NeuroBench: advancing neuromorphic computing through collaborative, fair and representative benchmarking

Published

April 20, 2023

Author(s)

Jason Yik, Soikat Hasan Ahmed, Zergham Ahmed, Brian Anderson, Andreas G. Andreou, Chiara Bartolozzi, Arindam Basu, Douwe den Blanken, Petrut Bogdan, Sonia Buckley, Sander Bohte, Younes Bouhadjar, Gert Cauwenberghs, Federico Corradi, Guido de Croon, Andreea Danielescu, Anurag Daram, Mike Davies, Yigit Demirag, Jason K. Eshraghian, Jeremy Forest, Steve Furber, Michael Furlong, Aditya Gilra, Giacomo Indiveri, Siddarth Joshi, Vedant Karia, Lyes Khacef, James C. Knight, Laura Kriener, Rajkumar Kubendran, Dhireesha Kudithipudi, Gregor Lenz, Rajit Manohar, Christian Mayr, Konstantinos Michmizos, Dylan Muir, Emre Neftci, Thomas Nowotny, Fabrizio Ottati, Ayca Ozcelikkale, Noah Pacik-Nelson, Priyadarshini Panda, Sun Pao-Sheng, Melika Payvand, Christian-Gernot Pehle, Mihai Alexandru Petrovici, Cristoph Posch, Alpha Renner, Yulia Sandamirskaya, Clemens Schaefer, Andre van Schaik, Johannes Schemmel, Catherine Schuman, Jae-sun Seo, Sumit Bam Shrestha, Manolis Sifalakis, Amos Sironi, Kenneth Stewart, Terrence Stewart, Philipp Stratmann, Guangzhi Tang, Jonathan Timcheck, Marian Verhelst, Craig Vineyard, Bernard Vogginger, Amirreza Yousefzadeh, Biyan Zhou, Fatima Tuz Zohora, Charlotte Frenkel, Vijay Janapa Reddy

Abstract

The field of neuromorphic computing holds great promise in terms of advancing computing efficiency and capabilities by following brain-inspired principles. However, the rich diversity of techniques employed in neuromorphic research has resulted in a lack of clear standards for benchmarking, hindering effective evaluation of the advantages and strengths of neuromorphic methods compared to traditional deep-learning-based methods. This paper presents a collaborative effort, bringing together members from academia and the industry, to define benchmarks for neuromorphic computing, named NeuroBench. The goals of NeuroBench are to be a collaborative, fair, and representative benchmark suite developed by the community, for the community. In this paper, we discuss the challenges associated with benchmarking neuromorphic solutions, and outline the key features of NeuroBench. We believe that NeuroBench will be a significant step towards defining standards that can unify the goals of neuromorphic computing and drive its technological progress.

Citation

https://arxiv.org/

Pub Weblink

https://arxiv.org/pdf/2304.04640.pdf

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Yik, J. , Ahmed, S. , Ahmed, Z. , Anderson, B. , Andreou, A. , Bartolozzi, C. , Basu, A. , den Blanken, D. , Bogdan, P. , Buckley, S. , Bohte, S. , Bouhadjar, Y. , Cauwenberghs, G. , Corradi, F. , de Croon, G. , Danielescu, A. , Daram, A. , Davies, M. , Demirag, Y. , Eshraghian, J. , Forest, J. , Furber, S. , Furlong, M. , Gilra, A. , Indiveri, G. , Joshi, S. , Karia, V. , Khacef, L. , Knight, J. , Kriener, L. , Kubendran, R. , Kudithipudi, D. , Lenz, G. , Manohar, R. , Mayr, C. , Michmizos, K. , Muir, D. , Neftci, E. , Nowotny, T. , Ottati, F. , Ozcelikkale, A. , Pacik-Nelson, N. , Panda, P. , Pao-Sheng, S. , Payvand, M. , Pehle, C. , Petrovici, M. , Posch, C. , Renner, A. , Sandamirskaya, Y. , Schaefer, C. , van Schaik, A. , Schemmel, J. , Schuman, C. , Seo, J. , Shrestha, S. , Sifalakis, M. , Sironi, A. , Stewart, K. , Stewart, T. , Stratmann, P. , Tang, G. , Timcheck, J. , Verhelst, M. , Vineyard, C. , Vogginger, B. , Yousefzadeh, A. , Zhou, B. , Zohora, F. , Frenkel, C. and Reddy, V. (2023), NeuroBench: advancing neuromorphic computing through collaborative, fair and representative benchmarking, https://arxiv.org/, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=936693, https://arxiv.org/pdf/2304.04640.pdf (Accessed November 23, 2024)