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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2304.04640 |
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| _version_ | 1866917892694474752 |
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| author | Yik, Jason Berghe, Korneel Van den Blanken, Douwe den 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 Joseph, George Vathakkattil 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-Taulé, 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 JS van Schaik, André 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 Tömen, Nergis Urgese, Gianvito Verhelst, Marian Vineyard, Craig M. Vogginger, Bernhard Yousefzadeh, Amirreza Zohora, Fatima Tuz Frenkel, Charlotte Reddi, Vijay Janapa |
| author_facet | Yik, Jason Berghe, Korneel Van den Blanken, Douwe den 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 Joseph, George Vathakkattil 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-Taulé, 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 JS van Schaik, André 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 Tömen, Nergis Urgese, Gianvito Verhelst, Marian Vineyard, Craig M. Vogginger, Bernhard Yousefzadeh, Amirreza Zohora, Fatima Tuz Frenkel, Charlotte Reddi, Vijay Janapa |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_04640 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems Yik, Jason Berghe, Korneel Van den Blanken, Douwe den 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 Joseph, George Vathakkattil 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-Taulé, 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 JS van Schaik, André 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 Tömen, Nergis Urgese, Gianvito Verhelst, Marian Vineyard, Craig M. Vogginger, Bernhard Yousefzadeh, Amirreza Zohora, Fatima Tuz Frenkel, Charlotte Reddi, Vijay Janapa Artificial Intelligence 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. |
| title | NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2304.04640 |