches. The NeuroBench framework 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 (algorithm track) and hardware-dependent (system track) settings. In this article, we outline tasks and guidelines for benchmarks across multiple application domains, and present initial performance baselines across neuromorphic and conventional approaches for both benchmark tracks. NeuroBench is intended to continually expand its benchmarks and features to foster and track the progress made by the research community.
Cite
@article{arxiv.2304.04640,
title = {NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems},
author = {Jason Yik and Korneel Van den Berghe and Douwe den Blanken and Younes Bouhadjar and Maxime Fabre and Paul Hueber and Weijie Ke and Mina A Khoei and Denis Kleyko and Noah Pacik-Nelson and Alessandro Pierro and Philipp Stratmann and Pao-Sheng Vincent Sun and Guangzhi Tang and Shenqi Wang and Biyan Zhou and Soikat Hasan Ahmed and George Vathakkattil Joseph and Benedetto Leto and Aurora Micheli and Anurag Kumar Mishra and Gregor Lenz and Tao Sun and Zergham Ahmed and Mahmoud Akl and Brian Anderson and Andreas G. Andreou and Chiara Bartolozzi and Arindam Basu and Petrut Bogdan and Sander Bohte and Sonia Buckley and Gert Cauwenberghs and Elisabetta Chicca and Federico Corradi and Guido de Croon and Andreea Danielescu and Anurag Daram and Mike Davies and Yigit Demirag and Jason Eshraghian and Tobias Fischer and Jeremy Forest and Vittorio Fra and Steve Furber and P. Michael Furlong and William Gilpin and Aditya Gilra and Hector A. Gonzalez and Giacomo Indiveri and Siddharth Joshi and Vedant Karia and Lyes Khacef and James C. Knight and Laura Kriener and Rajkumar Kubendran and Dhireesha Kudithipudi and Shih-Chii Liu and Yao-Hong Liu and Haoyuan Ma and Rajit Manohar and Josep Maria Margarit-Taulé and Christian Mayr and Konstantinos Michmizos and Dylan R. Muir and Emre Neftci and Thomas Nowotny and Fabrizio Ottati and Ayca Ozcelikkale and Priyadarshini Panda and Jongkil Park and Melika Payvand and Christian Pehle and Mihai A. Petrovici and Christoph Posch and Alpha Renner and Yulia Sandamirskaya and Clemens JS Schaefer and André van Schaik and Johannes Schemmel and Samuel Schmidgall and Catherine Schuman and Jae-sun Seo and Sadique Sheik and Sumit Bam Shrestha and Manolis Sifalakis and Amos Sironi and Kenneth Stewart and Matthew Stewart and Terrence C. Stewart and Jonathan Timcheck and Nergis Tömen and Gianvito Urgese and Marian Verhelst and Craig M. Vineyard and Bernhard Vogginger and Amirreza Yousefzadeh and Fatima Tuz Zohora and Charlotte Frenkel and Vijay Janapa Reddi},
journal= {arXiv preprint arXiv:2304.04640},
year = {2025}
}
Comments
To appear in Nature Neuromorphic Hardware and Computing collection