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This work describes the investigation of neuromorphic computing-based spiking neural network (SNN) models used to filter data from sensor electronics in high energy physics experiments conducted at the High Luminosity Large Hadron Collider.
A Detailed Simulation of the CMS Pixel Sensor
Morris Swartz. 2002 · 2002
Earlier work this paper cites.
The ATLAS Experiment at the CERN Large Hadron Collider
G. Aad et al · 2008
Earlier work this paper cites.
The CMS Experiment at the CERN LHC
S. Chatrchyan et al · 2008
Earlier work this paper cites.
Neuromorphic computing for temporal scientific data classification. In Proceedings of the Neuromorphic Computing Symposium . 1–6
Catherine D Schuman, Thomas E Potok, Steven Young, Robert Patton, Gabriel Perdue, Gangotree Chakma, Austin Wyer, and Garrett S Rose. 2017 · 2017
Earlier work this paper cites.
Efficient classification of supercomputer failures using neuromorphic computing. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI) . IEEE, 242–249
Prasanna Date, Christopher D Carothers, James A Hendler, and Malik Magdon-Ismail. 2018 · 2018
Earlier work this paper cites.
The TENNLab exploratory neuromorphic computing framework
James S Plank, Catherine D Schuman, Grant Bruer, Mark E Dean, and Garrett S Rose. 2018 · 2018
Earlier work this paper cites.
Non-traditional input encoding schemes for spiking neuromorphic systems. In 2019 International Joint Conference on Neural Networks (IJCNN) . IEEE, 1–10
Catherine D Schuman, James S Plank, Grant Bruer, and Jeremy Anantharaj. 2019 · 2019
Earlier work this paper cites.
A spiking and adapting tactile sensor for neuromorphic applications
Tom Birkoben, Henning Winterfeld, Simon Fichtner, Adrian Petraru, and Hermann Kohlstedt. 2020 · 2020
Earlier work this paper cites.
Event-Based Vision: A Survey
Guillermo Gallego, Tobi Delbrück, Garrick Orchard, Chiara Bartolozzi, Brian Taba, Andrea Censi, Stefan Leutenegger, Andrew J. Davison, Jörg Conradt, Kostas Daniilidis, and Davide Scaramuzza. 2022 · 2020
Earlier work this paper cites.
Modeling epidemic spread with spike-based models. In International Conference on Neuromorphic Systems 2020 . 1–5
Kathleen Hamilton, Prasanna Date, Bill Kay, and Catherine Schuman D. 2020 · 2020
Cited alongside, same era.
DEFFE: a data-efficient framework for performance characterization in domain-specific computing. In Proceedings of the 17th ACM International Conference on Computing Frontiers . 182–191
Frank Liu, Narasinga Rao Miniskar, Dwaipayan Chakraborty, and Jeffrey S Vetter. 2020 · 2020
Cited alongside, same era.
Caspian: A neuromorphic development platform. In Proceedings of the Neuro-inspired Computational Elements Workshop . 1–6
J Parker Mitchell, Catherine D Schuman, Robert M Patton, and Thomas E Potok. 2020 · 2020
Cited alongside, same era.
Evolutionary optimization for neuromorphic systems. In Proceedings of the Neuro-inspired Computational Elements Workshop . 1–9
Catherine D Schuman, J Parker Mitchell, Robert M Patton, Thomas E Potok, and James S Plank. 2020 · 2020
Cited alongside, same era.
Semi-Supervised Graph Structure Learning on Neuromorphic Computers. In Proceedings of the International Conference on Neuromorphic Systems 2022 . 1–4
Guojing Cong, Seung-Hwan Lim, Shruti Kulkarni, Prasanna Date, Thomas Potok, Shay Snyder, Maryam Parsa, and Catherine Schuman. 2022 · 2022
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Neuromorphic computing is Turing-complete. In Proceedings of the International Conference on Neuromorphic Systems 2022 . 1–10
Prasanna Date, Thomas Potok, Catherine Schuman, and Bill Kay. 2022 · 2022
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Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml
Elham E Khoda, Dylan Rankin, Rafael Teixeira de Lima, Philip Harris, Scott Hauck, Shih-Chieh Hsu, Michael Kagan, Vladimir Loncar, Chaitanya Paikara, Richa Rao, et al · 2022
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Neuromorphic Computing for Scientific Applications. In 2022 IEEE/ACM Redefining Scalability for Diversely Heterogeneous Architectures Workshop (RSDHA) . IEEE, 22–28
Robert Patton, Prasanna Date, Shruti Kulkarni, Chathika Gunaratne, Seung-Hwan Lim, Guojing Cong, Steven R Young, Mark Coletti, Thomas E Potok, and Catherine D Schuman. 2022 · 2022
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Prasanna Date, Bill Kay, Catherine Schuman, Robert Patton, and Thomas Potok. 2021 · 2021
Cited alongside, same era.
A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC
Giuseppe Di Guglielmo, Farah Fahim, Christian Herwig, Manuel Blanco Valentin, Javier Duarte, Cristian Gingu, Philip Harris, James Hirschauer, Martin Kwok, Vladimir Loncar, et al · 2021
Cited alongside, same era.
Neuromorphic computing for autonomous racing. In International Conference on Neuromorphic Systems 2021 . 1–5
Robert Patton, Catherine Schuman, Shruti Kulkarni, Maryam Parsa, J Parker Mitchell, N Quentin Haas, Christopher Stahl, Spencer Paulissen, Prasanna Date, Thomas Potok, et al · 2021
Cited alongside, same era.
Neuromorphic devices for bionic sensing and perception
Mingyue Zeng, Yongli He, Chenxi Zhang, and Qing Wan. 2021 · 2021
Cited alongside, same era.
A review of non-cognitive applications for neuromorphic computing
James Aimone, Prasanna Date, Gabriel Fonseca-Guerra, Kathleen Hamilton, Kyle Henke, Bill Kay, Garrett Kenyon, Shruti Kulkarni, Susan Mniszewski, Maryam Parsa, et al · 2022
Cited alongside, same era.
Evolutionary vs imitation learning for neuromorphic control at the edge
Catherine Schuman, Robert Patton, Shruti Kulkarni, Maryam Parsa, Christopher Stahl, N Quentin Haas, J Parker Mitchell, Shay Snyder, Amelie Nagle, Alexandra Shanafield, et al · 2022
Later among the works it cites.
Opportunities for neuromorphic computing algorithms and applications
Catherine D Schuman, Shruti R Kulkarni, Maryam Parsa, J Parker Mitchell, Bill Kay, et al · 2022
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Smart pixel dataset
Morris Swartz and Jennet Dickinson. 2022 · 2022
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Efficient spiking neural networks with radix encoding
Zhehui Wang, Xiaozhe Gu, Rick Siow Mong Goh, Joey Tianyi Zhou, and Tao Luo. 2022 · 2022
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Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics
Dominique J Kösters, Bryan A Kortman, Irem Boybat, Elena Ferro, Sagar Dolas, Roberto Ruiz de Austri, Johan Kwisthout, Hans Hilgenkamp, Theo Rasing, Heike Riel, et al · 2023
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